Google Professional Data Engineer Machine Learning and AI

I think that AD makes more sense. D is the explanation you gave. In the rest, A makes more sense, in any anomaly detection algorithm it is assumed a priori that you have much more "normal" samples than mutated ones, so that you can model normal patterns and detect patterns that are "off" that normal pattern. For that you will always need the no. of normal samples to be much bigger than the no. of mutated samples.

A instead of B:
"anomaly detection (also outlier detection[1]) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data

I think it's A & C, the anomaly detection algorithm train on "normal" data and anything that deviates from that is classified "mutated", and so there is no commun caracterstics or patterns for mutated data, this is why the statement C is correct

A. There are very few occurrences of mutations relative to normal samples. This is a strong characteristic supporting unsupervised anomaly detection. Anomaly detection methods often work well when the "normal" class is the majority and anomalies are rare outliers. The mutated samples, being few, would likely appear as anomalies relative to the cluster of normal samples.
D. You expect future mutations to have similar features to the mutated samples in the database. This characteristic supports unsupervised anomaly detection. If future mutations are expected to share similar characteristics with the existing mutated samples (even if they are rare), an anomaly detection method could potentially learn the pattern of the normal samples and flag anything significantly different (including the mutated samples) as an anomaly.

Unsupervised anomaly detection. Characteristics are few anomalies and future mutations different. So A and C.

AC. Answer C. Unsupervised anomaly detection methods are particularly useful when you don't have labeled examples of the anomalies you're trying to detect. Why not D, if future mutations are similar to existing ones, a supervised model trained on labeled examples of known mutations would likely be more accurate in classifying new samples.

The answer should be A and C.

Unsupervised anomaly detection is useful when labels are unavailable, or when anomalies are rare and distinct.

Hence A is definitely correct because anomaly detection excels when anomalies are rare compared to normal data.

I think C is correct because by adding new mutation data that is similar to the existing mutation data, the model will learn in a broader sense on what constitutes to 'mutation', and it leads to a better generalization. If the new data is too similar to the existing mutation data (answer D), the model might overfit to those specific examples. However, the new data should still share some fundamental characteristic to the existing data so that the model can recognize them as belonging to the same anomaly category.

I think it's A&C. For an anomaly detection model, the ratio of normal vs abnormal is expected to be high. 'C' because the model is expected to be adaptive meaning the model detects the abnormal features that can be different from the abnormal features currently being trained on.

The keyword is unsupervised anomaly detection. So A is correct. We think and should ensure the majority of data represents 'normal'. Unsupervised methods are good for detecting unknown patterns. Thus C could be correct.

AD: to use unsupervised anomaly detection the anomalies a) must be rare b) they must differ from the NORMAL. So...
A: mutated samples must be scarce compared to normal tissue.
D: yes, we expect the future mutated samples to have similar features to the mutated samples currently in the database.
Why not C? If I train my model with mutated samples with specific characteristics, I do not expect it to find different mutations. In the future, when new mutations appear, I would retrain my model including those new samples.

Anomaly detection has two basic assumptions:
*Anomalies only occur very rarely in the data.
*Their features differ from the normal instances significantly.
Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called “normal” instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection.
The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them.
https://www.science.gov/topicpages/u/unsupervised+anomaly+detection
A because “Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal”, B is for Supervised anomaly detection https://en.wikipedia.org/wiki/Anomaly_detection

A - anomaly detection is used for detecting rare events, meaning it is expected that there are much less of those than of normal ones.
D - you expect the future mutations to be similar to the mutations you already have, so that you can detect them (pattern recognition)

A makes sense

C and D compares future mutations to mutated samples in database

The question is pretty badly worded… If we were to run a full unsupervised anomaly detection over the entire dataset, C and D will be true, since some future mutations may be similar to current mutations and some will be significantly different to current mutations.

The question is suggesting "labelling" tissue samples using unsupervised anomaly detection, and subsequently using the labels with a supervised algorithm to classify future samples. If this interpretation of the question is correct, then D makes sense

The answer should be AD.

A, anomaly should have a little amount, if there are many samples then we should do classification instead, because unsupervised will give a lot of false positive.

D, the future anomaly should be of the same distribution as present anomaly! or else our anomaly detection will not be generalize to the future feature.

A. There are very few occurrences of mutations relative to normal samples. This characteristic is supportive of using an unsupervised anomaly detection method, as it is well suited for identifying rare events or anomalies in large amounts of data. By training the algorithm on the normal tissue samples in the database, it can then identify new tissue samples that have different features from the normal samples and classify them as mutated.

D. You expect future mutations to have similar features to the mutated samples in the database. This characteristic is supportive of using an unsupervised anomaly detection method, as it is well suited for identifying patterns or anomalies in the data. By training the algorithm on the mutated tissue samples in the database, it can then identify new tissue samples that have similar features and classify them as mutated.

D should be correct. You expect future samples will correlate with the training samples. That's the whole point of learning procedure. If you do not expect that they have similar features, then why would you use features in the training samples in the first place? A is also correct, since anomaly labels would be seen rarely.

A. There are very few occurrences of mutations relative to normal samples. This characteristic is supportive of using an unsupervised anomaly detection method, as it is well suited for identifying rare events or anomalies in large amounts of data. By training the algorithm on the normal tissue samples in the database, it can then identify new tissue samples that have different features from the normal samples and classify them as mutated.

D. You expect future mutations to have similar features to the mutated samples in the database. This characteristic is supportive of using an unsupervised anomaly detection method, as it is well suited for identifying patterns or anomalies in the data. By training the algorithm on the mutated tissue samples in the database, it can then identify new tissue samples that have similar features and classify them as mutated.

A is incorrect as you need to work with the data you have available
C is an optimisation not a solution
D is ethically incorrect and invasion to privacy, there could be several legal implications with this
B although oversimplified but is a workable solution

We have labelled data that contains whether a loan application is accepted or defaulted - So Classification Problem Data.

We need to predict (Default Rates for applicants) - I think whether application will be granted or defaulted. - So Binary Classification.

No option matches the answer. - if we mark 'B' - It should be Logistic Regression, Instead of Linear Regression

it's not B because it's not a problem of linear regression, it' s a classification !

A. Because the dataset is just of granted loans of the business, but is needed a grater database where to train the model in order to get aceptable results

"social" does not mean Social Media. This could be linked to demograhic data, si it could improve the score.

To predict default rates for credit applicants using the labeled dataset of granted loan applications, the most appropriate course of action would be:

B. Train a linear regression to predict a credit default risk score.

Here's the rationale for this approach:

Appropriate Model for Prediction: Linear regression is a common statistical method used for predictive modeling, particularly when the outcome variable (in this case, the likelihood of default) is continuous. In the context of credit scoring, linear regression can be used to predict a risk score that represents the probability of default.

Utilization of Labeled Data: Since you already have a labeled dataset containing information on loans that have been granted and whether they have defaulted, you can use this data to train the regression model. This historical data provides the model with examples of borrower characteristics and their corresponding default outcomes.

B. Train a linear regression to predict a credit default risk score.

What would be the target variable if B is correct i.e. training a linear regression model? Default/No-Default is a categorical variable one cannot train a linear regression model with this target variable

C - it is a typical approach in credit loans.
Keeping only the accepted loans leads to a bias in the application

Linear regression is not the good way to solve such a problem, but you can totally apply linear regression to solve a classification problem. Just set the labels to numeric values 0 and 1 and linear regression will try to predict a value inbetween and round to the closest label (0 or 1).

Totally not the way to go about it, but actually it's possible.

Cannot be B. This is logistic regression, not linear regression.

D is the only acceptable option.
Social profile can include things like high or low income, for example.
When you apply for a credit you usually have to give this information, so totally legal.

Because there is no option to know what dataset schema is even though B is needed for this question's purpose Nobody can't select B. So there is none to answer

all options are wrong: Still in favour of B
A: ofc its good to have more data but its not clear how much data we have
B: Linear can be a workable approach but current situation is not for linear approach, decision tree, random forest etc can be good for it.
C: DAta should be unbiased, removing bias is negative for tranining

default rates can be predicted with linear regression.

Answer should actually be a logistic Regression model

The question asks about default RATES, as in you are predicting a continuous variable, not a discrete one (classification). This is a regression problem, so choice B.

I used to be a Credit Risk modeler and I think this question is stupid.

Answer is C.
Bad performance of a model is either due to lack of relationship between dependent and independent variables used, or just overfit due to having used too many features and/or bad features.

A: Threading parallelisation can reduce training time, but if the selected featuers are the same then the resulting performance won't have changed
B: Serialization is only changing data into byte streams. This won't be useful.
C: This can show which features are bad. E.g. if it is one feature causing bad performance, then the dropout method will show it, so you can remove it from the model and retrain it.
D: This would become clear if the model did not fit the training data well. But the question says that the model fits the training data well, so D is not the answer.

C. Dropout Methods

Dropout is a regularization technique that can be used to prevent overfitting of the model to the training data. It works by randomly dropping out a certain percentage of neurons during training, which helps to reduce the complexity of the model and prevent it from memorizing the training data. This can improve the model's ability to generalize to new data and reduce the risk of poor performance when tested against new data.

The model fits the training data well but performs poorly on new data → this is overfitting, especially with a large/deep network.
Dropout is a regularization technique that randomly deactivates neurons during training, reduces co-adaptation, and improves generalization.
Threading only affects runtime, Serialization is for saving models, and Dimensionality Reduction isn’t the primary fix here since the model already fits the training set well.
Therefore: C. Dropout Methods.

Only option which fix overfitting

Answer is C.
For Neural Networks Dropout method of regularization thecnique, helps to prevent the overfit of the model, the main idea is remove/turn/off random % of neurons at training.

Droput method

Dropout method

Correct answer is C.
A - Is not Threading because it is used to accelerate the training in order to reduce training time.
B - Is not Serialization because it transforms (serializes into bytes) the training data but does not increase or change the original nature.
D - Is not dimensionality reduction because the model fits the training data.

Dropout methods is the solution here to resolve overfitting issue

Dropout is a specific technique to prevent overfitting by randomly disabling a certain percentage of neurons during training. This helps the network avoid relying too heavily on a subset of neurons, thereby improving its ability to generalize to new data.

Can we expect similar questions like this in GCP exam as well?

It occurs overfitting problem. A general idea is to simplify the model. A GENERALIZATION related method should be used.

C. Dropout Methods
Dropout is a regularization technique commonly used in neural networks to prevent overfitting. It helps improve the generalization of the model by randomly setting a fraction of the neurons to zero during each training iteration, which prevents the network from relying too heavily on specific neurons. This, in turn, can lead to better performance on new, unseen data.

A: Threading parallelisation can reduce training time, but if the selected featuers are the same then the resulting performance won't have changed
B: Serialization is only changing data into byte streams. This won't be useful.
C: This can show which features are bad. E.g. if it is one feature causing bad performance, then the dropout method will show it, so you can remove it from the model and retrain it.
D: This would become clear if the model did not fit the training data well. But the question says that the model fits the training data well.

So, C is the answer.

Dropout Methods are useful to prevent a TensorFlow model from overfitting

Answer is C. Dropout methods are used to mitigate overfitting. Hence, it is commonly used in training phase and it's beneficial for test-time performance.

Answer is C

Option [B] - looks to be correct

Answer is B.
Cloud Vision API detects lot of things for not damages. The description of Damages can be different for each business . So we need to train the model with test and training data to give our definition of Damages, so we need ML capabilities so answer is B, AutoML.

why not C - as per Gemini: The pre-trained Cloud Vision API provides general-purpose image analysis (e.g., object detection for common objects, landmark detection, text detection, safe search). It is not designed or trained to specifically detect "damage" on arbitrary packages. "Damage" is a highly specific and custom visual pattern that would require a custom model. The API would not inherently know what "damage" looks like on a package.
Fit: Incorrect tool for custom damage detection.

as per chat gpt One of the features of Cloud Vision API is damage detection, which can be used to identify and classify various types of damage in images, such as cracks, dents, scratches, stains, etc

For this scenario, where you need to automate the detection of damaged packages in real time while they are in transit, the most suitable solution among the provided options would be B.

Here's why this option is the most appropriate:

Real-Time Analysis: AutoML provides the capability to train a custom model specifically tailored to recognize patterns of damage in packages. This model can process images in real-time, which is essential in your scenario.

Integration with Existing Systems: By building an API around the AutoML model, you can seamlessly integrate this solution with your existing package tracking applications. This ensures that the system can flag damaged packages for human review efficiently.

Customization and Accuracy: Since the model is trained on your specific corpus of images, it can be more accurate in detecting damages relevant to your use case compared to pre-trained models.

I was leaning towards C but tested out uploading some damaged boxes to Vision API. It seems to have a lot of trouble detecting damaged boxes. It mislabeled boxes as a tire or toy. Also, there is no part of the API that seems to be able to detect damage. So I'll have to go with B. You should train a model to accomplish this then integrate with your app.

AutoML for Custom Models: AutoML (Auto Machine Learning) is designed to simplify the process of training custom machine learning models, including image classification models. It allows you to leverage Google Cloud's pre-built AutoML Vision service to train a model specifically for detecting package damage based on your corpus of images. This ensures accurate and customized results.
Real-time API Integration: After training the AutoML model, you can create an API endpoint that integrates seamlessly with your package tracking applications. This means that as packages move on the delivery lines, you can send images in real-time to the API for immediate analysis.
Scalability: AutoML Vision is built to scale, so it can handle the analysis of images in real-time, even as packages move continuously on the delivery lines.

Keywords: realtime, camera streaming

https://cloud.google.com/vision#:~:text=where%20you%20are-,Vertex%20AI%20Vision,-Vertex%C2%A0AI%20Vision

Option B AutoML would be too complex and not time efficient.

Using Vision AI(Vertex AI Vision) first + AutoML
Option D is better than B (just AutoML).

B
https://www.cloudskillsboost.google/focuses/22020?parent=catalog

Option B - AutoML

will stay with B. Might be more reliable, accurate.
as many says in duscission, Vision Api does not say it has defect detection.
i remember labs with Auto ML, where models were trained. Vertex AI labs.

B is right

B
C-is not answer
Vision API currently allows you to use the following features:
https://cloud.google.com/vision/docs/features-list#:~:text=Vision%20API%20currently%20allows%20you%20to%20use%20the%20following%20features%3A

It looks like B is the only valid option, with the assumption that you have a corpus of images (the question does not say that you do not).
It would not be Cloud Vision API because that does not do damage detection (https://cloud.google.com/vision/docs/features-list).

https://cloud.google.com/solutions/visual-inspection-ai#all-features

Here it is mentioned that the company is planning to implement a camera system. So it does not have the training data yet. Without having training data , the only option left is to use pre- trained models through cloud API . C Is the answer. B is wrong as you dont have data to train the model.

AutoML is used to train model and do damage detection Auto Vision is used is a pre trained model used to detect objects in images

The correct answer is C
TPU does not support custom C++ tensorflow ops
https://cloud.google.com/tpu/docs/tpus#when_to_use_tpus

D:
Cloud TPUs are not suited to the following workloads: [...] Neural network workloads that contain custom TensorFlow operations written in C++. Specifically, custom operations in the body of the main training loop are not suitable for TPUs.

C. Use Cloud GPUs after implementing GPU kernel support for your customs ops.
Here's why:

Your current bottleneck is in the custom C++ TensorFlow ops performing matrix multiplications, which are computationally intensive operations.
GPUs are specifically designed to efficiently handle matrix operations and would significantly speed up your training time compared to CPUs.
Since you've developed custom C++ TensorFlow ops, you'll need to implement GPU kernel support for these operations to take advantage of GPU acceleration.

According to the official documentation. Models that contain many custom TensorFlow operations written in C++ should keep using CPUs.

I think this is D. I recently did the ML professional exam and they ask that there, and it's always "c++ custom ops = CPU", it's in fact the only scenario for non-small models on CPU. It's written in black and white here: https://cloud.google.com/tpu/docs/intro-to-tpu#when_to_use_tpus, check out the CPU/GPU/TPU "when to use" section.

Why Not Other Options?
A. Use Cloud TPUs without any additional adjustment to your code:

TPUs are optimized for standard TensorFlow operations and require custom TensorFlow ops to be adapted to TPU-compatible kernels, which is not trivial.
Without modifications, your custom C++ ops will not run efficiently on TPUs.
B. Use Cloud TPUs after implementing GPU kernel support for your customs ops:

Implementing GPU kernel support alone is not sufficient for running on TPUs. TPUs require specific optimizations and adaptations beyond GPU kernels.
D. Stay on CPUs, and increase the size of the cluster you're training your model on:

While increasing the CPU cluster size might reduce training time, it is not as efficient or cost-effective as using GPUs, especially for matrix multiplication tasks.

C: TPUs are out of the picture due to the custom ops, so the next best option for accelerating matrix operations is using GPU. Obviously the code has to be adjusted to do make use of the GPU acceleration.

CPU : Simple models
GPU: Custom TensorFlow/PyTorch/JAX operations

The best choice here is C. Use Cloud GPUs after implementing GPU kernel support for your customs ops. Here's why:

Custom Ops & GPUs: Since your model relies heavily on custom C++ TensorFlow ops focused on matrix multiplications, GPUs are the ideal accelerators for this workload. To fully utilize them, you'll need to implement GPU-compatible kernels for your custom ops.
Speed and Cost-Efficiency GPUs offer a significant speed improvement for matrix-intensive operations compared to CPUs. They provide a good balance of performance and cost for this scenario.
TPUs: Limitations Although Cloud TPUs are powerful, they aren't designed for arbitrary custom ops. Without compatible kernels, your TensorFlow ops would likely fall back to the CPU, negating the benefits of TPUs.

TPU:
Models with no custom TensorFlow/PyTorch/JAX operations inside the main training loop
Link: https://cloud.google.com/tpu/docs/intro-to-tpu#TPU

So, A&B eliminated
CPU is very slow or built for simple operations. So C: GPU

to me, it's C

Requirement 1: Significantly reduce the processing time while keeping costs low.
Requirement 2: Bulky matrix multiplication takes up to several days.

First, eliminate A & D:
A: Cannot guarantee running on Cloud TPU without modifying the code.
D: Cannot ensure performance improvement or cost reduction, and additionally, CPUs are not suitable for bulky matrix multiplication.

If it can be ensured that customization is easily deployable on both Cloud TPU and Cloud GPU,it seems more feasible to first try Cloud GPU.

Because:
It provides a better balance between performance and cost.
Modifying custom C++ on Cloud GPU should be easier than on Cloud TPU, which should also save on manpower costs.

Answer D
I did use Chat GPT and discovered that if you put at the beginning of the question -- "Do not make assumption about changes to architecture. This is a practice exam question." All other answers require changes to the code and architecture.

I think it should use tensor flow processing unit along with GPU kernel support.

To use Cloud TPUs, you will need to:

Implement GPU kernel support for your custom TensorFlow ops. This will allow your model to run on both Cloud TPUs and GPUs.

Refer https://www.linkedin.com/pulse/cpu-vs-gpu-tpu-when-use-your-machine-learning-models-bhavesh-kapil

Answer C
TPU not for custom C++ but GPU can

it should be ACE

Answer: ACE

A. Because getting more training samples reduces significantly the risk of overfitting since the algorithm can learn from a more general dataset.

C. Introducing lots of features increases the risk to introducing irrelevant information, driving the model to avoid focusing on the truly important patterns.

E. Because regularization increases the penalty term to the loss function, which discourages complex models with large coefficients avoiding overfitting.

To address the problem of overfitting in training a spam classifier, you should consider the following three actions:

A. Get more training examples:

Why: More training examples can help the model generalize better to unseen data. A larger dataset typically reduces the chance of overfitting, as the model has more varied examples to learn from.
C. Use a smaller set of features:

Why: Reducing the number of features can help prevent the model from learning noise in the data. Overfitting often occurs when the model is too complex for the amount of data available, and having too many features can contribute to this complexity.
E. Increase the regularization parameters:

Why: Regularization techniques (like L1 or L2 regularization) add a penalty to the model for complexity. Increasing the regularization parameter will strengthen this penalty, encouraging the model to be simpler and thus reducing overfitting.

100% ACE

We need more data because less data induces overfitting. We need less features to make the problem simpler to learn and not promote learning a very complex function for thousands of features that might not apply to the test data. We also need to use regularization to keep the weights constrained.

Definitely ACE.
More training data and less variables can prevent the model from being too picky or specific.

? why A is answer? even though 'more training example' not 'more dataset example'. I understand that there is dataset same and there is only change the size of training examples size. in this case there are valid and test example should be reduced. isn't it?

Collect more training data: This will help the model generalize better and reduce overfitting.

Use regularization techniques: Techniques such as L1 and L2 regularization can be applied to the model's weights to prevent them from becoming too large and causing overfitting.

Use early stopping: This involves monitoring the performance of the model on a validation set during training, and stopping the training when the performance on the validation set starts to degrade. This helps to prevent the model from becoming too complex and overfitting the training data.

A -The training data is causing the overfiting for the testing data, so addition of training data will solve this.
C - Larger sets will cause overfitting, so we have to use smaller sets or reduce features
E - Increase the regularization is a method for solving the Overfitting model

Answers are;
A. Get more training examples
C. Use a smaller set of features
E. Increase the regularization parameters

Prevent overfitting: less variables, regularisation, early ending on the training

Reference:
https://cloud.google.com/bigquery-ml/docs/preventing-overfitting

Answer ADE

100% sure ACE

https://elitedatascience.com/overfitting-in-machine-learning

Answer is : ACE
https://www.ibm.com/cloud/learn/overfitting#:~:text=Overfitting%20is%20a%20concept%20in,unseen%20data%2C%20defeating%20its%20purpose.

im vote for ACE

It should be ACE

@medeis_jar

As MaxNRG wrote:
The tools to prevent overfitting: less variables, regularization, early ending on the training.

- Adding more training data will increase the complexity of the training set and help with the variance problem.
- Reducing the feature set will ameliorate the overfitting and help with the variance problem.
- Increasing the regularization parameter will reduce overfitting and help with the variance problem.

- Before you can train a model, you need to decide how to split your dataset.

Option C - you've just concluded processing data, ending up with clean and prepared data for the model. Now you need to decide how to split the data for testing and for training. Only afterwards, you can train the model, evaluate it, fine tune it and, eventually, predict with it

Anwser D is correct:
the next step in the machine learning lifecycle is to evaluate the model

Delineate test/train data should have been done BEFORE or during data processing

First ever time Exam Topic answers matching with users answer yoooo hoooooo.

C

Option C

Model doesn't seem to be trained yet

Correct answer is A. A tip here to decide when a liner regression should be used or logistics regression needs to be used. If you are forecasting that is the values in the column that you are predicting is numeric, it is always liner regression. If you are classifying, that is buy or no buy, yes or no, you will be using logistics regression.

linear regression to predict numeric with least cost. for classifying with options use logistics.

the keyword here is running it on a single resource-constrained virtual machine. linear regression is a simple and efficient algorithm that is well-suited for predicting continuous values.

Linear regression is a simple and resource-efficient algorithm for predicting continuous values like housing prices. It's computationally lightweight and well-suited for single machines with limited resources. It doesn't require the extensive computational power or specialized hardware that more complex algorithms like neural networks (options C and D) might need.

Option B (Logistic classification) is used for binary classification tasks, not for predicting continuous values like housing prices, so it's not the right choice in this context.

Linear regression is used for continous distribution.

Here, due to budget constraints, we're utilizing a single resource-constrained virtual machine, operating in a minimal resource environment. Linear regression emerges as the appropriate algorithm. It's a lightweight predictive model that suits our resource limitations

Linear regression will be used since the prediction requires forecasting prices involving numeric values and is computationally less resource intensive

Correct answer is A

Correct Answer is A. Since linear regression is used to predict a numeric value. While logistic regression is used to classify among the binary scenario.
Further option C and D are advance ML options and not cost and resource effective for the current situation.

predict housing prices = linear regresssion

must be A.
Though C can do it, linear regression is the better practice.

Must be A

A for sure. B is for classification. Neural nets can accomplish the task but they take WAY too many resources

correct answer -> Linear Regression

Linear regression is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables: One variable, denoted X, is regarded as the independent variable. The other variable denoted y is regarded as the dependent variable. Linear regression uses one independent variable X to explain or predict the outcome of the dependent variable y.

Whenever you are told to predict some future value of a process which is currently running, you can go with a regression algorithm.

A as Supervised learning using Regression can help build a model to predict house prices.
Option B is wrong as Classification would not help to solve the problem.
Options C & D are wrong as they would need more resources.

Ans: A

Ok the right answer is A, but the question is why? Then:
- B not because we are make forecasting and not classifying
- C and D not because this solution need more nodes, then more VM.
Right?

Correct Answer : A

Entity analysis -> Identify entities within documents receipts, invoices, and contracts and label them by types such as date, person, contact information, organization, location, events, products, and media.

Sentiment analysis -> Understand the overall opinion, feeling, or attitude sentiment expressed in a block of text.
-- Avoid Custom models

Call the Cloud Natural Language API from your application. Process the generated Entities Analysis as labels.

The Cloud Natural Language API is a pre-trained machine learning model that can be used for natural language processing tasks such as entity recognition, sentiment analysis, and syntax analysis. The API can be called from your application using a simple API call, and it can generate entities analysis that can be used as labels for the user's blog posts. This would be the quickest and easiest option for your team since it would not require any machine learning expertise or additional developer resources to build and train a model. Additionally, it will give you accurate and up-to-date results as the API is constantly updated by Google.

For the first time, the answer in exam topics matches community vote :-).

Of course the answer is A. Since the problem already states that you don't have time, resources or expertise. So the best solution in the case is to utilize the available API. Also since we need to extract the labels and not the sentiment of the text, we'll go for option A and not B

A. Call the Cloud Natural Language API from your application. Process the generated Entities Analysis as labels.

The Cloud Natural Language API is a pre-trained machine learning model that can be used for natural language processing tasks such as entity recognition, sentiment analysis, and syntax analysis. The API can be called from your application using a simple API call, and it can generate entities analysis that can be used as labels for the user's blog posts. This would be the quickest and easiest option for your team since it would not require any machine learning expertise or additional developer resources to build and train a model. Additionally, it will give you accurate and up-to-date results as the API is constantly updated by Google.

Answer is A
Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.

Entity analysis -> Identify entities within documents receipts, invoices, and contracts and label them by types such as date, person, contact information, organization, location, events, products, and media.

Sentiment analysis -> Understand the overall opinion, feeling, or attitude sentiment expressed in a block of text.

Apparently, there is unanimity on answer [A]
What if there was another available answer in an actual exam?

E. Call the Cloud Natural Language API from your application. Process the generated Content Classification as labels

What would you choose, A or E?

My opinion is that Content Classification is more suitable for detecting subject.

A is the right one . Doc says:
Entity analysis inspects the given text for known entities (Proper nouns such as public figures, landmarks, and so on. Common nouns such as restaurant, stadium, and so on.) and returns information about those entities. Entity analysis is performed with the analyzeEntities method.

Vote for A

a is correct

A.
CD don't work as it requires Machine Learning experience.
B - Sentiment Analysis is to analyze attitude, opinion, etc. So A.

Vote for A

A is correct

should be C, since we need to recognize both voice and intent

Option A - Cloud Speech-to-Text API.
The question is just asking to " interpret customer voice commands" .. it does not mention anything related to sentiment analysis so NLP is not required. DialogFlow is more of a chat bot services typically suited for a "Service Desk" kind of setup - where clients will call a centralized helpdesk and automation is achieved through Chat bot services like - google Dialog flow

While both are Google Cloud services related to speech processing, the key difference is that Cloud Speech-to-Text API is solely focused on transcribing spoken language into text, while DialogFlow is a more comprehensive platform that not only transcribes speech but also interprets the meaning of the conversation, allowing you to build conversational AI applications with features like intent recognition and entity extraction

This is a queston from Actual exam question from Google's Professional Data Engineer so C makes sense. This is not an AWS question

The question clearly states "voice commands" which is a term for short (few words long at most) well-defined phrases to be recognized. No need for a dialog.
Even if I were to use Dialogflow, I would use ES instead of CX (new name for Enterprise Edition), no fancy features are required for this.

Ans C . main thing is that question is saying" customer voice commands " there is no need to sentimental analysis of language so thats why.

C. Dialogflow Enterprise Edition

Dialogflow is a powerful natural language understanding platform developed by Google. It allows you to build conversational interfaces, interpret user voice commands, and integrate with various platforms and devices like Google Home. The "Enterprise Edition" provides additional features and support for more complex use cases, making it a good choice for a retailer looking to integrate with in-home assistants and handle customer voice commands effectively.

Answer is C. However Google Assistant Conversational Actions will be sunsetted on June 13, 2023.

https://cloud.google.com/dialogflow/es/docs/integrations/aog

I think the answer is A: Speech to Text.
You want to interpret what a user say... Diagflow is text to speech, not what the question asked for...

Thoughts?

The question is just asking to " interpret customer voice commands" so A is out of the box solution

Enable voice control

Implement voice commands such as “turn the volume up,” and voice search such as saying “what is the temperature in Paris?” Combine this with the Text-to-Speech API to deliver voice-enabled experiences in IoT (Internet of Things) applications.
https://cloud.google.com/speech-to-text#section-9

https://cloud.google.com/blog/products/gcp/introducing-dialogflow-enterprise-edition-a-new-way-to-build-voice-and-text-conversational-apps

Dialogflow is the answer

Dialogflow provides a seamless integration with Google Assistant. This integration has the following advantages: You can use the same Dialogflow agent to power Google Assistant and other integrations. Dialogflow agents provide Google Cloud enterprise-grade security, privacy, support, and SLAs

dialog

speech

It should be D. INTERPRET customer voice commands and issue an order to the backend systems. Option C is usually applied for conversation. But in this case, it is not a conversation.

recognize voice and intent
https://cloud.google.com/blog/products/gcp/introducing-dialogflow-enterprise-edition-a-new-way-to-build-voice-and-text-conversational-apps

B - You only need a PoC and it has be done quickly

A - https://cloud.google.com/vertex-ai/docs/beginner/beginners-guide Target at least 1000 examples per target

The key difference between Google Cloud Vision AutoML and Cloud Vision API is that Cloud Vision API provides pre-trained models for basic image analysis tasks like object detection and labeling, while Cloud Vision AutoML allows you to train custom machine learning models to identify specific objects or concepts within images that are unique to your dataset, requiring you to provide labeled training data.

AutoML Vision is deprecated since march 31, 2024. The question will refer to Vertex AI AutoML. And as bet practice, the minimum dataset size for each label is 1000. So, with an updated question, the answer would be A.

A. Use Cloud Vision AutoML with the existing dataset.

Here's why this is the most suitable option:

Speed and Ease: AutoML simplifies model building. You simply upload your labeled images, and AutoML takes care of model selection, training, and evaluation.
Existing Dataset Sufficiency: Your dataset (750 components x 1000 images each) is a decent starting point for AutoML, allowing you to quickly test its effectiveness.
Minimal Custom Development: AutoML's out-of-the-box deployment options let you integrate the model into your app without extensive coding.

Option B is the fastest way to train a model that can be used to recognize the 750 different components.

Whats wrong with C, its fast, cheap and add your 750 labels which is not big work.
AutoML is good to train on big dataset and costly as compared to APIs

First I think in Vision API, but that is a pre-trained AI, will not recognize my labels, so because you have 1000 samples per item, AUTO ML is perfect. B cannot be because have not sensed to reduce your dataset if you have the recommended number of info.
https://cloud.google.com/vision/automl/docs/beginners-guide#include_enough_labeled_examples_in_each_category
The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label.

A - https://cloud.google.com/vision/automl/docs/beginners-guide#include_enough_labeled_examples_in_each_category

Based on this:
"As a rule of thumb, we recommend to have at least 100 training samples per class if you have distinctive and few classes, and more than 200 training samples if the classes are more nuanced and you have more than 50 different classes"

750 different components = more than 50 different classes. That means we need more than 200 training samples. If we used 250 training samples out of the 1000 samples and multiply it to 750 different classes we get a total of 187,500 which is the equivalent of reducing the dataset twice.

https://cloud.google.com/vision/automl/object-detection/docs/prepare#how_big_does_the_dataset_need_to_be

I choose A because on the vertex AI documentation (https://cloud.google.com/vertex-ai/docs/image-data/classification/prepare-data), on the best practices of preparing data for image recognition recommend this: We recommend about 1000 training images per label. The minimum per label is 10. In general, it takes more examples per label to train models with multiple labels per image, and resulting scores are harder to interpret.

I know that is PoC, but if you do it without enough accuracy, you maybe discard the solution because it isn't fit for your requirements. So is better to do it with enough data to be sure that the model is or not accuracy enough with this data, because you maybe haven't enough accuracy and the problem is the quality of the data and not the amount of it.

https://cloud.google.com/vision/automl/docs/beginners-guide#include_enough_labeled_examples_in_each_category

The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label.

750*1000 are a lot.

It is labeled, so A is correct

It's A.
https://cloud.google.com/vision/automl/docs/beginners-guide#data_preparation

The bare minimum required by AutoML Vision training is 100 image examples per category/label. The likelihood of successfully recognizing a label goes up with the number of high quality examples for each; in general, the more labeled data you can bring to the training process, the better your model will be. Target at least 1000 examples per label.

Option A & B are quite close. Refer: https://cloud.google.com/vision/automl/docs/beginners-guide#data_preparation – Says to target at least 1000 images per label for training.

B
cant choose A because model needs to pass through the dataset several times for a proof of concept, existing data set samples might not be all seen in several working days causing over generalization

Ans: B
A: correlated to output means that feature can contribute a lot to the model. so not a good idea.
C: you need to run with almost same number, but you will iterate twice, once for averaging and second time to feed the averaged value.
D: removing features even if it 50% nulls is not good idea, unless you prove that it is not at all correlated to output. But this is nowhere so can remove.

Trying to find a reason why it is B and not D, found this and it seems the answer is D.
https://cloud.google.com/architecture/data-preprocessing-for-ml-with-tf-transform-pt1
Feature selection. Selecting a subset of the input features for training the model, and ignoring the irrelevant or redundant ones, using filter or wrapper methods. This can also involve simply dropping features if the features are missing a large number of values.

I think the best option is B.
D could be an option but what if the feature is very correlated to the result ?

B: combine the dependent features. It is more like PCA (principal component analysis).
D: could be the answer, but what if that feature is very important, or how often do you get a feature with more than 50% NULL values?

I am not into ML, to be honest, so I will rely on community opinion and choose B

B. Combine highly co-dependent features into one representative feature.

Combining highly correlated features into a single representative feature can reduce the dimensionality of your dataset, making the training process faster while preserving relevant information. This approach often helps eliminate redundancy in the input data.

Option A (eliminating features that are highly correlated to the output labels) can be counterproductive, as you want to maintain features that are informative for your prediction task. Removing features that are correlated with the output may reduce model accuracy.

Option C (averaging feature values in batches of 3) is not a common technique for reducing dimensionality, and it could lead to loss of important information.

Option D (removing features with null values for more than 50% of training records) can help reduce the dimensionality and may be useful if you have a large number of features with missing data, but it may not necessarily address co-dependency among features.

B. Combine highly co-dependent features into one representative feature.
This is the best choice.

"D" is wrong, and very dangerous. For instance, it might represent modern measurements only installed in <50% of weather stations, but very very precise and valuable.

Nulls are not a problem for models, out-of-the-box or with transformations models can handle nulls just fine.

wrong question. there are two answers B, D

Answer is Combine highly co-dependent features into one representative feature.

A: correlated to output means that feature can contribute a lot to the model. so not a good idea.
C: you need to run with almost same number, but you will iterate twice, once for averaging and second time to feed the averaged value.
D: removing features even if it 50% nulls is not good idea, unless you prove that it is not at all correlated to output. But this is nowhere so can remove.

I have a doubt, instead of combining highly corelated features why cant we remove corelated features which may give much more simplified dataset?

Answer: B

Data that is co-dependent is high corelated is some kind of reduldant information in some cases. If the features x1, x2 and x3 are x2 = x1 + 1 and x3 = 2*x1, for example, x2 and x3 are reduldant because can be explained with x1 feature, so can be excluded of the the model. Other option is to group this features. There is a lot of ways to resolve, but the main ideia is to use data engineer in co-depedent features to reduce the number of features in the model

This method is called Data Engineering, that you combine two or more values to get a custom info, this will avoid that the model read an extra column on the training and probably increase it's accuracy.

Answer: B

Co-dependent -> correlated -> correlated info = already present info in other variable.

B
null values can have many meanings and need different approach to handle, otherwise it causes inaccurate model, so not D

For me the best option is B

This is a case of underfitting - not overfitting (for over fitting the model will have extremely low training error but a high testing error) - so we need to make the model more complex - answer is D

small RMS Error means--overfitting--fits well--so make it complex by dropping features.
big RMS Error means--underfitting--not good fit--so increase complexity by adding layers/features. Answer D.

Could be B (data requirement for task is vague), but let's assume 100 million data points is enough and rule that out.

Indication of overfitting is significantly better performance on training data compared to unseen data. Here we are told that the unseen data is performing significantly better which is the opposite of what we should see if it were overfitting. Rule out C.

Symptoms of model underfitting is poor performance in BOTH training AND unseen data. While underfitting might be the issue, the more pressing concern is that the test set is clearly not representative of the overall data and could be skewed. This is further supported by the 90/10 split (academic/industry standard is 80/20 or 75/25 based on the Pareto principle: https://en.wikipedia.org/wiki/Pareto_principle). A 90/10 split would be useful if we were doing k-fold cross validation (https://machinelearningmastery.com/k-fold-cross-validation/), however there is no indication of such in the prompt.

Note: The question does not explicitly say that the model is performing poorly/errors are significantly bad, just that the error is twice as high in the training set (they could both have low error values).
So whilst it could be a case of underfitting (D), the first step taken should be addressing the obviously problematic data representation by adjusting the train-test split (option A).

It is underfitting problem, which means that the used models is too easy.

This is A. The key is that 90/10 is a weirdly small test set, that stood out to me straight away (I work professionally as a machine learning engineer and have the cert). Next tip, that everyone seems to be ignoring - this is not underfit OR overfit. The model outperforms on the TEST set, this is not a miswording. Test scores higher than train. The time you might expect to see this is if your test set is too small to be a representative sample, leading to unrepresentative results. Seeing as the question already set up this conclusion with the 90/10 thing, it's definitely A. None of the others (or indeed anything else) can address Test outperforming Train, and the conclusion of others below that this is due to a poorly worded question is a bizarre conclusion.

Underfitting scenario

It is an underfitting situation - D

Should be C
C. Try out regularization techniques (e.g., dropout or batch normalization) to avoid overfitting:

This is a reasonable approach. Regularization techniques can help prevent overfitting, especially when the model shows a significantly higher error on the training set compared to the test set.
D. Increase the complexity of your model (e.g., introducing an additional layer or increasing the size of vocabularies or n-grams):

This could potentially exacerbate the overfitting issue. Increasing model complexity without addressing overfitting concerns may lead to poor generalization on new data.

Are the questions in this relevant for the new exam or are these all now outdated?

https://stats.stackexchange.com/questions/497050/how-big-a-difference-for-test-train-rmse-is-considered-as-overfit#:~:text=RMSE%20of%20test%20%3C%20RMSE%20of,is%20always%20overfit%20or%20underfit.
RMSE of test > RMSE of train => OVER FITTING of the data.
RMSE of test < RMSE of train => UNDER FITTING of the data.
so for answer is D

Underfitting models: In general High Train RMSE, High Test RMSE.
Overfitting models: In general Low Train RMSE, High Test RMSE.

https://daviddalpiaz.github.io/r4sl/regression-for-statistical-learning.html

I passed the exam today. I am pretty sure it is overfitting. Answer must be c

RMSE is more on training. That means, model is not performing well on training dataset but performing well on testing dataset. This happens in the case of underfitting. So D.

chatGPT says option C

"root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set." means the RMSE of training set is two time of RMSE of test set, which indicates the training is not as good as test, then underfiting, so D.

Based on the given information, this scenario indicates a case of overfitting.

Overfitting occurs when a machine learning model performs well on the training data but poorly on unseen data (test data). In this case, the root-mean-squared error (RMSE) of the model is twice as high on the train set (the data used for training) compared to the test set (the data used for evaluation). This suggests that the model is fitting the training data too closely and is not generalizing well to new, unseen data.

So with dropout method we can overcome the overfitting so C is correct

underfitting

Cost effective - among the choices, it is cheaper to have a temporary accelerator instead of increasing our VM cost for an indefinite amount of time
D -> TPU accelerator cannot support custom operations

The best way to reduce the TensorFlow training time in a cost-effective manner is to use a VM with a GPU hardware accelerator. TensorFlow can take advantage of GPUs to significantly speed up training time for many models.

Specifically, option C is the best choice.

Changing the VM to another standard type like n2-highmem-32 or e2-standard-32 (options A and B) may provide some improvement, but likely not a significant speedup.

Using a TPU (option D) could speed up training, but TPUs are more costly than GPUs. For a cost-effective solution, GPU acceleration provides the best performance per dollar.

Since the model must run partially on CPUs, a VM instance with GPUs added will allow TensorFlow to offload appropriate operations to the GPUs while keeping CPU-specific operations on the CPU. This can provide a significant reduction in training time for many common TensorFlow models while keeping costs reasonable

key pjrse is "run partially on a CPU" from https://cloud.google.com/tpu/docs/intro-to-tpu#when_to_use_tpus refers to GPU

https://cloud.google.com/tpu/docs/intro-to-tpu#when_to_use_tpus

C. Train the model using a VM with a GPU hardware accelerator.

C
https://cloud.google.com/tpu/docs/tpus#when_to_use_tpus:~:text=Models%20with%20a%20significant%20number%20of%20custom%20TensorFlow%20operations%20that%20must%20run%20at%20least%20partially%20on%20CPUs

I agree with C, for choosing a GPU one of the cases says:
"Models with a significant number of custom TensorFlow operations that must run at least partially on CPUs"
https://cloud.google.com/tpu/docs/tpus#when_to_use_tpus

For fitting a linear classifier when the data is in a circle use A.

I think it's A as explai ned here https://medium.com/@sachinkun21/using-a-linear-model-to-deal-with-nonlinear-dataset-c6ed0f7f3f51

I think A should be x^2+y^2. We need a circle to classify the data.

Just a note, they are using X2 and Y2 to mean Xsquared, and Ysquared. This is a circle in the form X2+Y2 = k, so for a given k will split that dataset nicely.

It's not obvious to me it is A.

As others said, cos(X) does ignore the Y value. But answer A does not seem good either. The differences seem minimal.

If you do A then you have the following issues. If you take elements in the bottom right or the top left of the circle, they will all have the same value, ZERO. Not only that, they will actually have the same value with the elements in the middle of the circle which are completely black. Moreover, elements on the extreme right and extreme right will have different values (-x_max and +x_max).

However, if you use a cos(x) then the elements in the beginning

A. X2+Y2

The synthetic feature that should be added in this case is the squared value of the distance from the origin (0,0). This is equivalent to X2+Y2. By adding this feature, the classifier will be able to make more accurate predictions by taking into account the distance of each data point from the origin.

X2 and Y2 alone will not give enough information to classify the data because they do not take into account the relationship between X and Y.

D. cos(X) is not a suitable option because it does not take into account the Y coordinate.

A is the correct answer as graph of circle is x^2 + y^2

Answer is A:
The answer reflects 'x' to the 2nd power + 'y' the 2nd power.
I guess they can't use carots in the exam answers!

A is right
Reference:
https://medium.com/@sachinkun21/using-a-linear-model-to-deal-with-nonlinear-dataset-c6ed0f7f3f51

https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture

linear circle X2+Y2 https://www.stat.cmu.edu/~cshalizi/dm/20/lectures/08/lecture-08.html

If the shape was a circle, it would be (x^2 + y ^2). But I think that a quadric curve will do a better job of separating the two classes, so it would be (x^2)

Answer: A.
X^2+Y^2 is the equation of a circle.

C'est A

only A is draw a circle

Equation of circle as represented in the question

F(x) as A B C will have always a positive values as result, for A will need a third dimenssion Z to represent data, only D:cos(x) can be presented as the shown classification. this is a math question

Answer: B. Subsample your training dataset.

Subsampling your training dataset can help increase the training speed of your neural network model. By reducing the size of your training dataset, you can speed up the process of updating the weights in your neural network. This can help you quickly test and iterate your model to improve its accuracy.

Subsampling your test dataset, on the other hand, can lead to inaccurate evaluation of your model's performance and may result in overfitting. It is important to evaluate your model's performance on a representative test dataset to ensure that it can generalize to new data.

Increasing the number of input features or layers in your neural network can also improve its performance, but this may not necessarily increase the training speed. In fact, adding more layers or features can increase the complexity of your model and make it take longer to train. It is important to balance the model's complexity with its performance and training time.

B is correct

B should be correct. Increasing the layers can also decrease the training time but may introduce vanishing gradient hence D may not be correct

answer is B

Reduce training time and probably accuracy too.

all other are wrong

of course !

B. Subsampling your training dataset can decrease the amount of data the model needs to process and can speed up training time. However, it can lead to decrease in the model's accuracy.

Although it shouldn't matter since we are not even in testing phase yet and we aren't looking for accuracy.

B is the answer as we are bothered about speed not the accuracy.

The answer is B. Building a more complex model by increasing the number of layer will not reduce the training time.

By SubSampling the training data, you will reduce the training time.

In case of D, if you increase the number of layers, then the model's accuracy will be increased. But it will not reduce the time required to train the model.

Increase speed of the help to train quicker.. option B is sub sample that also help but it drop accurately of model . So I think Option D is good to go.

It is B. D would improve the accuracy, not speed.

It's B. D Would be for increase performance

if you Increase the number of layers, you increase the training time, right?

Both B and D seems correct.