Google Professional Machine Learning Engineer Exam Questions

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Question 1

You need to build an ML model for a social media application to predict whether a user's submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

AUse AutoML to optimize the model's recall in order to minimize false negatives.

BUse AutoML to optimize the model's F1 score in order to balance the accuracy of false positives and false negatives.

CUse Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.

DUse Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.

Answer : A

Recall is the ratio of true positives to the sum of true positives and false negatives. It measures how well the model can identify all the relevant cases. In this scenario, the relevant cases are the pictures that do not meet the profile photo requirements. Therefore, minimizing false negatives means minimizing the cases where the model incorrectly predicts that a non-compliant picture meets the requirements. By using AutoML to optimize the model's recall, the model will be more likely to reject a non-compliant picture and inform the user accordingly.Reference:

[AutoML Vision] is a service that allows you to train custom ML models for image classification and object detection tasks. You can use AutoML to optimize your model for different metrics, such as recall, precision, or F1 score.

[Recall] is one of the evaluation metrics for ML models. It is defined as TP / (TP + FN), where TP is the number of true positives and FN is the number of false negatives. Recall measures how well the model can identify all the relevant cases. A high recall means that the model has a low rate of false negatives.

Question 2

You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer's loan request has been rejected by your model, and the bank's risks department is asking you to provide the reasons that contributed to the model's decision. What should you do?

AUse local feature importance from the predictions.

BUse the correlation with target values in the data summary page.

CUse the feature importance percentages in the model evaluation page.

DVary features independently to identify the threshold per feature that changes the classification.

Answer : A

Option A is correct because using local feature importance from the predictions is the best way to provide the reasons that contributed to the model's decision for a specific customer's loan request.Local feature importance is a measure of how much each feature affects the prediction for a given instance, relative to the average prediction for the dataset1.AutoML Tables provides local feature importance values for each prediction, which can be accessed using the Vertex AI SDK for Python or the Cloud Console2. By using local feature importance, you can explain why the model rejected the loan request based on the customer's data.

Option B is incorrect because using the correlation with target values in the data summary page is not a good way to provide the reasons that contributed to the model's decision for a specific customer's loan request.The correlation with target values is a measure of how much each feature is linearly related to the target variable for the entire dataset, not for a single instance3.The data summary page in AutoML Tables shows the correlation with target values for each feature, as well as other statistics such as mean, standard deviation, and histogram4. However, these statistics are not useful for explaining the model's decision for a specific customer, as they do not account for the interactions between features or the non-linearity of the model.

Option C is incorrect because using the feature importance percentages in the model evaluation page is not a good way to provide the reasons that contributed to the model's decision for a specific customer's loan request.The feature importance percentages are a measure of how much each feature affects the overall accuracy of the model for the entire dataset, not for a single instance5. The model evaluation page in AutoML Tables shows the feature importance percentages for each feature, as well as other metrics such as precision, recall, and confusion matrix. However, these metrics are not useful for explaining the model's decision for a specific customer, as they do not reflect the individual contribution of each feature for a given prediction.

Option D is incorrect because varying features independently to identify the threshold per feature that changes the classification is not a feasible way to provide the reasons that contributed to the model's decision for a specific customer's loan request. This method involves changing the value of one feature at a time, while keeping the other features constant, and observing how the prediction changes. However, this method is not practical, as it requires making multiple prediction requests, and may not capture the interactions between features or the non-linearity of the model.

Local feature importance

Getting local feature importance values

Correlation with target values

Data summary page

Feature importance percentages

[Model evaluation page]

[Varying features independently]

Question 3

You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

AOption A

BOption B

COption C

DOption D

Answer : D

To log the metrics of a machine learning model in TensorFlow using the Vertex AI Python SDK, you should utilize theaiplatform.log_metricsfunction to log the F1 score andaiplatform.log_classification_metricsfunction to log the confusion matrix. These functions allow users to manually record and store evaluation metrics for each model, facilitating an efficient comparison based on specific performance indicators like F1 scores and confusion matrices.Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI and TensorFlow.

Vertex AI Python SDK reference | Google Cloud

Logging custom metrics | Vertex AI

Migrating from scikit-learn to TensorFlow | TensorFlow

Question 4

Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API. You want to use the most efficient approach. What should you do?

A1 Upload the audio files to Cloud Storage
2. Call the speech: Iongrunningrecognize API endpoint to generate transcriptions
3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

B1 Upload the audio files to Cloud Storage
2 Call the speech: Iongrunningrecognize API endpoint to generate transcriptions.
3 Create a Cloud Function that calls the Natural Language API by using the analyzesentiment method

C1 Iterate over your local Tiles in Python
2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object and set the content to the audio file data
3. Call the speech: recognize API endpoint to generate transcriptions
4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

D1 Iterate over your local files in Python
2 Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object, and set the content to the audio file data
3. Call the speech: lengrunningrecognize API endpoint to generate transcriptions
4 Call the Natural Language API by using the analyzesenriment method

Answer : B

According to the official exam guide1, one of the skills assessed in the exam is to ''design, build, and productionalize ML models to solve business challenges using Google Cloud technologies''.The Speech-to-Text API2allows you to convert audio to text by applying powerful neural network models.The Natural Language API3enables you to analyze text and extract information about the sentiment, entities, and syntax.The Cloud Functions4service lets you write and deploy code that runs in response to events, such as a Pub/Sub message or an HTTP request. Therefore, option B is the most efficient approach to analyze the audio files for customer sentiment, as it leverages the existing Google Cloud services and avoids unnecessary data processing and model training. The other options are not relevant or optimal for this scenario.Reference:

Professional ML Engineer Exam Guide

Speech-to-Text API

Natural Language API

Cloud Functions

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Question 5

You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?

APoor data quality

BLack of model retraining

CToo few layers in the model for capturing information

DIncorrect data split ratio during model training, evaluation, validation, and test

Answer : B

Model retraining is the process of updating an existing machine learning model with new data and parameters to improve its performance and accuracy. Model retraining is essential for maintaining the relevance and validity of the model, especially when the data or the environment changes over time.Model retraining can help to avoid or reduce the effects of model degradation, which is the phenomenon of the model's predictive performance decreasing as it is tested on new datasets within rapidly evolving environments1.

For the use case of predicting sales numbers, model accuracy is crucial, because the production model is required to keep up with market changes. Market changes can affect the demand, supply, price, and preference of the products, and thus influence the sales numbers. If the model is not retrained with new data that reflects the market changes, it may become outdated and inaccurate, and fail to capture the patterns and trends of the sales numbers. Therefore, the most likely issue that is causing the steady decline in model accuracy is the lack of model retraining.

The other options are not as likely as option B, because they are not directly related to the model's ability to adapt to market changes. Option A, poor data quality, may affect the model's accuracy, but it is not a specific cause of model degradation over time. Option C, too few layers in the model for capturing information, may affect the model's complexity and expressiveness, but it is not a specific cause of model degradation over time. Option D, incorrect data split ratio during model training, evaluation, validation, and test, may affect the model's generalization and validation, but it is not a specific cause of model degradation over time. Therefore, option B, lack of model retraining, is the best answer for this question.

Beware Steep Decline: Understanding Model Degradation In Machine Learning Models

Question 6

You developed a custom model by using Vertex Al to predict your application's user churn rate You are using Vertex Al Model Monitoring for skew detection The training data stored in BigQuery contains two sets of features - demographic and behavioral You later discover that two separate models trained on each set perform better than the original model

You need to configure a new model mentioning pipeline that splits traffic among the two models You want to use the same prediction-sampling-rate and monitoring-frequency for each model You also want to minimize management effort What should you do?

AKeep the training dataset as is Deploy the models to two separate endpoints and submit two Vertex Al Model Monitoring jobs with appropriately selected feature-thresholds parameters

BKeep the training dataset as is Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and feature selections

CSeparate the training dataset into two tables based on demographic and behavioral features Deploy the models to two separate endpoints, and submit two Vertex Al Model Monitoring jobs

DSeparate the training dataset into two tables based on demographic and behavioral features. Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets

Answer : D

Option A is incorrect because it does not separate the training dataset into two tables based on the features, which is necessary to train the two models separately and accurately.

Option B is incorrect because it does not separate the training dataset into two tables based on the features, and because it uses the same monitoring-config-from parameter for both models, which would not account for the different feature selections.

Option C is incorrect because it deploys the models to two separate endpoints, which would increase the management effort and complexity of the pipeline.

Option D is correct because it separates the training dataset into two tables based on the features, which would enable the two models to be trained separately and accurately. It also deploys both models to the same endpoint, which would simplify the pipeline and reduce the management effort. It also submits a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets, which would enable the skew detection to work properly for each model.

Question 7

You trained a model on data stored in a Cloud Storage bucket. The model needs to be retrained frequently in Vertex AI Training using the latest data in the bucket. Data preprocessing is required prior to retraining. You want to build a simple and efficient near-real-time ML pipeline in Vertex AI that will preprocess the data when new data arrives in the bucket. What should you do?

ACreate a pipeline using the Vertex AI SDK. Schedule the pipeline with Cloud Scheduler to preprocess the new data in the bucket. Store the processed features in Vertex AI Feature Store.

BCreate a Cloud Run function that is triggered when new data arrives in the bucket. The function initiates a Vertex AI Pipeline to preprocess the new data and store the processed features in Vertex AI Feature Store.

CBuild a Dataflow pipeline to preprocess the new data in the bucket and store the processed features in BigQuery. Configure a cron job to trigger the pipeline execution.

DUse the Vertex AI SDK to preprocess the new data in the bucket prior to each model retraining. Store the processed features in BigQuery.

Answer : B

Cloud Run can be triggered on new data arrivals, which makes it ideal for near-real-time processing. The function then initiates the Vertex AI Pipeline for preprocessing and storing features in Vertex AI Feature Store, aligning with the retraining needs. Cloud Scheduler (Option A) is suitable for scheduled jobs, not event-driven triggers. Dataflow (Option C) is better suited for batch processing or ETL rather than ML preprocessing pipelines.