Free Practice Questions for Google Professional-Machine-Learning-Engineer Exam

Option A is incorrect because training a time-series model to predict the machines’ performance values, and configuring an alert if a machine’s actual performance values significantly differ from the predicted performance values, is not the best way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option assumes that the performance values follow a predictable pattern, which may not be the case for complex systems. Moreover, this option does not use any historical incident data, which may contain useful information for identifying failures. Furthermore, this option does not involve any model evaluation or validation, which are essential steps for ensuring the quality and reliability of the model.

Option B is correct because implementing a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data, and training a model to predict anomalies based on this labeled dataset, is a reasonable way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option uses a simple and fast method to label the historical performance data, which is necessary for supervised learning.  A z-score is a measure of how many standard deviations a value is away from the mean of a distribution 1 . By using a z-score, we can label the performance values that are unusually high or low as anomalies, which may indicate failures. Then, we can train a model to learn the patterns of normal and anomalous performance values, and use it to predict anomalies on new data. We can also evaluate and validate the model using metrics such as precision, recall, or F1-score, and compare it with other models or methods.

Option C is incorrect because developing a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data, and testing this heuristic in a production environment, is not a safe way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option does not involve any model training or evaluation, which are essential steps for ensuring the quality and reliability of the solution. Moreover, this option does not test the heuristic on a separate dataset, such as a validation or test set, before deploying it to production, which may lead to errors or failures in the production environment.

Option D is incorrect because hiring a team of qualified analysts to review and label the machines’ historical performance data, and training a model based on this manually labeled dataset, is not a feasible way to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. This option may produce high-quality labels, but it is also costly, time-consuming, and prone to human errors or biases. Moreover, this option may not scale well with large or complex datasets, which may require more analysts or more time to label.

 This answer is correct because it allows AutoML Tables to handle the time signal in the data and split the data accordingly. This ensures that the model is trained on the historical data and evaluated on the more recent data, which is consistent with the prediction task. AutoML Tables can automatically detect and handle temporal features in the data, such as date, time, and duration. By specifying the Time column, AutoML Tables can also perform time-series forecasting and use the time signal to generate additional features, such as seasonality and trend.  References:

[AutoML Tables: Preparing your training data]

[AutoML Tables: Time-series forecasting]

According to the official exam guide 1 , one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”.  TPUs 2  are Google’s custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads. TPUs are designed to handle large batch sizes, high dimensional data, and complex computations.  TPUs can significantly reduce the training time and compute costs of large language models, especially when used with distributed training strategies, such as MultiWorkerMirroredStrategy 3 . Therefore, option D is the best way to configure a training architecture that minimizes both training time and compute costs for the given use case. The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

TPUs

MultiWorkerMirroredStrategy

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

According t o the web search results, BigQuery ML 1  is a service that allows you to create and execute machine learning models in BigQuery using SQL queries.  BigQuery ML supports various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, deep neural networks, and time series forecasting 1 .  ARIMA_PLUS 2  is a statistical model for time series forecasting that is built in to BigQuery ML. ARIMA_PLUS stands for AutoRegressive Integrated Moving Average with eXogenous regressors. ARIMA_PLUS models the relationship between a target variable and its past values, as well as other external factors that might influence the target variable.  ARIMA_PLUS can handle multiple time series, seasonality, holidays, and missing values 2 . Therefore, option C is the best way to use a solution that can be implemented quickly with minimal effort for the given use case, as it allows you to use SQL queries to build and run a forecasting model in BigQuery without moving the data or writing custom code. The other options are not relevant or optimal for this scenario.  References :

BigQuery ML

ARIMA_PLUS

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

The best option for using Vertex AI Model Monitoring for drift detection and minimizing the cost is to use the features and the feature attributions for monitoring, and set a prediction-sampling-rate value that is closer to 0 than 1. This option allows you to leverage the power and flexibility of Google Cloud to detect feature drift in the input predict requests for custom models, and reduce the storage and computation costs of the model monitoring job. Vertex AI Model Monitoring is a service that can track and compare the results of multiple machine learning runs. Vertex AI Model Monitoring can monitor the model’s prediction input data for feature skew and drift. Feature drift occurs when the feature data distribution in production changes over time. If the original training data is not available, you can enable drift detection to monitor your models for feature drift. Vertex AI Model Monitoring uses TensorFlow Data Validation (TFDV) to calculate the distributions and distance scores for each feature, and compares them with a baseline distribution. The baseline distribution is the statistical distribution of the feature’s values in the training data. If the training data is not available, the baseline distribution is calculated from the first 1000 prediction requests that the model receives. If the distance score for a feature exceeds an alerting threshold that you set, Vertex AI Model Monitoring sends you an email alert. However, if you use a custom model, you can also enable feature attribution monitoring, which can provide more insights into the feature drift. Feature attribution monitoring analyzes the feature attributions, which are the contributions of each feature to the prediction output. Feature attribution monitoring can help you identify the features that have the most impact on the model performance, and the features that have the most significant drift over time.  Feature attribution monitoring can also help you understand the relationship between the features and the prediction output, and the correlation between the features 1 . The prediction-sampling-rate is a parameter that determines the percentage of prediction requests that are logged and analyzed by the model monitoring job. Using a lower prediction-sampling-rate can reduce the storage and computation costs of the model monitoring job, but also the quality and validity of the data. Using a lower prediction-sampling-rate can introduce sampling bias and noise into the data, and make the model monitoring job miss some important features or patterns of the data. However, using a higher prediction-sampling-rate can increase the storage and computation costs of the model monitoring job, and also the amount of data that needs to be processed and analyzed.  Therefore, there is a trade-off between the prediction-sampling-rate and the cost and accuracy of the model monitoring job, and the optimal prediction-sampling-rate depends on the business objec tive and the data characteristics 2 . By using the features and the feature attributions for monitoring, and setting a prediction-sampling-rate value that is closer to 0 than 1, you can use Vertex AI Model Monitoring for drift detection and minimize the cost.

The other options are not as good as option D, for the following reasons:

Option A: Using the features for monitoring and setting a monitoring-frequency value that is higher than the default would not enable feature attribution monitoring, and could increase the cost of the model monitoring job. The monitoring-frequency is a parameter that determines how often the model monitoring job analyzes the logged prediction requests and calculates the distributions and distance scores for each feature. Using a higher monitoring-frequency can increase the frequency and timeliness of the model monitoring job, but also the computation costs of the model monitoring job.  Moreover, using the features for monitoring would not enable feature attribution monitoring, which can provide mor e insights into the feature drift and the model performance 1 .

Option B: Using the features for monitoring and setting a prediction-sampling-rate value that is closer to 1 than 0 would not enable feature attribution monitoring, and could increase the cost of the model monitoring job. The prediction-sampling-rate is a parameter that determines the percentage of prediction requests that are logged and analyzed by the model monitoring job. Using a higher prediction-sampling-rate can increase the quality and validity of the data, but also the storage and computation costs of the model monitoring job.  Moreover, using the features for monitoring would not enable feature attribution monitoring, which can provide more insights into the feature drift and the model performance 1 2 .

Option C: Using the features and the feature attributions for monitoring and setting a monitoring-frequency value that is lower than the default would enable feature attribution monitoring, but could reduce the frequency and timeliness of the model monitoring job. The monitoring-frequency is a parameter that determines how often the model monitoring job analyzes the logged prediction requests and calculates the distributions and distance scores for each feature. Using a lower monitoring-frequency can reduce the computation costs of the model monitoring job, but also the frequency and timeliness of the model monitoring job.  This can make the model monitoring job less responsive and effective in detecting and alerting the feature drif t 1 .

Dataflow  is a fully managed service for executing Apache Beam pipelines that can process streaming or batch data 1 .

Al Platform  is a unified platform that enables you to build and run machine learning applications across Google Cloud 2 .

BigQuery  is a serverless, highly scalable, and cost-effective cloud data warehouse designed for business agility 3 .

These services are suitable for building an ML model to detect anomalies in real-time sensor data, as they can handle large-scale data ingestion, preprocessing, training, serving, storage, and visualization. The other options are not as suitable because:

DataPro c  is a service for running Apache Spark and Apache Hadoop clusters, which are not optimized for streaming data processing 4 .

AutoML  is a suite of machine learning products that enab les developers with limited machine learning expertise to train high-quality models specific to their business needs 5 . However, it does not support custom models or real-time predictions.

Cloud Bigtable  is a scalable, fully managed NoSQL database service for large analytical and operational workloads. However, it is not designed for ad hoc queries or interactive analysis.

Cloud Functions  is a serverless execution environment for building and connecting cloud services. However, it is not suitable for storing or visualizing data.

Cloud Storage  is a service for storing and accessing data on Google Cloud. However, it is not a data warehouse and does not support SQL queries or visualization tools.

Option A is correct because importing the TensorFlow model with BigQuery ML, and running the ml.predict function is the easiest way to execute a batch prediction on a large BigQuery table with a custom TensorFlow model, and store the predicted results in another BigQuery table.  BigQuery ML allows you to import TensorFlow models that are stored in Cloud Storage, and use them for prediction with SQL queries 1 .  The ml.predict function returns a table with the predicted values, which can be saved to another BigQuery table 2 .

Option B is incorrect because using the TensorFlow BigQuery reader to load the data, and using the BigQuery API to write the results to BigQuery requires more effort to build the inference pipeline than option A.  The TensorFlow BigQuery reader is a way to read data from BigQuery into TensorFlow datasets, which can be used for training or prediction 3 .  However, this option also requires writing code to load the TensorFlow model, run the prediction, and use the BigQuery API to write the results back to BigQuery 4 .

Option C is incorrect because creating a Dataflow pipeline to convert the data in BigQuery to TFRecords, running a batch inference on Vertex AI Prediction, and writing the results to BigQuery requires more effort to build the inference pipeline than option A.  Dataflow is a service for creating and running data processing pipelines, such as ETL (extract, transform, load ) or batch processing 5 . Vertex AI Prediction is a service for deploying and serving ML models for online or batch prediction. However, this option also requires writing code to create the Dataflow pipeline, convert the data to TFRecords, run the batch inference, and write the results to BigQuery.

Option D is incorrect because loading the TensorFlow SavedModel in a Dataflow pipeline, using the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and writing the results to BigQuery requires more effort to build the inference pipeline than option A. The BigQuery I/O connector is a way to read and write data from BigQuery within a Dataflow pipeline. However, this option also requires writing code to load the TensorFlow SavedModel, create the custom function for inference, and write the results to BigQuery.

BigQuery is a service that allows you to store and query large amounts of data in a scalable and cost-effective way. You can use BigQuery to ingest the Avro files from the Cloud Storage bucket and perform analytics on the structured data. Avro is a binary file format that can store complex data types and schemas. You can use the bq load command or the BigQuery API to load the Avro files into a BigQuery table. You can then use SQL queries to analyze the data and generate insights. Dataflow is a service that allows you to create and run scalable and portable data processing pipelines on Google Cloud. You can use Dataflow to create the features for your ML models, such as transforming, aggregating, and encoding the data. You can use the Apache Beam SDK to write your Dataflow pipeline code in Python or Java. You can also use the built-in transforms or custom transforms to apply the feature engineering logic to your data. Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud. You can use Vertex AI Feature Store to host the features that your ML models use for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can use the Vertex AI Feature Store API to write the features from your Dataflow pipeline to a feature store entity type. You can then use the Vertex AI Feature Store online serving API to read the features from the feature store and pass them to your ML models for online prediction. By using BigQuery, Dataflow, and Vertex AI Feature Store, you can configure a pipeline that performs analytics, creates features, and hosts the features that your ML models use for online prediction.  References :

BigQuery documentation

Dataflow documentation

Vertex AI Feature Store documentation

Preparing for Goo gle Cloud Certification: Machine Learning Engineer Professional Certificate

 Recommendations AI is a service that allows users to build, test, and deploy personalized product recommendations for their ecommerce websites. It uses Google’s deep learning models to learn from user behavior and product data, and generate high-quality recommendations that can increase revenue, click-through rate, and customer satisfaction. One of the best practices for using Recommendations AI is to choose the right recommendation type for the business objective. The “Frequently Bought Together” recommendation type shows products that are often purchased together with the current product, and encourages users to add more items to their shopping cart. This can increase the average order value and the revenue for each transaction. The other options are not as effective or feasible for this objective. The “Other Products You May Like” recommendation type shows products that are similar to the current product, and may increase the click-through rate, but not necessarily the shopping cart size. Importing the user events and then the product catalog is not a recommended order, as it may cause data inconsistency and missing recommendations. The product catalog should be imported first, and then the user events. Using placeholder values for the product catalog is not a viable option, as it will not produce meaningful recommendations or reflect the real performance of the model.  References:

Recommendations AI documentation

Choosing a recommendation type

Importing data to Recommendations AI

Kubeflow Pipelines allows for efficient use of compute resources through parallel task execution and caching, which eliminates redundant executions 1 . By enabling caching in all the steps of the Kubeflow pipeline, you can avoid re-running the same steps when you execute the pipeline multiple times.  This can significantly speed up the pipeline execution and reduce your development time without incurring additional costs