Free Google Professional Machine Learning Engineer Practice Exams - Page 3 of 41

Question #21

You need to analyze user activity data from your company’s mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery.

What should you do?

A . Configure Pub/Sub to stream the data into BigQuery.
B . Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
C . Run a Dataflow streaming job to ingest the data into BigQuery.
D . Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,

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Correct Answer: C
C

Explanation:

The best option to ensure real-time ingestion of the user activity data into BigQuery is to run a Dataflow streaming job to ingest the data into BigQuery. Dataflow is a fully managed service that can handle both batch and stream processing of data, and can integrate seamlessly with BigQuery and other Google Cloud services. Dataflow can also use Apache Beam as the programming model, which provides a unified and portable API for developing data pipelines. By using Dataflow, you can avoid the complexity and overhead of managing your own infrastructure, and focus on the logic and transformation of your data. Dataflow can also handle various types of data, such as structured, unstructured, or binary data, and can apply windowing, aggregation, and other operations on the data streams.

The other options are not optimal for the following reasons:

Question #22

You recently designed and built a custom neural network that uses critical dependencies specific to your organization’s framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure.

What should you do?

A . Use a built-in model available on Al Platform Training
B . Build your custom container to run jobs on Al Platform Training
C . Build your custom containers to run distributed training jobs on Al Platform Training
D . Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training

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Correct Answer: C
C

Explanation:

AI Platform Training is a service that allows you to run your machine learning training jobs on Google Cloud using various features, model architectures, and hyperparameters. You can use AI Platform Training to scale up your training jobs, leverage distributed training, and access specialized hardware such as GPUs and TPUs1. AI Platform Training supports several pre-built containers that provide different ML frameworks and dependencies, such as TensorFlow, PyTorch, scikit-learn, and XGBoost2. However, if the ML framework and related dependencies that you need are not supported by the pre-built containers, you can build your own custom containers and use them to run your training jobs on AI Platform Training3.

Custom containers are Docker images that you create to run your training application. By using custom containers, you can specify and pre-install all the dependencies needed for your application, and have full control over the code, serving, and deployment of your model4. Custom containers also enable you to run distributed training jobs on AI Platform Training, which can help you train large-scale and complex models faster and more efficiently5. Distributed training is a technique that splits the training data and computation across multiple machines, and coordinates them to update the model parameters. AI Platform Training supports two types of distributed training: parameter server and collective all-reduce. The parameter server architecture consists of a set of workers that perform the computation, and a set of servers that store and update the model parameters. The collective all-reduce architecture consists of a set of workers that perform the computation and synchronize the model parameters among themselves. Both architectures also have a scheduler that coordinates the workers and servers.

For the use case of training a custom neural network that uses critical dependencies specific to your organization’s framework, the best option is to build your custom containers to run distributed training jobs on AI Platform Training. This option allows you to use the ML framework and dependencies of your choice, and train your model on multiple machines without having to manage the infrastructure. Since your ML framework of choice uses the scheduler, workers, and servers distribution structure, you can use the parameter server architecture to run your distributed training job on AI Platform Training. You can specify the number and type of machines, the custom container image, and the training application arguments when you submit your training job. Therefore, building your custom containers to run distributed training jobs on AI Platform Training is the best option for this use case.

Reference: AI Platform Training documentation

Pre-built containers for training

Custom containers for training

Custom containers overview | Vertex AI | Google Cloud

Distributed training overview

[Types of distributed training]

[Distributed training architectures]

[Using custom containers for training with the parameter server architecture]

Question #23

You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving.

How should you configure the preprocessing routine?

A . Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.
B . Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.
C . Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.
D . Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.

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Correct Answer: D
D

Explanation:

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”. TensorFlow Transform2 is a library for preprocessing data with TensorFlow. TensorFlow Transform enables you to define and execute distributed pre-processing or feature engineering functions on large data sets, and then export the same functions as a TensorFlow graph for re-use during training or serving. TensorFlow Transform can handle both instance-level and full-pass data transformations. Apache Beam3 is an open source framework for building scalable and portable data pipelines. Apache Beam supports both batch and streaming data processing. Dataflow4 is a fully managed service for running Apache Beam pipelines on Google Cloud. Dataflow handles the provisioning and management of the compute resources, as well as the optimization and execution of the pipelines. Therefore, option D is the best way to configure the preprocessing routine for the given use case, as it allows you to use the same preprocessing logic during model training and serving, and leverage the scalability and performance of Dataflow. The other options are not relevant or optimal for this scenario.

Reference: Professional ML Engineer Exam Guide

TensorFlow Transform

Apache Beam

Dataflow

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Question #24

Train the model by using the Workbench instance.

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Correct Answer: A

Explanation:

TFRecord is a binary file format that stores your data as a sequence of binary strings1. TFRecord files are efficient, scalable, and easy to process1. Sharding is a technique that splits a large file into smaller files, which can improve parallelism and performance2. Dataflow is a service that allows you to create and run data processing pipelines on Google Cloud3. Dataflow can create sharded TFRecord files from your images in a Cloud Storage directory4.

tf.data.TFRecordDataset is a class that allows you to read and parse TFRecord files in TensorFlow. You can use this class to create a tf.data.Dataset object that represents your input data for training. tf.data.Dataset is a high-level API that provides various methods to transform, batch, shuffle, and prefetch your data.

Vertex AI Training is a service that allows you to train your custom models on Google Cloud using various hardware accelerators, such as GPUs. Vertex AI Training supports TensorFlow models and can read data from Cloud Storage. You can use Vertex AI Training to train your image classification model by using a V100 GPU, which is a powerful and fast GPU for deep learning.

Reference: TFRecord and tf.Example | TensorFlow Core

Sharding | TensorFlow Core

Dataflow | Google Cloud

Creating sharded TFRecord files | Google Cloud

[tf.data.TFRecordDataset | TensorFlow Core v2.6.0]

[tf.data: Build TensorFlow input pipelines | TensorFlow Core]

[Vertex AI Training | Google Cloud]

[NVIDIA Tesla V100 GPU | NVIDIA]

Question #25

You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII) You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields

What should you do?

A . Use the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.
B . Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.
C . Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.
D . Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.

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Correct Answer: A
A

Explanation:

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”. Cloud Data Loss Prevention (DLP) API2 is a service that provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams, such as text blocks and images. Cloud DLP API helps you discover, classify, and protect your sensitive data by using techniques such as de-identification, masking, tokenization, and bucketing. You can use Cloud DLP API to de-identify the PII data before performing data exploration and preprocessing, and retain the data utility for ML purposes. Therefore, option A is the best way to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. The other options are not relevant or optimal for this scenario.

Reference: Professional ML Engineer Exam Guide

Cloud Data Loss Prevention (DLP) API

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Question #25

You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII) You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields

What should you do?

A . Use the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.
B . Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.
C . Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.
D . Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.

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Correct Answer: A
A

Explanation:

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”. Cloud Data Loss Prevention (DLP) API2 is a service that provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams, such as text blocks and images. Cloud DLP API helps you discover, classify, and protect your sensitive data by using techniques such as de-identification, masking, tokenization, and bucketing. You can use Cloud DLP API to de-identify the PII data before performing data exploration and preprocessing, and retain the data utility for ML purposes. Therefore, option A is the best way to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. The other options are not relevant or optimal for this scenario.

Reference: Professional ML Engineer Exam Guide

Cloud Data Loss Prevention (DLP) API

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Question #27

You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible.

What should you do?

A . Retrain the model by using BigQuery ML. and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
B . Retrain the model by using Vertex Al Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
C . Alter the model by using BigQuery ML and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
D . Export the model from BigQuery ML to Cloud Storage Import the model into Vertex Al Model Registry Deploy the model to a Vertex Al endpoint.

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Correct Answer: A
A

Explanation:

BigQuery ML is a service that allows you to create and train ML models using SQL queries. You can use BigQuery ML to train an AutoML regression model, which is a type of model that automatically selects the best features and architecture for your data. You can also specify Vertex AI as the model registry, which is a service that allows you to store and manage your ML models. By using Vertex AI as the model registry, you can easily deploy your model to a Vertex AI endpoint, which is a service that allows you to serve your ML models online and scale them automatically. By using BigQuery ML, Vertex AI model registry, and Vertex AI endpoint, you can deploy your model for online prediction as quickly as possible, without having to export, import, or retrain your model.

Reference: BigQuery ML documentation

Vertex AI documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Question #27

You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible.

What should you do?

A . Retrain the model by using BigQuery ML. and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
B . Retrain the model by using Vertex Al Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
C . Alter the model by using BigQuery ML and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
D . Export the model from BigQuery ML to Cloud Storage Import the model into Vertex Al Model Registry Deploy the model to a Vertex Al endpoint.

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Correct Answer: A
A

Explanation:

BigQuery ML is a service that allows you to create and train ML models using SQL queries. You can use BigQuery ML to train an AutoML regression model, which is a type of model that automatically selects the best features and architecture for your data. You can also specify Vertex AI as the model registry, which is a service that allows you to store and manage your ML models. By using Vertex AI as the model registry, you can easily deploy your model to a Vertex AI endpoint, which is a service that allows you to serve your ML models online and scale them automatically. By using BigQuery ML, Vertex AI model registry, and Vertex AI endpoint, you can deploy your model for online prediction as quickly as possible, without having to export, import, or retrain your model.

Reference: BigQuery ML documentation

Vertex AI documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Question #27

You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible.

What should you do?

A . Retrain the model by using BigQuery ML. and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
B . Retrain the model by using Vertex Al Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
C . Alter the model by using BigQuery ML and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
D . Export the model from BigQuery ML to Cloud Storage Import the model into Vertex Al Model Registry Deploy the model to a Vertex Al endpoint.

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Correct Answer: A
A

Explanation:

BigQuery ML is a service that allows you to create and train ML models using SQL queries. You can use BigQuery ML to train an AutoML regression model, which is a type of model that automatically selects the best features and architecture for your data. You can also specify Vertex AI as the model registry, which is a service that allows you to store and manage your ML models. By using Vertex AI as the model registry, you can easily deploy your model to a Vertex AI endpoint, which is a service that allows you to serve your ML models online and scale them automatically. By using BigQuery ML, Vertex AI model registry, and Vertex AI endpoint, you can deploy your model for online prediction as quickly as possible, without having to export, import, or retrain your model.

Reference: BigQuery ML documentation

Vertex AI documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Question #30

You recently deployed a model to a Vertex Al endpoint Your data drifts frequently so you have enabled request-response logging and created a Vertex Al Model Monitoring job. You have observed that your model is receiving higher traffic than expected. You need to reduce the model monitoring cost while continuing to quickly detect drift.

What should you do?

A . Replace the monitoring job with a DataFlow pipeline that uses TensorFlow Data Validation (TFDV).
B . Replace the monitoring job with a custom SQL scnpt to calculate statistics on the features and predictions in BigQuery.
C . Decrease the sample_rate parameter in the Randomsampleconfig of the monitoring job.
D . Increase the monitor_interval parameter in the scheduieconfig of the monitoring job.

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Correct Answer: C
C

Explanation:

According to the official exam guide1, one of the skills assessed in the exam is to “configure and optimize model monitoring jobs”. The Vertex AI Model Monitoring documentation states that “to reduce the cost of model monitoring, you can configure the sample rate of the requests that are logged and analyzed by model monitoring”. Therefore, decreasing the sample_rate parameter in the Randomsampleconfig of the monitoring job would reduce the model monitoring cost while continuing to quickly detect drift. The other options are not relevant or optimal for this scenario.

Reference: Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Professional ML Engineer Exam Guide

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions [Vertex AI Model Monitoring]