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Google Professional Machine Learning Engineer Sample Questions (Q100-Q105):

NEW QUESTION # 100
You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?

• A. Store all ML metadata in Google Cloud's operations suite.
• B. Manage your ML workflows with Vertex ML Metadata.
• C. Manage all relational entities in the Hive Metastore.
• D. Store your tf.logging data in BigQuery.

Answer: B

Explanation:
Vertex ML Metadata is a service that lets you track and manage the metadata produced by your ML workflows in a centralized way. It helps you have reproducible experiments by generating artifacts that represent the data, parameters, and metrics used or produced by your ML system. You can also analyze the lineage and performance of your ML artifacts using Vertex ML Metadata.
Some of the benefits of using Vertex ML Metadata are:
* It captures your ML system's metadata as a graph, where artifacts and executions are nodes, and events are edges that link them as inputs or outputs.
* It allows you to create contexts to group sets of artifacts and executions together, such as experiments, runs, or projects.
* It supports querying and filtering the metadata using the Vertex AI SDK for Python or REST commands.
* It integrates with other Vertex AI services, such as Vertex AI Pipelines and Vertex AI Experiments, to automatically log metadata and artifacts.
The other options are not suitable for tracking and managing ML metadata in a centralized way.
* Option A: Storing your tf.logging data in BigQuery is not enough to capture the full metadata of your ML system, such as the artifacts and their lineage. BigQuery is a data warehouse service that is mainly used for analytics and reporting, not for metadata management.
* Option B: Managing all relational entities in the Hive Metastore is not a good solution for ML metadata, as it is designed for storing metadata of Hive tables and partitions, not for ML artifacts and executions. Hive Metastore is a component of the Apache Hive project, which is a data warehouse system for querying and analyzing large datasets stored in Hadoop.
* Option C: Storing all ML metadata in Google Cloud's operations suite is not a feasible option, as it is a set of tools for monitoring, logging, tracing, and debugging your applications and infrastructure, not for ML metadata. Google Cloud's operations suite does not provide the features and integrations that Vertex ML Metadata offers for ML workflows.

 

NEW QUESTION # 101
You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

• A. Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance.
• B. Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al.
• C. Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al.
• D. Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance.

Answer: B

 

NEW QUESTION # 102
You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company's products. The workflow logic is shown in the diagram Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?

• A. Expose each individual model as an endpoint in Vertex Al Endpoints. Create a custom container endpoint to orchestrate the workflow.
• B. Expose each individual model as an endpoint in Vertex Al Endpoints. Use Cloud Run to orchestrate the workflow.
• C. Create a custom container endpoint for the workflow that loads each models individual files Track the versions of each individual model in BigQuery.
• D. Load each model's individual files into Cloud Run Use Cloud Run to orchestrate the workflow Track the versions of each individual model in BigQuery.

Answer: B

Explanation:
The option C is the most efficient and scalable solution for deploying a machine learning workflow with multiple models while ensuring version control and minimizing compute resource utilization. By exposing each model as an endpoint in Vertex AI Endpoints, it allows for easy versioning and management of individual models. Using Cloud Run to orchestrate the workflow ensures that the application can scale down to zero, thus minimizing resource utilization when not in use. Cloud Run is a service that allows you to run stateless containers on a fully managed environment or on Google Kubernetes Engine. You can use Cloud Run to invoke the endpoints of each model in the workflow and pass the data between them. You can also use Cloud Run to handle the input and output of the workflow and provide an HTTP interface for the application.
References:
* Vertex AI Endpoints documentation
* Cloud Run documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

 

NEW QUESTION # 103
You are developing an image recognition model using PyTorch based on ResNet50 architecture Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs What should you do?

• A. Configure a Compute Engine VM with all the dependencies that launches the training Tram your model with Vertex Al using a custom tier that contains the required GPUs.
• B. Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to tram your model.
• C. Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.
• D. Package your code with Setuptools and use a pre-built container. Train your model with Vertex Al using a custom tier that contains the required GPUs.

Answer: D

Explanation:
Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides a managed service for training custom models with various frameworks, such as TensorFlow, PyTorch, scikit-learn, and XGBoost. To train your PyTorch model with Vertex AI, you need to package your code with Setuptools, which is a Python tool for creating and distributing packages. You also need to use a pre-built container, which is a Docker image that contains the dependencies and libraries for your framework. You can choose from a list of pre-built containers provided by Google, or create your own custom container. By using a pre-built container, you can avoid the hassle of installing and configuring the environment for your model. You can also specify a custom tier for your training job, which allows you to select the number and type of GPUs you want to use. You can choose from various GPU options, such as V100, P100, K80, and T4. By using 4 V100 GPUs, you can leverage the high performance and memory capacity of these accelerators to train your model faster and cheaper than using CPUs. This solution requires minimal changes to your code and can scale your training workload efficiently. Reference:
Vertex AI | Google Cloud
Custom training with pre-built containers | Vertex AI
[Using GPUs | Vertex AI]

 

NEW QUESTION # 104
You work for a food product company. Your company's historical sales data is stored in BigQuery You need to use Vertex Al's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales You plan to implement a data preprocessing algorithm that performs min-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost and development effort How should you configure this workflow?

• A. Write SQL queries to transform the data in-place in BigQuery.
• B. Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data process it and write it back to BigQuery.
• C. Write the transformations into Spark that uses the spark-bigquery-connector and use Dataproc to preprocess the data.
• D. Add the transformations as a preprocessing layer in the TensorFlow models.

Answer: A

 

NEW QUESTION # 105
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