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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 171–175 (page: 36)

Updated On: 25-Oct-2026
Page 36 of 82
PDF with 402 Questions

You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible.
What should you do?

  1. Use the BigQuery console to execute your query, and then save the query results into a new BigQuery table.
  2. Write a Python script that uses the BigQuery API to execute queries against BigQuery. Execute this script as the first step in your Kubeflow pipeline.
  3. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries.
  4. Locate the Kubeflow Pipelines repository on GitHub. Find the BigQuery Query Component, copy that component’s URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery.

Answer(s): D



You are building a linear regression model on BigQuery ML to predict a customer’s likelihood of purchasing your company’s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables.
What should you do?

  1. Use TensorFlow to create a categorical variable with a vocabulary list. Create the vocabulary file, and upload it as part of your model to BigQuery ML.
  2. Create a new view with BigQuery that does not include a column with city information
  3. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5, and then use that number to represent the city in the model.
  4. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.

Answer(s): D



You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed. You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex AI Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?

  1. 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata.
    2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_merrics function to log loss values.
  2. 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata.
    2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.
  3. 1. Create a Vertex AI TensorBoard instance and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance.
    2. Use the assign_input_artifact method to track the preprocessed data and use the log_time_series_metrics function to log loss values.
  4. 1. Create a Vertex AI TensorBoard instance, and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance.
    2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.

Answer(s): B



You are part of a team of ML engineers building a new churn prediction model using PyTorch. Your team’s raw data is stored in BigQuery. For the initial prototyping phase, your team needs a collaborative, cloud-based environment that meets the following criteria:
● It must provide access to GPU accelerators.
● It must allow seamless authentication to BigQuery without manual management of service account keys.
● It must be pre-configured with common ML frameworks.
You need to choose the most secure solution with the least setup overhead for your team to begin model development and experimentation.
What should you do?

  1. Configure a JupyterHub server on a cluster of Compute Engine instances. Install the necessary GPU drivers and other required libraries on each instance. Instruct each team member to create and download a service account key for BigQuery access.
  2. Package the PyTorch model code in a Docker container, and upload it to Artifact Registry. Use Agent Platform Serverless Training to run a custom training job that runs on a GPU-enabled machine type. Instruct the team to analyze the training logs to iterate on the model.
  3. Instruct team members to use the free version of Agent Platform Colab Enterprise. Use the Agent Platform SDK for Python to load the data into a shared Agent Platform Colab Enterprise notebook. Have each member perform analysis in the shared notebook.
  4. Create Agent Platform Workbench instances for your team members. Select instance types with attached GPUs and the preinstalled PyTorch kernel. Use the BigQuery client library to access the data, and use the instances’ managed identity for authentication.

Answer(s): D

Explanation:

Agent Platform Workbench provides managed Jupyter environments with GPU-enabled machine types and preconfigured PyTorch kernels. Its managed identity enables secure access to BigQuery through Application Default Credentials, eliminating the need to create, distribute, or maintain service account keys while minimizing setup effort for collaborative experimentation.



You are developing an ML model to identify your company’s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex AI Training. You need to read images at scale during training while minimizing data I/O bottlenecks.
What should you do?

  1. Load the images directly into the Vertex AI compute nodes by using Cloud Storage FUSE. Read the images by using the tf.data.Dataset.from_tensor_slices function.
  2. Create a Vertex AI managed dataset from your image data. Access the AIP_TRAINING_DATA_URI environment variable to read the images by using the tf.data.Dataset.list_files function.
  3. Convert the images to TFRecords and store them in a Cloud Storage bucket. Read the TFRecords by using the tf.data.TFRecordDataset function.
  4. Store the URLs of the images in a CSV file. Read the file by using the tf.data.experimental.CsvDataset function.

Answer(s): C



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