Professional Machine Learning Engineer Free Practice Questions — Page 5
Question 42
Your team is developing a customer support chatbot for a healthcare company that processes sensitive patient information. You need to ensure that all personally identifiable information (PII) captured during customer conversations is protected prior to storing or analyzing the data. What should you do?
A. Use the Cloud Natural Language API to identify and redact PII in chatbot conversations.
B. Use the Cloud Natural Language API to classify and categorize all data, including PII, in chatbot conversations.
C. Use the DLP API to encrypt PII in chatbot conversations before storing the data.
D. Use the DLP API to scan and de-identify PII in chatbot conversations before storing the data.
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Correct Answer: D
Explanation: The Cloud Data Loss Prevention (DLP) API is designed to detect sensitive data such as PII and apply de-identification techniques (masking, tokenization, redaction) before data is stored or analyzed. This directly addresses protecting patient information while preserving analytical utility. Cloud Natural Language focuses on text analysis, not PII protection, and encryption alone does not meet the requirement to scan and handle PII within conversations.
Question 43
You work for an ecommerce company that wants to automatically classify products in images to improve user experience. You have a substantial dataset of labeled images depicting various unique products. You need to implement a solution for identifying custom products that is scalable, effective, and can be rapidly deployed. What should you do?
A. Develop a rule-based system to categorize the images.
B. Use a TensorFlow deep learning model that is trained on the image dataset.
C. Use a pre-trained object detection model from Model Garden.
D. Use AutoML Vision to train a model using the image dataset.
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Correct Answer: D
Explanation: AutoML Vision is designed for custom image classification with labeled datasets. It requires minimal ML expertise, scales automatically, and allows rapid training and deployment, making it more suitable than rule-based systems, manual deep learning development, or generic pre-trained models for identifying unique custom products.
Question 44
You work at a retail company, and are tasked with developing an ML model to predict product sales. Your company’s historical sales data is stored in BigQuery and includes features such as date, store location, product category, and promotion details. You need to choose the most effective combination of a BigQuery ML model and feature engineering to maximize prediction accuracy. What should you do?
A. Use a linear regression model. Perform one-hot encoding on categorical features, and create additional features based on the date, such as day of the week or month.
B. Use a boosted tree model. Perform label encoding on categorical features, and transform the date column into numeric values.
C. Use an autoencoder model. Perform label encoding on categorical features, and normalize the date column.
D. Use a matrix factorization model. Perform one-hot encoding on categorical features, and create interaction features between the store location and product category variables.
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Correct Answer: A
Explanation: BigQuery ML works best with straightforward, well-understood models and explicit feature engineering. Linear regression is appropriate for sales prediction when features like promotions and seasonality are important. One-hot encoding is the correct way to handle categorical variables, and deriving date features (day of week, month, seasonality indicators) captures temporal patterns effectively. The other options either use less suitable model types (autoencoder, matrix factorization) or suboptimal encodings (label encoding for trees and raw numeric dates) that can reduce accuracy.
Question 45
Your organization’s employee onboarding team wants you to build an interactive self-help tool for new employees. The tool needs to receive queries from users and provide answers from the organization’s internal documentation. This documentation is spread across standalone documents such as PDF files. You want to build a solution quickly while minimizing maintenance overhead. What should you do?
A. Create a custom chatbot user interface hosted on App Engine. Use Vertex AI to fine-tune a Gemini model on the organization’s internal documentation. Send users’ queries to the fine-tuned model by using the custom chatbot and return the model’s responses to the users.
B. Deploy an internal website to a Google Kubernetes Engine (GKE) cluster. Build a search index by ingesting all of the organization’s internal documentation. Use Vertex AI Vector Search to implement a semantic search that retrieves results from the search index based on the query entered into the search box.
C. Use Vertex AI Agent Builder to create an agent. Securely index the organization’s internal documentation to the agent’s datastore. Send users’ queries to the agent and return the agent’s grounded responses to the users.
D. Deploy an internal website to a Google Kubernetes Engine (GKE) cluster. Organize the relevant internal documentation into sections. Collect user feedback on website content and store it in BigQuery. Request that the onboarding team regularly update the links based on user feedback.
Show Answer
Correct Answer: C
Explanation: The requirement is to quickly build an interactive self-help tool that answers questions grounded in internal PDF documentation with minimal maintenance. Vertex AI Agent Builder is designed for this use case: it can securely index standalone documents, perform retrieval-augmented generation automatically, and return grounded answers without custom infrastructure, fine-tuning, or search pipeline management. Other options require more custom development, ongoing maintenance, or are less suitable for conversational Q&A over documents.
Question 46
You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model. What should you do?
A. Create a Vertex AI Workbench notebook. Use IPython magic to run the CREATE MODEL statement to create an ARIMA model.
B. Access BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create an AutoML regression model.
C. Create a Vertex AI Workbench notebook. Use IPython magic to run the CREATE MODEL statement to create an AutoML regression model.
D. Access BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create an ARIMA model.
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Correct Answer: B
Explanation: The requirement is to predict customer lifetime value using historical data in BigQuery with the simplest possible approach. BigQuery ML allows training models directly with SQL, avoiding notebook setup or custom code. Customer lifetime value is a regression problem, not a pure time series forecast, so AutoML regression is more appropriate than ARIMA. Running a CREATE MODEL statement for an AutoML regression model directly in BigQuery Studio is therefore the simplest and most suitable solution.
Question 47
You are developing a batch process that will train a custom model and perform predictions. You need to be able to show lineage for both your model and the batch predictions. What should you do?
A. 1. Upload your dataset to BigQuery. 2. Use a Vertex AI custom training job to train your model. 3. Generate predictions by using Vertex AI SDK custom prediction routines.
B. 1. Use Vertex AI Experiments to evaluate model performance during training. 2. Register your model in Vertex AI Model Registry. 3. Generate batch predictions in Vertex AI.
C. 1. Create a Vertex AI managed dataset. 2. Use a Vertex AI training pipeline to train your model. 3. Generate batch predictions in Vertex AI.
D. 1. Use a Vertex AI Pipelines custom training job component to train your model. 2. Generate predictions by using a Vertex AI Pipelines model batch predict component.
Show Answer
Correct Answer: D
Explanation: To show end-to-end lineage for both model training and batch predictions, you need a workflow that automatically tracks artifacts, inputs, outputs, and execution metadata. Vertex AI Pipelines provides built-in lineage tracking. Using a custom training job component captures training lineage, and using the pipelines batch prediction component captures prediction lineage, linking datasets, model artifacts, and runs. The other options lack full, automatic lineage across both training and batch prediction steps.
Question 48
You are developing a TensorFlow Extended (TFX) pipeline with standard TFX components. The pipeline includes data preprocessing steps. After the pipeline is deployed to production, it will process up to 100 TB of data stored in BigQuery. You need the data preprocessing steps to scale efficiently, publish metrics and parameters to Vertex AI Experiments, and track artifacts by using Vertex ML Metadata. How should you configure the pipeline run?
A. Run the TFX pipeline in Vertex AI Pipelines. Configure the pipeline to use Vertex AI Training jobs with distributed processing.
B. Run the TFX pipeline in Vertex AI Pipelines. Set the appropriate Apache Beam parameters in the pipeline to run the data preprocessing steps in Dataflow.
C. Run the TFX pipeline in Dataproc by using the Apache Beam TFX orchestrator. Set the appropriate Vertex AI permissions in the job to publish metadata in Vertex AI.
D. Run the TFX pipeline in Dataflow by using the Apache Beam TFX orchestrator. Set the appropriate Vertex AI permissions in the job to publish metadata in Vertex AI.
Show Answer
Correct Answer: B
Explanation: Running the TFX pipeline in Vertex AI Pipelines provides native integration with Vertex AI Experiments and Vertex ML Metadata for tracking metrics, parameters, and artifacts. Configuring Apache Beam to run preprocessing components on Dataflow enables efficient, autoscaling distributed processing required for up to 100 TB of BigQuery data. Vertex AI Training jobs are not intended for large-scale preprocessing, and using Dataproc/Dataflow as the orchestrator lacks the tight Vertex AI integration provided by Vertex AI Pipelines.
Question 49
You work for a company that builds bridges for cities around the world. To track the progress of projects at the construction sites, your company has set up cameras at each location. Each hour, the cameras take a picture that is sent to a Cloud Storage bucket. A team of specialists reviews the images, filters important ones, and then annotates specific objects in them. You want to propose using an ML solution that will help the company scale and reduce costs. You need the solution to have minimal up-front cost. What method should you propose?
A. Train an AutoML object detection model to annotate the objects in the images to help specialists with the annotation task.
B. Use the Cloud Vision API to automatically annotate objects in the images to help specialists with the annotation task.
C. Create a BigQuery ML classification model to classify important images. Use the model to predict which new images are important to help specialists with the filtering task.
D. Use Vertex AI to train an open source object detection to annotate the objects in the images to help specialists with the annotation task.
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Correct Answer: B
Explanation: The requirement is an ML solution with minimal up-front cost that helps scale image filtering/annotation. The Cloud Vision API provides pre-trained object detection and labeling with no training or infrastructure setup and is pay-per-use, so it can be used immediately to assist specialists. AutoML or Vertex AI training and open-source models require labeled data, training time, and training costs. BigQuery ML is not suitable for image-based object detection.
Question 50
You are tasked with building an MLOps pipeline to retrain tree-based models in production. The pipeline will include components related to data ingestion, data processing, model training, model evaluation, and model deployment. Your organization primarily uses PySpark-based workloads for data preprocessing. You want to minimize infrastructure management effort. How should you set up the pipeline?
A. Set up a TensorFlow Extended (TFX) pipeline on Vertex AI Pipelines to orchestrate the MLOps pipeline. Write a custom component for the PySpark-based workloads on Dataproc.
B. Set up a Vertex AI Pipelines to orchestrate the MLOps pipeline. Use the predefined Dataproc component for the PySpark-based workloads.
C. Set up Kubeflow Pipelines on Google Kubernetes Engine to orchestrate the MLOps pipeline. Write a custom component for the PySparkbased workloads on Dataproc.
D. Set up Cloud Composer to orchestrate the MLOps pipeline. Use Dataproc workflow templates for the PySpark-based workloads in Cloud Composer.
Show Answer
Correct Answer: B
Explanation: Vertex AI Pipelines is a fully managed orchestration service that minimizes infrastructure management. It integrates natively with Dataproc via predefined components, allowing PySpark-based preprocessing without writing custom components. This fits production MLOps needs for tree-based models while avoiding the overhead of managing GKE (Kubeflow), Composer, or custom TFX components.
Question 51
You have developed an AutoML tabular classification model that identifies high-value customers who interact with your organization's website. You plan to deploy the model to a new Vertex AI endpoint that will integrate with your website application. You expect higher traffic to the website during nights and weekends. You need to configure the model endpoint's deployment settings to minimize latency and cost. What should you do?
A. Configure the model deployment settings to use an n1-standard-32 machine type.
B. Configure the model deployment settings to use an n1-standard-4 machine type. Set the minReplicaCount value to 1 and the maxReplicaCount value to 8.
C. Configure the model deployment settings to use an n1-standard-4 machine type and a GPU accelerator. Set the minReplicaCount value to 1 and the maxReplicaCount value to 4.
D. Configure the model deployment settings to use an n1-standard-8 machine type and a GPU accelerator.
Show Answer
Correct Answer: B
Explanation: To minimize both latency and cost, the endpoint should use a modest machine type with autoscaling. AutoML tabular models do not benefit from GPUs, and very large machine types would increase cost without proportional latency gains. Using an n1-standard-4 machine with a minReplicaCount of 1 ensures low baseline cost, while a maxReplicaCount of 8 allows the endpoint to scale out automatically during higher traffic at nights and weekends, maintaining low latency.
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