Professional Machine Learning Engineer Free Practice Questions — Page 10
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: Vertex AI Agent Builder is designed for rapidly building grounded conversational agents over enterprise documents, including PDFs and other unstructured content. It securely indexes the organization's documentation and uses retrieval-augmented generation (RAG) to provide answers with minimal development and maintenance. Fine-tuning a model on documents is unnecessary and less suitable for frequently changing documentation, while building custom search or websites requires more engineering effort.
Question 45
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
B. Use a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled query feature in BigQuery.
C. Write a Cloud Functions script that launches a training and deploying job on AI Platform that is triggered by Cloud Scheduler.
D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model.
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Correct Answer: A
Explanation: Kubeflow Pipelines can orchestrate the end-to-end, multi-step ML workflow, including training, evaluation, and deployment, and schedule recurring runs for monthly retraining. The other options either rely on a narrower tool or do not provide the same ML-specific workflow orchestration.
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: Customer lifetime value prediction is a supervised regression problem, not a univariate time-series forecasting task. BigQuery ML supports creating an AutoML regression model directly with a CREATE MODEL statement in BigQuery Studio, which is the simplest approach because it avoids setting up a Vertex AI Workbench notebook. ARIMA is intended for forecasting time series and is not the appropriate choice for customer-level lifetime value prediction based on historical customer features.
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: Vertex AI Pipelines automatically captures ML Metadata (lineage) across pipeline components, including custom training jobs and batch prediction components. Using a custom training component followed by the model batch predict component provides end-to-end lineage linking datasets, training, model artifacts, and batch prediction outputs. The other options include useful Vertex AI features but do not provide the same built-in workflow lineage across both training and batch prediction.
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: TFX standard components that perform data preprocessing (such as ExampleGen, StatisticsGen, Transform) use Apache Beam. For very large datasets (100 TB in BigQuery), the scalable runner is Dataflow, configured through Apache Beam pipeline arguments. Running the pipeline in Vertex AI Pipelines provides native integration with Vertex ML Metadata and Vertex AI Experiments for artifact tracking, metrics, and parameters. Vertex AI Training jobs are intended for model training rather than Beam-based preprocessing, and using Beam orchestrators directly on Dataproc or Dataflow does not provide the same managed Vertex AI Pipelines integration.
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 emphasizes an ML solution with minimal up-front cost that helps scale the image review process. Cloud Vision API provides pre-trained object detection and labeling with a pay-per-use model, so there is no need to collect labeled training data or pay for model training. AutoML and custom Vertex AI object detection require training data and training jobs, increasing up-front cost. BigQuery ML is not the appropriate choice for image classification in this scenario.
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 the managed orchestration service for ML workflows and minimizes infrastructure management. It provides predefined Dataproc pipeline components for running PySpark jobs, making it a good fit for existing PySpark preprocessing. TFX is primarily oriented toward TensorFlow workflows and would require custom components for PySpark. Kubeflow on GKE requires managing Kubernetes infrastructure, and Cloud Composer is a general workflow orchestrator rather than the recommended managed ML pipeline service for this use case.
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: For an AutoML tabular classification model, CPU-based serving is typically sufficient; GPUs add cost without meaningful benefit for this workload. To handle higher traffic during nights and weekends while minimizing both latency and cost, deploy on a modest machine type with autoscaling enabled. Setting minReplicaCount to 1 keeps baseline cost low, and maxReplicaCount to 8 allows the endpoint to scale out under increased load.
Question 52
You developed a BigQuery ML linear regressor model by using a training dataset stored in a BigQuery table. New data is added to the table every minute. You are using Cloud Scheduler and Vertex AI Pipelines to automate hourly model training, and use the model for direct inference. The feature preprocessing logic includes quantile bucketization and MinMax scaling on data received in the last hour. You want to minimize storage and computational overhead. What should you do?
A. Preprocess and stage the data in BigQuery prior to feeding it to the model during training and inference.
B. Use the TRANSFORM clause in the CREATE MODEL statement in the SQL query to calculate the required statistics.
C. Create a component in the Vertex AI Pipelines directed acyclic graph (DAG) to calculate the required statistics, and pass the statistics on to subsequent components.
D. Create SQL queries to calculate and store the required statistics in separate BigQuery tables that are referenced in the CREATE MODEL statement.
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Correct Answer: B
Explanation: The TRANSFORM clause in BigQuery ML is designed to define feature preprocessing as part of model creation. BigQuery ML persists the learned transformation parameters with the model and automatically applies the same preprocessing during prediction, avoiding separate staging tables or pipeline components. This minimizes both storage and operational/computational overhead compared with preprocessing externally or maintaining separate statistics tables.
Question 53
You work for a hospital. You received approval to collect the necessary patient data, and you trained a Vertex AI tabular AutoML model that calculates patients' risk score for hospital admission. You deployed the model. However, you're concerned that patient demographics might change over time and alter the feature interactions and impact prediction accuracy. You want to be alerted if feature interactions change, and you want to understand the importance of the features for the predictions. You want your alerting approach to minimize cost. What should you do?
A. Create a feature drift monitoring job. Set the sampling rate to 1 and the monitoring frequency to weekly.
B. Create a feature drift monitoring job. Set the sampling rate to 0.1 and the monitoring frequency to weekly.
C. Create a feature attribution drift monitoring job. Set the sampling rate to 1 and the monitoring frequency to weekly.
D. Create a feature attribution drift monitoring job. Set the sampling rate to 0.1 and the monitoring frequency to weekly.
Show Answer
Correct Answer: D
Explanation: Feature attribution drift monitoring is designed to detect changes in feature attributions (explanations), which reflect how features influence predictions over time. This directly addresses concerns about changing feature interactions and the need to understand feature importance. Using a 0.1 sampling rate minimizes monitoring cost while still providing ongoing detection, and weekly monitoring satisfies the alerting requirement.
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