Professional Machine Learning Engineer Free Practice Questions — Page 4
Question 31
You developed an ML model using Vertex AI and deployed it to a Vertex AI endpoint. You anticipate that the model will need to be retrained as new data becomes available. You have configured a Vertex AI Model Monitoring Job. You need to monitor the model for feature attribution drift and establish continuous evaluation metrics. What should you do?
A. Set up alerts using Cloud Logging, and use the Vertex AI console to review feature attributions.
B. Set up alerts using Cloud Logging, and use Looker Studio to create a dashboard that visualizes feature attribution drift. Review the dashboard periodically.
C. Enable request-response logging for the Vertex AI endpoint, and set up alerts using Pub/Sub. Create a Cloud Run function to run TensorFlow Data Validation on your dataset.
D. Enable request-response logging for the Vertex AI endpoint, and set up alerts using Cloud Logging. Review the feature attributions in the Google Cloud console when an alert is received.
Show Answer
Correct Answer: A
Explanation: Vertex AI Model Monitoring supports feature attribution drift monitoring and surfaces results in the Vertex AI/Google Cloud console. Cloud Logging can be used to create alerts for monitoring events. Options C and D require enabling request-response logging on the endpoint, which is not compatible with an endpoint already using Vertex AI Model Monitoring in this context. Building a custom Looker Studio dashboard is unnecessary when the built-in console already provides feature attribution monitoring.
Question 32
You are developing a model to detect fraudulent credit card transactions. You need to prioritize detection, because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to train a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you increase the number of fraudulent transactions that are detected?
A. Add more non-fraudulent examples to the training set.
B. Reduce the maximum number of node hours for training.
C. Increase the probability threshold to classify a fraudulent transaction.
D. Decrease the probability threshold to classify a fraudulent transaction.
Show Answer
Correct Answer: D
Explanation: To detect more fraudulent transactions, increase recall by lowering the classification probability threshold for the fraud class. This causes more transactions to be labeled as fraudulent, reducing false negatives at the expense of more false positives, which aligns with the stated priority.
Question 33
You work as an ML researcher at an investment bank, and you are experimenting with the Gemma large language model (LLM). You plan to deploy the model for an internal use case. You need to have full control of the mode's underlying infrastructure and minimize the model's inference time. Which serving configuration should you use for this task?
A. Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.
B. Deploy the model on a Google Kubernetes Engine (GKE) cluster by using the deployment options in Model Garden.
C. Deploy the model on a Vertex AI endpoint by using one-click deployment in Model Garden.
D. Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by cresting a custom yaml manifest.
Show Answer
Correct Answer: B
Explanation: The requirement is full control over the underlying infrastructure while deploying an open model (Gemma) and minimizing inference time. Vertex AI endpoint options do not provide full infrastructure control. GKE does. Between the GKE choices, Model Garden's GKE deployment provides optimized deployment patterns for Gemma while still giving you control over the Kubernetes infrastructure, avoiding the unnecessary effort of building a deployment from scratch with a custom YAML manifest.
Question 34
Your company recently migrated several of is ML models to Google Cloud. You have started developing models in Vertex AI. You need to implement a system that tracks model artifacts and model lineage. You want to create a simple, effective solution that can also be reused for future models. What should you do?
A. Use a combination of Vertex AI Pipelines and the Vertex AI SDK to integrate metadata tracking into the ML workflow.
B. Use Vertex AI Pipelines for model artifacts and MLflow for model lineage.
C. Use Vertex AI Experiments for model artifacts and use Vertex ML Metadata for model lineage.
D. Implement a scheduled metadata tracking solution using Cloud Composer and Cloud Run functions.
Show Answer
Correct Answer: A
Explanation: Vertex AI Pipelines natively integrates with Vertex ML Metadata to automatically track artifacts, executions, and lineage. Using the Vertex AI SDK allows you to programmatically log additional metadata, making the solution simple, reusable, and aligned with future workflows. Experiments is built on top of ML Metadata for organizing and comparing runs rather than serving as a separate artifact-tracking system, so splitting artifacts and lineage between Experiments and ML Metadata is not the intended design.
Question 35
You lead a data science team that is working on a computationally intensive project involving running several experiments. Your team is geographically distributed and requires a platform that provides the most effective real-time collaboration and rapid experimentation. You plan to add GPUs to speed up your experimentation cycle, and you want to avoid having to manually set up the infrastructure. You want to use the Google-recommended approach. What should you do?
A. Configure a managed Dataproc cluster for large-scale data processing. Configure individual Jupyter notebooks on VMs that each team member uses for experimentation and model development.
B. Use Colab Enterprise with Cloud Storage for data management. Use a Git repository for version control.
C. Use Vertex AI Workbench and Cloud Storage for data management. Use a Git repository for version control.
D. Configure a distributed JupyterLab instance that each team member can access on a Compute Engine VM. Use a shared code repository for version control.
Show Answer
Correct Answer: B
Explanation: The requirements emphasize real-time collaboration, rapid experimentation, GPU support, and avoiding infrastructure management. Colab Enterprise is Google's managed, collaborative notebook environment designed for these priorities. Cloud Storage is appropriate for data management and Git for version control. Vertex AI Workbench is also managed but is intended when you need more control over notebook environments rather than maximizing collaborative editing with minimal infrastructure management.
Question 36
You are an AI engineer that works for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers that have been identified as at risk of churning with personalized offers. You want to deploy the model while minimizing maintenance effort. What should you do?
A. Use Vertex AI’s prebuilt containers for prediction. Deploy the container on Cloud Run to generate online predictions.
B. Use Vertex AI’s prebuilt containers for prediction. Deploy the model on Google Kubernetes Engine (GKE), and configure the model for batch prediction.
C. Deploy the model to a Vertex AI endpoint, and configure the model for batch prediction. Schedule the batch prediction to run weekly.
D. Deploy the model to a Vertex AI endpoint, and configure the model for online prediction. Schedule a job to query this endpoint weekly.
Show Answer
Correct Answer: C
Explanation: Weekly scoring of a population for a retention campaign is a batch inference workload, not a low-latency online serving use case. Deploying the model on Vertex AI and using managed batch prediction with a scheduled weekly job minimizes operational overhead because Vertex AI manages the infrastructure. Online endpoints are unnecessary for weekly batch processing, and Cloud Run or GKE would require more maintenance.
Question 37
Your team is experimenting with developing smaller, distilled LLMs for a specific domain. You have performed batch inference on a dataset by using several variations of your distilled LLMs and stored the batch inference outputs in Cloud Storage. You need to create an evaluation workflow that integrates with your existing Vertex AI pipeline to assess the performance of the LLM versions while also tracking artifacts. What should you do?
A. Develop a custom Python component that reads the batch inference outputs from Cloud Storage, calculates evaluation metrics, and writes the results to a BigQuery table.
B. Use a Dataflow component that processes the batch inference outputs from Cloud Storage, calculates evaluation metrics in a distributed manner, and writes the results to a BigQuery table.
C. Create a custom Vertex AI Pipelines component that reads the batch inference outputs from Cloud Storage, calculates evaluation metrics, and writes the results to a BigQuery table.
D. Use the Automatic side-by-side (AutoSxS) pipeline component that processes the batch inference outputs from Cloud Storage, aggregates evaluation metrics, and writes the results to a BigQuery table.
Show Answer
Correct Answer: D
Explanation: The Automatic side-by-side (AutoSxS) pipeline component is designed for evaluating and comparing multiple LLM variants within Vertex AI Pipelines. It integrates with pipeline artifact tracking and aggregates evaluation results, making it the best fit for comparing several distilled model versions using existing batch inference outputs. The other options require building custom evaluation components and do not provide the specialized side-by-side LLM evaluation workflow.
Question 38
You are an ML engineer at a bank. You need to build a solution that provides transparent and understandable explanations for AI-driven decisions for loan approvals, credit limits, and interest rates. You want to build this system to require minimal operational overhead. What should you do?
A. Deploy the Learning Interpretability Tool (LIT) on App Engine to provide explainability and visualization of the output.
B. Use Vertex Explainable AI to generate feature attributions, and use feature-based explanations for your models.
C. Use AutoML Tables with built-in explainability features, and use Shapley values for explainability.
D. Deploy pre-trained models from TensorFlow Hub to provide explainability using visualization tools.
Show Answer
Correct Answer: B
Explanation: Vertex Explainable AI is the managed Google Cloud service designed to provide feature attributions and explanations for model predictions with minimal operational overhead. It works with Vertex AI models and supports transparent, feature-based explanations suitable for regulated use cases such as lending. LIT is primarily a visualization/debugging tool that requires deployment and maintenance, AutoML Tables is a specific training product rather than the general explainability solution, and TensorFlow Hub does not provide explainability services.
Question 40
You are building an application that extracts information from invoices and receipts. You want to implement this application with minimal custom code and training. What should you do?
A. Use the Cloud Vision API with TEXT_DETECTION type to extract text from the invoices and receipts, and use a pre-built natural language processing (NLP) model to parse the extracted text.
B. Use the Cloud Document AI API to extract information from the invoices and receipts.
C. Use Vertex AI Agent Builder with the pre-built Layout Parser model to extract information from the invoices and receipts.
D. Train an AutoML Natural Language model to classify and extract information from the invoices and receipts.
Show Answer
Correct Answer: B
Explanation: Cloud Document AI provides pre-trained processors specifically for invoices and receipts, extracting structured fields such as vendor, invoice number, dates, totals, line items, and taxes with minimal custom code and no custom model training. Cloud Vision OCR only extracts text, Layout Parser is not the purpose-built solution for invoice field extraction, and AutoML Natural Language would require training and is not intended for document form parsing.
Question 41
You work for a bank. You need to train a model by using unstructured data stored in Cloud Storage that predicts whether credit card transactions are fraudulent. The data needs to be converted to a structured format to facilitate analysis in BigQuery. Company policy requires that data containing personally identifiable information (PII) remain in Cloud Storage. You need to implement a scalable solution that preserves the data’s value for analysis. What should you do?
A. Use BigQuery’s authorized views and column-level access controls to restrict access to PII within the dataset.
B. Use the DLP API to de-identify the sensitive data before loading it into BigQuery.
C. Store the unstructured data in a separate PII-compliant BigQuery database.
D. Remove the sensitive data from the files manually before loading them into BigQuery.
Show Answer
Correct Answer: B
Explanation: The requirement is to convert unstructured Cloud Storage data into a structured format for BigQuery while ensuring that PII remains protected and preserving analytical value. Using the Cloud DLP API to de-identify sensitive fields before loading the structured data into BigQuery is the scalable approach. Authorized views and column-level security protect access after data is already in BigQuery and do not satisfy the policy intent. Storing raw PII in BigQuery contradicts the requirement, and manual removal is not scalable.
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