PMLE Practice Test 8 — 10 Questions

Question 1

A hospital wants to deploy a machine learning model for detecting anomalies in patient vital signs. The model was trained on historical data but must comply with HIPAA regulations. The model serving must be low-latency (under 100 ms) and handle up to 1000 requests per second. Which architecture should they use on Google Cloud?

Accepted Answer

Deploy the model to Vertex AI Prediction with a private endpoint and use VPC Service Controls for data isolation. Vertex AI Prediction with a private endpoint and VPC Service Controls meets all requirements: it provides low-latency (sub-100ms) online predictions for up to 1000 QPS, enforces HIPAA compliance by isolating the model within a VPC and preventing data exfiltration, and supports autoscaling. Batch Prediction (A) cannot meet the latency requirement, BigQuery ML (B) is designed for analytical queries not real-time serving, and Cloud Run (C) lacks native HIPAA-compliant data isolation controls.

Answer

Use Vertex AI Batch Prediction to run predictions in batch jobs every hour

Answer

Use BigQuery ML to run predictions directly from a BigQuery table

Answer

Deploy the model as a container on Cloud Run with a load balancer

Question 2

A retail company wants to build a customer churn prediction model using BigQuery ML. The data is stored in BigQuery tables and includes customer demographics, purchase history, and support interactions. The data scientist wants to experiment with different model types quickly without moving data to another environment. Which approach should they use?

Accepted Answer

Use BigQuery ML to create and evaluate models directly in BigQuery.. BigQuery ML (BQML) allows data scientists to create, train, and evaluate machine learning models directly in BigQuery using SQL, without moving data to another environment. This approach supports rapid experimentation with various model types (e.g., logistic regression, boosted trees, deep neural networks) and is ideal for the stated requirement of quick iteration while keeping data in place.

Answer

Use Cloud Composer to orchestrate a custom training pipeline on Vertex AI.

Answer

Use AI Platform Notebooks with pandas and scikit-learn.

Answer

Export the data to Cloud Storage and use Vertex AI AutoML Tables.

Question 3

A company wants to implement a document processing solution that extracts key information from invoices and receipts. They have limited ML expertise and want to use a pre-trained solution as much as possible. Which Google Cloud service should they use?

Accepted Answer

Document AI with a pre-trained invoice processor.. Document AI with a pre-trained invoice processor is the correct choice because it provides a fully managed, pre-trained solution specifically designed for extracting structured data (e.g., vendor name, invoice number, line items) from invoices and receipts. This aligns with the company's limited ML expertise and desire to use a pre-trained solution, requiring no custom model training or complex coding.

Answer

AutoML Natural Language with custom entity extraction.

Answer

Vertex AI Workbench with custom Python scripts.

Answer

Cloud Vision API with OCR.

Question 4

A team has trained a sentiment analysis model using PyTorch on Vertex AI Training. They now want to deploy it for online predictions with low latency. Which TWO actions should they take? (Choose 2)

Accepted Answer

Use a machine type with a GPU for faster inference.. Option B is correct because GPU-accelerated inference significantly reduces latency for deep learning models like sentiment analysis, especially when using PyTorch, which has native CUDA support. Vertex AI Prediction supports GPU machine types (e.g., n1-standard-4 with NVIDIA T4) that can process batched requests faster than CPUs, directly addressing the low-latency requirement.

Answer

Create multiple model versions for A/B testing.

Answer

Enable batch prediction instead of online prediction.

Answer

Convert the model to TensorFlow SavedModel format.

Question 5

Refer to the exhibit. A data analyst creates a BigQuery ML logistic regression model for churn prediction. The model evaluation shows high precision but low recall. Which change to the model creation would most likely improve recall?

Accepted Answer

Use ML.ADJUST_THRESHOLD to lower the classification threshold.. Option C is correct because lowering the classification threshold (e.g., from 0.5 to 0.3) will classify more customers as positive (churn), increasing recall (true positives / (true positives + false negatives)). In BigQuery ML, ML.ADJUST_THRESHOLD directly modifies the decision boundary, trading off precision for recall. This is the most direct way to address low recall without altering the model architecture or training data.

Answer

Drop more columns to reduce overfitting.

Answer

Increase the training data by including customers without churn dates.

Answer

Change model_type to 'BOOSTED_TREE_CLASSIFIER'.

Question 6

A data scientist wants to quickly train a binary classification model on a tabular dataset stored in BigQuery without writing any code. They have limited ML experience. Which Google Cloud service should they use?

Accepted Answer

BigQuery ML with CREATE MODEL statement using SQL.. Option C is correct because BigQuery ML allows a data scientist to train a binary classification model directly in BigQuery using a `CREATE MODEL` SQL statement, without writing any code or moving data. This is the fastest low-code approach for users with limited ML experience, as it leverages familiar SQL syntax and runs entirely within BigQuery's serverless infrastructure.

Answer

Vertex AI Workbench with a built-in scikit-learn notebook.

Answer

Dataflow with a TensorFlow pipeline.

Answer

AutoML Tables with a direct BigQuery connection.

Question 7

Refer to the exhibit. A data scientist trained a BigQuery ML classification model to detect fraudulent transactions. The dataset has 95% non-fraud (class 0) and 5% fraud (class 1). The evaluation metrics show high accuracy (0.91) but low recall (0.60) for fraud detection. Which low-code approach should the data scientist take to improve recall without significantly sacrificing precision?

Accepted Answer

Use the ML.PREDICT function with a lower classification threshold (e.g., 0.3 instead of 0.5) to capture more positive cases.. Option A is correct because lowering the classification threshold in ML.PREDICT (e.g., from 0.5 to 0.3) causes the model to classify more transactions as fraud, directly increasing recall. This is a low-code adjustment that does not require retraining or complex feature engineering, and it allows the data scientist to trade off precision for recall as needed.

Answer

Apply feature selection to reduce the number of features and focus on the most predictive ones.

Answer

Increase the number of training iterations by setting the MAX_ITERATIONS option to a higher value.

Answer

Re-train the model using AutoML Tables with class weights to penalize false negatives more heavily.

Question 8

A data science team uses BigQuery to store raw data and Vertex AI for model training. They want to ensure that only authorized users can access training data, and that model artifacts are automatically versioned and tracked. Which combination of Google Cloud services should they use?

Accepted Answer

Vertex AI Feature Store with access control and Vertex AI ML Metadata for model versioning. Vertex AI Feature Store provides fine-grained access control to training data, ensuring only authorized users can access it. Vertex AI ML Metadata automatically tracks and versions model artifacts, lineage, and parameters, which aligns with the requirement for automated versioning and tracking.

Answer

Dataflow for data access control and Vertex AI Experiments for model tracking

Answer

Cloud Storage with bucket-level IAM and Cloud Build for versioning

Answer

Cloud Composer for data access control and Cloud Source Repositories for model versioning

Question 9

Which TWO statements about Vertex AI Feature Store are correct? (Choose 2)

Accepted Answer

Feature Store provides a centralized repository for feature data.. Option D is correct because Vertex AI Feature Store is designed as a centralized repository that organizes, stores, and serves feature data consistently across different models and pipelines. This centralization ensures feature reuse, consistency, and governance, preventing data silos and duplication across the ML lifecycle.

Answer

Feature Store automatically applies feature engineering transformations.

Answer

Feature Store can only store numerical features.

Answer

Feature Store can only be used with Vertex AI models.

Question 10

Which THREE actions are best practices for managing ML models in production on Google Cloud? (Choose 3)

Accepted Answer

Monitor model performance and data drift continuously.. Option B is correct because continuous monitoring of model performance and data drift is essential for maintaining prediction accuracy in production. Google Cloud's Vertex AI Model Monitoring automatically detects skew and drift by comparing serving data against training data distributions, alerting you to degradation before it impacts business outcomes.

Answer

Manually tune hyperparameters for each retraining run.

Answer

Store all raw training data indefinitely for auditability.