PMLE Practice Test 16 — 15 Questions

Question 1

An ML engineer is building a continuous training pipeline that retrains a model when new data arrives. The pipeline should also detect skew between training and serving data. Which TWO Google Cloud services should they use? (Choose two.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring (B) is correct because it is purpose-built to detect skew between training and serving data by continuously comparing feature distributions and alerting on statistically significant drift. Vertex AI Pipelines (D) is correct because it provides a serverless, scalable orchestration service for building continuous training pipelines that automatically retrain models when new data arrives, integrating with Cloud Build and other services.

Answer

Cloud Logging

Answer

Cloud Functions

Answer

Cloud Monitoring

Question 2

A company uses a Cloud Composer DAG to run a daily ML pipeline that includes Dataflow jobs and model training on Vertex AI. The pipeline frequently fails due to insufficient permissions when the Dataflow worker accesses data in Cloud Storage. What is the most efficient way to resolve this issue?

Accepted Answer

Create a custom service account with required permissions and assign it to the Dataflow job.. The most efficient way to resolve insufficient permissions for Dataflow workers accessing Cloud Storage is to create a custom service account with the required roles (e.g., roles/storage.objectViewer) and assign it to the Dataflow job via the --serviceAccount option. This follows the principle of least privilege and ensures that only the Dataflow workers have the necessary permissions, without affecting other pipeline components or exposing the bucket publicly.

Answer

Grant the 'roles/storage.objectViewer' role to 'allUsers' on the Cloud Storage bucket.

Answer

Use the Composer environment's service account for all pipeline components.

Answer

Move the Dataflow job to run after the pipeline so that data is already processed.

Question 3

A data engineer needs to version large datasets (multiple TB) in a Data Lake on Google Cloud. They require ACID transactions to ensure consistency when multiple jobs read/write concurrently. Which solution should they use?

Accepted Answer

Delta Lake on Dataproc. Delta Lake on Dataproc provides ACID transactions on cloud storage data lakes, enabling concurrent reads/writes with consistency.

Answer

BigQuery table snapshots

Answer

DVC (Data Version Control)

Answer

Vertex AI Feature Store

Question 4

An ML engineer trained a model and registered it in Vertex AI Model Registry. They want to assign the alias 'champion' to the best-performing version for production deployment. Which gcloud command should they use?

Accepted Answer

gcloud ai models versions update --model=MODEL_ID --version=VERSION_ID --update-aliases=champion. The correct command is 'gcloud ai models versions update' with the --update-aliases flag. This command updates a specific model version's aliases, allowing you to assign or remove aliases like 'champion' to denote the best-performing version for production. Option A only describes a version, option B uploads a new model, and option C deploys a model to an endpoint, none of which assign aliases to a version.

Answer

gcloud ai models versions describe --model=MODEL_ID --version=VERSION_ID

Answer

gcloud ai models upload --model-id=MODEL_ID --display-name=champion

Answer

gcloud ai endpoints deploy-model --model=MODEL_ID --alias=champion

Question 5

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 6

A company uses Vertex AI Pipelines to train and deploy models. They want to automatically generate model documentation that includes model details, intended use, and evaluation results. What should they use?

Accepted Answer

Model Cards. Model Cards are a standardized format for model documentation, and Vertex AI supports automated generation of model cards.

Answer

Vertex AI Explanations

Answer

Vertex AI Metadata

Answer

Vertex AI Model Registry with custom metadata

Question 7

A data science team needs to share features across multiple ML models while ensuring consistency between training and serving. Which approach best achieves this?

Accepted Answer

Use Vertex AI Feature Store to define and serve features for both training and online prediction. Vertex AI Feature Store provides a central repository where features are defined once and reused across models, reducing training-serving skew.

Answer

Store features in a shared BigQuery dataset without versioning

Answer

Export features to CSV files shared via Cloud Storage

Answer

Each team maintains its own feature engineering code in separate pipelines

Question 8

An organization wants to implement central governance for ML models across teams. Which TWO services should they use together to achieve model versioning, lineage, and deployment management? (Select 2)

Accepted Answer

Vertex AI Model Registry. Vertex AI Model Registry manages model versions and aliases; Vertex AI Metadata tracks lineage.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Cloud Data Catalog

Question 9

An engineer needs to deploy multiple models on a single Vertex AI endpoint with separate traffic allocations. What is the maximum number of deployed models that can be assigned traffic on one endpoint?

Accepted Answer

5. Vertex AI endpoints allow up to 5 deployed models to receive traffic simultaneously, with each model assigned a traffic percentage that sums to 100%. This limit ensures predictable routing and resource management, preventing overcommitment of the endpoint's underlying infrastructure.

Answer

2

Answer

Unlimited

Answer

10

Question 10

A startup has developed a prototype ML model using scikit-learn on a single machine. They now need to scale it to handle larger datasets and deploy it for real-time predictions. The team is small and wants minimal operational overhead. Which Google Cloud service should they use?

Accepted Answer

Vertex AI. Vertex AI (option B) is the correct choice because it provides a unified, fully managed MLOps platform that integrates model training, deployment, and scaling with minimal operational overhead. It supports scikit-learn models natively, offers auto-scaling for real-time predictions, and eliminates the need to manage infrastructure, making it ideal for a small team transitioning from a prototype.

Answer

AI Platform Prediction

Answer

Cloud Functions

Answer

Compute Engine with TensorFlow Serving

Question 11

A company wants to analyze videos to detect objects and track their movement over time. Which TWO Google Cloud services are suitable for this task?

Accepted Answer

AutoML Video. AutoML Video supports object tracking, and Video Intelligence API provides both object detection and tracking. AutoML Vision is for images only, Natural Language for text, and Speech-to-Text for audio.

Answer

AutoML Vision

Answer

Speech-to-Text

Answer

Natural Language API

Question 12

You are configuring a Vertex AI Feature Store online store for a real-time recommendation system that requires single-digit millisecond latency and high throughput. The feature values are updated frequently. Which online store type should you use?

Accepted Answer

Optimized online store. The optimized online store (backed by Cloud Bigtable) is designed for high throughput, low latency, and frequent updates, making it suitable for real-time systems. Bigtable online store is the legacy option with similar performance but higher cost and manual scaling.

Answer

Spanner online store

Answer

Bigtable online store

Answer

Firestore online store

Question 13

Which TWO actions are appropriate when you detect that a production model's prediction distribution has shifted significantly from the training distribution?

Accepted Answer

Retrain the model using the most recent data. Retraining the model on the most recent data directly addresses the distribution shift by adapting the model to the new data patterns. This is a standard practice in MLOps when the shift is confirmed and the cause is understood, ensuring the model remains accurate and reliable in production.

Answer

Immediately roll back to the previous model version

Answer

Increase logging for future predictions

Answer

Reduce the traffic to the model to minimize impact

Question 14

A developer needs to transcribe phone calls with high accuracy for a call center analytics application. The audio is in English and has background noise. Which Speech-to-Text model should they choose?

Accepted Answer

telephony. The telephony model is optimized for phone call audio with background noise. latest_long is for longer audio (without phone-specific optimization), and latest_short is for short utterances.

Answer

latest_short

Answer

latest_long

Answer

Any model; they are all equivalent

Question 15

A company wants to analyze customer reviews for sentiment (positive, negative, neutral) using a pre-trained model with no training. They have text data stored in BigQuery. Which Google Cloud service should they use?

Accepted Answer

Natural Language API. Natural Language API provides pre-trained sentiment analysis. AutoML NLP is for custom models. Speech-to-Text is for audio transcription. Translation API is for language translation.

Answer

Translation API

Answer

Speech-to-Text API

Answer

AutoML NLP