PMLE Practice Test 4 — 25 Questions

Question 1

A company is deploying a machine learning model for real-time inference on Vertex AI. Which TWO practices improve serving performance and reliability?

Accepted Answer

Enable autoscaling to handle traffic variations.. Vertex AI's autoscaling dynamically adjusts the number of replicas based on incoming request traffic, ensuring low latency during spikes and cost savings during lulls. This is critical for real-time inference, where consistent response times are required and manual scaling would either over-provision or under-provision resources. Autoscaling uses metrics like CPU utilization or request count to scale up or down, directly improving serving performance and reliability.

Answer

Use batch prediction for all requests.

Answer

Use manual scaling with a fixed number of replicas.

Answer

Deploy all models on the same machine type for consistency.

Question 2

A company serves a PyTorch model using a custom container on Vertex AI Prediction. They notice that after a few hours, the endpoint returns 502 errors. The logs show 'Out of memory' errors. The container has a memory limit of 4GB, and the model loads a 3GB vocabulary file. What is the most likely cause and best fix?

Accepted Answer

Load the vocabulary file once at startup and reuse it.. The 502 errors and 'Out of memory' errors indicate that the container is running out of memory during inference. Since the model loads a 3GB vocabulary file, and the container has only 4GB of memory, loading this file repeatedly for each prediction request (e.g., inside the prediction handler) would quickly exhaust memory. The correct fix is to load the vocabulary file once at container startup and reuse it across all requests, which is a standard best practice for serving models with large static assets.

Answer

Increase the container memory to 8GB.

Answer

Increase the number of replicas to distribute load.

Answer

Switch to Vertex AI Batch Prediction.

Question 3

A data scientist has deployed a classification model on a Vertex AI Endpoint and wants to monitor for feature drift in the serving data compared to the training data. Which Vertex AI service should be used?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the purpose-built service that detects training-serving skew and prediction drift by comparing live serving data distributions against a baseline (typically the training dataset). It computes statistics like Jensen-Shannon divergence and can alert when drift exceeds configured thresholds. Explainable AI, Model Evaluation, and Continuous Training serve different purposes and do not provide ongoing drift detection on deployed endpoints.

Answer

Vertex AI Explainable AI

Answer

Vertex AI Model Evaluation

Answer

Vertex AI Continuous Training

Question 4

A team of ML engineers is collaborating on a project using Vertex AI. They want to ensure that only approved models are deployed to production. Which approach should they use?

Accepted Answer

Use Vertex AI Model Registry with version aliases to manage model versions and promote them after approval.. Vertex AI Model Registry provides a centralized repository for managing model versions, with support for version aliases (e.g., 'champion', 'challenger') that allow teams to promote models to production only after approval. This ensures governance and traceability, meeting the requirement that only approved models are deployed.

Answer

Store all models in a Cloud Storage bucket and manually control access via IAM permissions.

Answer

Deploy models directly from training jobs to an endpoint without version tracking.

Answer

Use Cloud Dataflow to transform raw predictions and then store them in BigQuery for analysis.

Question 5

A company wants to monitor fairness of a model by evaluating performance metrics across demographic subgroups. They have ground truth labels stored in BigQuery. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Model Evaluation. Vertex AI Model Evaluation is the service designed to assess model performance using ground truth labels, including slicing metrics across demographic subgroups to detect fairness issues. It computes metrics like precision, recall, and AUC per slice, directly supporting bias and fairness analysis. This makes it the correct choice when labels are available in BigQuery.

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Prediction

Answer

Vertex AI Explainability

Question 6

A company wants to build a product recommendation engine for their e-commerce website. They have historical purchase data and user interaction logs. They want a managed service that can quickly generate personalized recommendations without building custom models. Which service should they use?

Accepted Answer

Recommendations AI. Recommendations AI is a fully managed Google Cloud service purpose-built for personalized product recommendations, ingesting purchase and interaction data and serving recommendations via API without custom model development. It handles training, tuning, and serving, which matches the requirement for a managed service that quickly generates personalized recommendations.

Answer

Dataflow with TensorFlow

Answer

BigQuery ML with MATRIX_FACTORIZATION

Answer

AutoML Tables

Question 7

An ML engineer is using Vertex AI Vizier to tune hyperparameters for a PyTorch model. They want to maximise the chance of finding the global optimum within a fixed trial budget of 50 trials. Which algorithm should they select?

Accepted Answer

Bayesian optimisation. Bayesian optimisation (option B) is the correct choice because it builds a probabilistic surrogate model of the objective function and uses an acquisition function to balance exploration and exploitation, making it highly sample-efficient. With only 50 trials, Bayesian optimisation maximises the probability of finding the global optimum by focusing trials on the most promising hyperparameter regions, unlike random or grid search which waste trials on unpromising areas.

Answer

Random search

Answer

Grid search

Answer

Evolutionary algorithm

Question 8

A company uses BigQuery ML to train a boosted tree classifier on a large dataset. After training, they want to understand which features most influence predictions. Which BigQuery ML function should they use?

Accepted Answer

ML.FEATURE_IMPORTANCE. ML.FEATURE_IMPORTANCE is the BigQuery ML function specifically designed to return the relative importance of each input feature for a trained model, including boosted tree classifiers (Boosted Tree, XGBoost, Random Forest). It computes importance scores using the model's internal split-gain or weight-based metrics, giving a ranked list of features that most influence predictions. This is the direct, purpose-built answer for global feature attribution on a trained model.

Answer

ML.EXPLAIN_PREDICT

Answer

ML.EVALUATE

Answer

ML.PREDICT

Question 9

A team wants to track the lineage of ML pipeline runs, including which datasets, parameters, and models were used in each execution. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata (part of Vertex ML Metadata) is the service designed to record and query ML metadata, including artifacts (datasets, models), executions (pipeline runs), and events, forming a lineage graph. It captures which datasets, parameters, and models were used in each pipeline execution, enabling reproducibility and auditability. This directly matches the requirement to track lineage of ML pipeline runs.

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 10

An ML team is optimizing an inference model for deployment on edge devices. They need to reduce the model size and improve latency while maintaining accuracy as much as possible. Which two techniques should they use? (Choose TWO.)

Accepted Answer

Post-training quantization to INT8.. Post-training quantization to INT8 reduces model size by converting 32-bit floating-point weights and activations to 8-bit integers, which also speeds up inference on edge devices with integer-optimized hardware. This technique typically maintains accuracy within 1-2% of the original model while significantly lowering memory footprint and latency.

Answer

Use a larger pre-trained model as a starting point.

Answer

Use half-precision (FP16) instead of INT8.

Answer

Increase the number of layers in the model.

Question 11

A company wants to use ML to predict customer churn. They have user activity logs in Cloud Storage, account data in BigQuery, and want an automated pipeline. Which pipeline architecture on Google Cloud should they use?

Accepted Answer

Use BigQuery to join logs and account data, train on Vertex AI, deploy to an endpoint. It leverages BigQuery's ability to join structured account data with semi-structured logs (via federated queries or external tables), then uses Vertex AI for end-to-end ML training and deployment. This architecture minimizes data movement, keeps the pipeline serverless, and directly addresses the requirement for an automated pipeline with both data sources.

Answer

Load both data sources into AutoML Tables and train directly

Answer

Export logs from Cloud Storage to Cloud Dataproc for preprocessing, then train

Answer

Use Cloud Functions to preprocess data, then train on AI Platform

Question 12

The exhibit shows part of a Vertex AI Pipeline definition. The pipeline fails at the training step with an error: 'Missing required input: train_data'. What is the most likely cause?

Accepted Answer

The data_processing step does not define any outputs. The error 'Missing required input: train_data' indicates that the training step expects an input artifact named 'train_data', but no upstream step provides it. In Vertex AI Pipelines, a component's output must be explicitly defined and connected to the downstream component's input. Since the data_processing step does not define any outputs, it cannot produce the 'train_data' artifact, causing the training step to fail.

Answer

The evaluation step expects a metric output but training does not produce it

Answer

The training step uses the wrong image tag

Answer

The container command for data_processing is incorrect

Answer

The pipeline is missing a deployment step

Question 13

Which TWO practices are important when scaling a prototype ML model to production on Google Cloud? (Choose two.)

Accepted Answer

Set up model monitoring for data drift and concept drift. Option A is correct because production ML models degrade as input data distributions shift (data drift) and as the relationship between features and labels changes (concept drift), so Vertex AI Model Monitoring must track these and trigger retraining or alerts. Option E is correct because scaling to production requires reproducible, automated MLOps: CI/CD pipelines (e.g., Cloud Build, Vertex AI Pipelines) automate training, evaluation, and deployment, ensuring consistency and fast iteration. Option B is wrong because manual feature engineering per iteration does not scale and should be replaced by automated feature pipelines (e.g., Vertex AI Feature Store). Option C is wrong because a single high-memory Compute Engine VM is a non-scalable, non-resilient deployment; production serving should use managed, horizontally scalable endpoints like Vertex AI Prediction. Option D is wrong because proprietary, lock-in-heavy libraries hinder portability, maintainability, and integration with Google Cloud's managed ML services.

Answer

Manually engineer features for each training iteration

Answer

Run the model on a single high-memory Compute Engine VM

Answer

Use proprietary libraries to maximize performance regardless of lock-in

Question 14

An ML team wants to automatically track training runs, including hyperparameters and metrics, with minimal code changes. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Experiments with autologging. Vertex AI Experiments with autologging automatically captures parameters, metrics, and artifacts from training runs with minimal code changes—often just a single call to initialize and start a run. This directly satisfies the requirement to track training runs with minimal code. It integrates with the Vertex AI SDK to log framework-specific details (e.g., TensorFlow, PyTorch) automatically.

Answer

Vertex AI Prediction

Answer

Vertex AI Workbench

Answer

Vertex AI Metadata

Question 15

A team needs to quickly create a visual interface for data exploration and model building without writing code. They want to run AutoML jobs and visualize results. Which Google Cloud tool should they use?

Accepted Answer

Vertex AI Workbench. Vertex AI Workbench provides a managed JupyterLab environment with a low-code interface for data exploration, AutoML model training, and result visualization without writing code. It integrates directly with Vertex AI's AutoML and custom training services, allowing users to run AutoML jobs and view evaluation metrics, feature importance, and predictions through its UI.

Answer

Cloud Datalab

Answer

Cloud Composer

Answer

Google Colab

Question 16

A data scientist is creating a Vertex AI pipeline using the Kubeflow Pipelines SDK v2. Which TWO statements about pipeline parameters are correct? (Choose two.)

Accepted Answer

Pipeline parameters are defined as inputs to the pipeline function decorated with @dsl.pipeline.. Option A is correct because in the Kubeflow Pipelines SDK v2, pipeline parameters are declared as typed arguments of the function decorated with @dsl.pipeline, which defines the pipeline's input interface. Option D is correct because these parameters are runtime inputs: when submitting a run (e.g., via the Vertex AI Pipelines API or the SDK's create_run_from_pipeline_func), you can supply new values that override the defaults specified in the pipeline definition. Option B is wrong because KFP v2 handles parameter serialization automatically; you do not manually JSON-serialize parameters before use. Option C is wrong because KFP v2 parameters support multiple types such as int, float, bool, str, list, and dict, not only str. Option E is wrong because parameters are intended for small scalar/structured configuration values; large datasets should be passed between components as artifacts (e.g., Dataset, Model), not as parameters.

Answer

Pipeline parameters must be serialized to JSON before use.

Answer

Pipeline parameters can only be of type str.

Answer

Pipeline parameters can be used to pass large datasets between components.

Question 17

Drag and drop the steps to perform a hyperparameter tuning job on Vertex AI in the correct order.

Accepted Answer

Step 1: Define the search space. Step 2: Create and run the tuning job. Step 3: Monitor the job. Step 4: Select the best parameters.. Define the search space, then create and run the tuning job, monitor, and select the best parameters.

Answer

Step 1: Create and run the tuning job. Step 2: Define the search space. Step 3: Monitor the job. Step 4: Select the best parameters.

Answer

Step 1: Define the search space. Step 2: Create and run the tuning job. Step 3: Select the best parameters. Step 4: Monitor the job.

Answer

Step 1: Monitor the job. Step 2: Define the search space. Step 3: Create and run the tuning job. Step 4: Select the best parameters.

Question 18

You have a Vertex AI endpoint that serves a model for real-time predictions. You want to update the model to a new version with zero downtime. Which approach should you take?

Accepted Answer

Deploy the new model version to the same endpoint and then set traffic to 100% for the new version.. Vertex AI endpoints support canary deployments by allowing you to deploy a new model version to the same endpoint and then gradually shift traffic to it using the `traffic_split` parameter. Setting traffic to 100% for the new version after deployment ensures zero downtime, as the endpoint remains active and serves requests from the old version until the switch is complete.

Answer

Delete the endpoint and recreate it with the new model.

Answer

Use Cloud Load Balancing to switch traffic between two endpoints.

Answer

Create a new endpoint and update the client application to point to the new endpoint.

Question 19

A company wants to log all prediction requests and responses from a Vertex AI Endpoint to BigQuery for auditing and debugging. How can they achieve this?

Accepted Answer

Enable request/response logging on the endpoint and create a BigQuery sink for the log.. Vertex AI endpoints can be configured to enable request/response logging. The logs can be sent to a BigQuery table via a log sink.

Answer

Export endpoint logs from Cloud Logging to Cloud Storage and then load into BigQuery manually.

Answer

Use a Cloud Function to intercept predictions and write to BigQuery.

Answer

Vertex AI endpoints do not support request/response logging.

Question 20

A startup wants to build a product recommendation engine without writing custom training code. They have user-item interaction data stored in BigQuery. Which Google Cloud service should they use?

Accepted Answer

BigQuery ML matrix factorization. BigQuery ML matrix factorization is the correct choice because it allows building a recommendation engine directly in BigQuery using SQL, without writing custom training code. It supports implicit and explicit user-item interaction data and provides built-in evaluation metrics, making it ideal for low-code ML solutions on existing BigQuery data.

Answer

Cloud Dataflow with ML APIs

Answer

Vertex AI AutoML Tables

Answer

Vertex AI Matching Engine