PMLE Practice Test 12 — 15 Questions

Question 1

A marketing agency uses Vertex AI AutoML Vision to classify social media images into brand logos and generic content. They have 5,000 images per class. The model achieves 95% accuracy on validation set, but in production it misclassifies many images that contain logos in unusual angles or lighting. They have limited ML expertise and want to improve robustness. Which action should they take?

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

Accepted Answer

Augment the training set with images that have varied angles and lighting.. The core issue is a domain shift between the training data (likely clean, canonical logo images) and production data (logos at unusual angles and lighting). Augmenting the training set with those specific variations directly addresses the lack of robustness by exposing the model to the missing edge cases during training, which is the most effective and simplest fix for a team with limited ML expertise using AutoML Vision.

Answer

Switch to a custom CNN model trained with data augmentation.

Answer

Deploy the model with a lower confidence threshold.

Answer

Use Vertex AI Matching Engine for similarity search instead.

Question 2

A machine learning engineer is building a Vertex AI pipeline that uses a pre-built Google Cloud Pipeline Components (GCPC) to train a custom model. Which component should the engineer use to submit a custom training job to Vertex AI?

Accepted Answer

CustomJob. The CustomJob component is the correct choice because it is the pre-built GCPC component specifically designed to submit a custom training job to Vertex AI. It allows the engineer to specify a custom container image or a Python training script, along with machine configuration and hyperparameters, directly within a Vertex AI pipeline. Other components serve different purposes, such as hyperparameter tuning, batch predictions, or model deployment.

Answer

HyperparameterTuningJob

Answer

BatchPredictionJob

Answer

ModelDeploy

Question 3

Which TWO options are best practices for reducing model serving latency on Vertex AI Endpoints? (Choose two.)

Accepted Answer

Optimize the model using quantization or pruning. Option B is correct because quantization (e.g., reducing weights from FP32 to INT8) and pruning (removing redundant parameters) shrink the model size and reduce the compute required per inference, directly lowering prediction latency on Vertex AI Endpoints. Option C is correct because deploying the model in the same region as the clients minimizes network round-trip time, which is a significant component of end-to-end serving latency for online predictions. Option A is not a best practice for latency specifically: a larger machine with more memory may help with throughput or memory-bound models, but it does not inherently reduce per-request latency and increases cost. Option D is wrong because batch prediction is an asynchronous, offline mode that does not serve real-time requests and is not a latency optimization for online endpoints. Option E is not a supported Vertex AI Endpoint feature; there is no endpoint-level 'model caching' toggle that reduces serving latency.

Answer

Use a larger machine type with more memory

Answer

Use batch prediction instead of online prediction

Answer

Enable model caching at the endpoint

Question 4

A company uses Vertex AI AutoML to train a vision model, but the model has low accuracy. What should they do first?

Accepted Answer

Add more labeled images to the dataset. Adding more labeled images directly addresses the most common cause of low accuracy in AutoML vision models: insufficient or unrepresentative training data. Vertex AI AutoML relies on transfer learning from pre-trained models, and its performance is heavily dependent on the quality and quantity of labeled examples. Before adjusting hyperparameters or infrastructure, the first step should always be to improve the dataset, as AutoML is designed to handle model architecture and training budget automatically.

Answer

Switch to a custom model

Answer

Increase the training budget

Answer

Reduce image size to speed up training

Question 5

You are using DVC for data versioning in an ML project on Google Cloud. Your training data is stored in Cloud Storage. You want to track a new version of the dataset after preprocessing. Which DVC command should you use to register the changes?

Accepted Answer

dvc add data/processed. The `dvc add` command registers a file or directory with DVC, creating a .dvc metafile that captures the hash and metadata of the data, and adds the actual data to the DVC cache. After preprocessing produces a new dataset in data/processed, running `dvc add data/processed` creates the versioned pointer that DVC tracks in Git, which is exactly what 'registering the changes' means.

Answer

dvc push

Answer

dvc run -n preprocess

Answer

dvc commit

Question 6

An organization has multiple ML pipelines running on Vertex AI. They want to centralize monitoring and alerting for pipeline failures, including root cause analysis. Which combination of services should they use?

Accepted Answer

Cloud Logging + Cloud Monitoring + Error Reporting. Cloud Logging captures pipeline execution logs, Cloud Monitoring provides metrics and alerting on pipeline failures, and Error Reporting aggregates and analyzes errors with stack traces for root cause analysis. Together, they form a centralized observability stack that meets the requirement for monitoring, alerting, and root cause analysis of ML pipeline failures on Vertex AI.

Answer

Cloud Trace + Cloud Debugger

Answer

Cloud Operations for GKE + Stackdriver

Answer

Cloud Audit Logs + Cloud Functions

Question 7

A company needs to maintain an audit trail of model changes for compliance. Multiple teams will be updating models. What is the best approach to track who created, modified, or deployed each model version?

Accepted Answer

Use Vertex AI Experiments and Metadata to track model lineage and audit logs. Vertex AI Experiments and Metadata provide built-in lineage tracking that records which experiment, run, artifact, and model version were created, by which user, and with which parameters and metrics. Combined with Cloud Audit Logs for Vertex AI API calls, this gives a complete, queryable audit trail of who created, modified, or deployed each model version — exactly what compliance requires.

Answer

Enable Cloud Storage audit logs and require all model files to be stored in a bucket

Answer

Use Cloud Logging to collect logs from all services and search for model names

Answer

Ask team members to maintain a shared spreadsheet of changes

Question 8

Which THREE factors should be considered when choosing a compute option for serving a deep learning model in production on Google Cloud? (Choose three.)

Accepted Answer

Integration with Vertex AI for model monitoring. Option A is correct because integration with Vertex AI enables managed model monitoring, which is essential for detecting training-serving skew, drift, and performance degradation in production deep learning deployments. Option B is correct because autoscaling capabilities allow the serving infrastructure to dynamically adjust compute resources based on traffic patterns, ensuring cost efficiency and consistent latency under variable load. Option C is correct because deep learning inference often requires GPU or TPU acceleration to meet latency and throughput requirements, and the chosen compute option must support the necessary hardware accelerators. Option D is not a relevant factor because the programming language used during training does not constrain the production serving compute option, as models are typically exported to framework-agnostic formats or served via standard runtimes. Option E is clearly irrelevant because the team's logo color has no bearing on compute selection for model serving.

Answer

The programming language used for training

Answer

The color of the team's logo

Question 9

A data scientist deployed a TensorFlow model for sentiment analysis to Vertex AI Prediction. The model expects input key 'text' but the client sends requests with key 'review_text'. Which step should the data scientist take to resolve the error without retraining the model?

Accepted Answer

Modify the client code to send requests with input key 'text'. The most straightforward and reliable solution is to modify the client code to send the request with the expected input key 'text'. This avoids any additional infrastructure, latency, or complexity, and does not require retraining the model or altering the deployed endpoint. Vertex AI Prediction serves the model as-is, so aligning the client's request format with the model's expected input is the simplest and most maintainable fix.

Answer

Use a Cloud Function to strip the 'review_text' key and replace it with 'text'

Answer

Retrain the model with input key 'review_text'

Answer

Create a new Vertex AI Endpoint with an alias mapping 'review_text' to 'text'

Question 10

You are using Vertex AI Matching Engine for similarity search. Your index has 10 million embeddings of 512 dimensions. The query latency requirement is under 10ms for 99th percentile. Which index type should you choose?

Accepted Answer

Approximate Nearest Neighbor (ANN) index using the ScaNN algorithm.. The ScaNN (Scalable Nearest Neighbors) algorithm is specifically designed for high-dimensional, large-scale similarity search with strict latency requirements. With 10 million 512-dimensional embeddings, an ANN index like ScaNN can achieve sub-10ms query latency at the 99th percentile by trading a small amount of recall for dramatic speed improvements, which is exactly what Vertex AI Matching Engine optimizes for.

Answer

Brute-force index with cosine distance.

Answer

A custom distance-based index using Cloud SQL.

Answer

A tree-based index from scikit-learn deployed as a custom container.

Question 11

A team uses Vertex AI Feature Store for storing features. They want to share feature definitions with other teams in a collaborative manner. What is the best way to collaborate on feature definitions?

Accepted Answer

Use a shared repository with feature definition files and CI/CD to update the feature store.. Using a shared repository with feature definition files and CI/CD pipelines enables version control, peer review, and automated deployment to Vertex AI Feature Store. This approach ensures consistency, traceability, and collaboration without risking direct, uncoordinated changes to the production feature store.

Answer

Grant all teams write access to the same feature store so they can modify definitions directly.

Answer

Export the feature definitions as CSV and email them to the other teams.

Answer

Use a wiki page to document feature definitions and update it manually.

Question 12

Which Vertex AI service is designed for building and managing approximate nearest neighbor (ANN) indexes for similarity search at scale?

Accepted Answer

Vertex AI Matching Engine (Vector Search). Vertex AI Matching Engine (Vector Search) is specifically designed for building and managing approximate nearest neighbor (ANN) indexes for similarity search at scale. It allows you to create indexes from embeddings and perform low-latency similarity queries. This service is optimized for large-scale vector search use cases.

Answer

Vertex AI AutoML

Answer

Vertex AI Workbench

Answer

Vertex AI Prediction

Question 13

A large e-commerce company deploys a recommendation model on Vertex AI with autoscaling enabled. During Black Friday, traffic spikes rapidly. The autoscaler adds new instances, but new instances take several minutes to become ready (cold start). As a result, many requests time out. What should they do to mitigate this issue?

Accepted Answer

Set a higher minimum number of instances to handle the expected peak.. Setting a higher minimum number of instances ensures that a baseline capacity is always running and ready to serve traffic. This pre-warms instances, eliminating the cold-start latency during rapid traffic spikes, such as Black Friday, because new instances do not need to initialize from scratch.

Answer

Use a larger machine type to reduce the number of instances needed.

Answer

Configure the autoscaler to use CPU utilization metric instead of request count.

Answer

Increase the health check grace period for new instances.

Question 14

You are using Vertex AI Vector Search with an approximate nearest neighbor index. You need to update the index with new data every hour. The updates must be available for queries immediately. Which update method should you use?

Accepted Answer

Streaming updates using the streaming API.. Vertex AI Vector Search supports streaming updates via its streaming API, which allows you to insert, update, or delete vectors in real time. This ensures that new data is immediately available for approximate nearest neighbor (ANN) queries without requiring index recreation or redeployment, meeting the requirement for hourly updates with instant query availability.

Answer

Recreate the index every hour using a scheduled job.

Answer

Batch update by creating a new index and deploying it.

Answer

Use a brute-force index that supports real-time updates.

Question 15

An ML engineer needs to deploy a model to an endpoint and gradually shift traffic from the previous version (champion) to a new version (challenger) for A/B testing. How should they configure the endpoint?

Accepted Answer

Deploy both versions to the same endpoint and set traffic splitting. To gradually shift traffic between a champion and challenger model for A/B testing, the engineer should deploy both versions to the same endpoint and configure traffic splitting. This allows the endpoint to route a percentage of requests to each version, enabling controlled experimentation and rollback. This is the standard pattern for A/B testing on managed ML platforms.

Answer

Use a canary deployment with Cloud Run

Answer

Manually update the endpoint to point to the challenger after testing

Answer

Create a new endpoint for the challenger and route traffic via load balancer