PMLE Practice Test 31 — 15 Questions

Question 1

A data scientist needs to scale a prototype deep learning model to train on a massive dataset using multiple GPUs. Which three strategies are essential for efficient distributed training? (Select THREE)

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

Accepted Answer

Implement data parallelism.. The correct strategies are B (data parallelism), C (optimized input pipeline with tf.data.Dataset), and D (synchronous gradient updates). Data parallelism is the foundation for distributing training across multiple GPUs. Synchronous gradient updates ensure consistency and convergence. An optimized input pipeline prevents I/O bottlenecks. Option A (single large batch size) is not essential, and option E (asynchronous updates) can lead to poor convergence.

Answer

Use a single large batch size across all workers.

Answer

Use asynchronous gradient updates to reduce communication overhead.

Question 2

Which THREE components should you include in a comprehensive model monitoring dashboard for a production ML system?

Accepted Answer

System resource utilization (CPU, memory, latency). System resource utilization metrics (CPU, memory, latency) are essential for monitoring the health and performance of the production infrastructure hosting the ML model. These metrics help detect resource bottlenecks, scaling issues, or degradation that could impact inference latency and throughput, which are critical for maintaining service-level objectives (SLOs).

Answer

Team member roles and responsibilities

Answer

Training pipeline code version

Question 3

A team uses Vertex AI Feature Store with an online store for real-time predictions. They notice that the online store queries are taking longer than expected. Which TWO actions could improve online store performance? (Choose 2)

Accepted Answer

Reduce the number of features served by the online store.. For Bigtable online store, increasing nodes improves throughput; for optimized online store, decreasing number of features reduces load. Caching is not a built-in feature.

Answer

Use the offline store for serving predictions.

Answer

Switch from Bigtable to Firestore online store.

Answer

Enable caching on the online store.

Question 4

An ML engineer notices that predictions are taking longer than expected under moderate traffic. Reviewing the endpoint configuration, what is the most likely cause of the high latency?

Accepted Answer

The accelerator count is 0, meaning no GPU is used.. When the accelerator count is set to 0, the endpoint runs inference on the CPU only, which is significantly slower than GPU-accelerated inference for deep learning models. This is the most direct cause of high latency under moderate traffic, as the model's compute demands exceed CPU throughput.

Answer

Container logging is disabled, slowing down request processing.

Answer

The machine type n1-standard-4 is underpowered for the model's compute needs.

Answer

Automatic scaling is set with a maxReplicaCount of 10, which creates overhead.

Question 5

A machine learning engineer has a Vertex AI pipeline that trains a model. The pipeline uses caching to avoid re-running components that have not changed. After updating the training code, the engineer notices that the pipeline still uses cached outputs from the previous run. What could be the reason?

Accepted Answer

The base image used for the component has not changed, so the cache key matches despite code changes inside the container.. In Vertex AI Pipelines, caching uses a cache key derived from the component source code, input parameters, and the base image digest. If only the training code inside the container changes but the base image digest remains the same, the cache key does not change, resulting in a cache hit from the previous run. This explains why the pipeline still uses cached outputs. Option A is incorrect because changing parameter values would change the cache key, causing a miss. Option B is incorrect because pre-built components support caching unless explicitly disabled. Option D would disable caching entirely, which is not the case here.

Answer

The pipeline parameter values have changed, causing a cache hit.

Answer

The pipeline is using a pre-built component that ignores caching.

Answer

The component has caching disabled via the @dsl.component decorator.

Question 6

Which TWO factors should you consider when choosing between BigQuery and Cloud Storage for storing training data? (Choose 2)

Accepted Answer

The format of the data: structured vs. unstructured.. BigQuery is optimized for structured, tabular data (e.g., CSV, Avro, Parquet) and supports SQL queries, while Cloud Storage is a better fit for unstructured data (e.g., images, videos, raw text files) that does not require schema enforcement. Choosing the right storage depends on whether the training data has a fixed schema and requires relational querying or is blob-based and needs high-throughput access.

Answer

The requirement for data encryption at rest.

Answer

The need for fine-grained access control at the row level.

Answer

The maximum size of the dataset (BigQuery limit 1 TB).

Question 7

A financial institution wants to use Natural Language API for sentiment analysis on customer feedback, but the domain-specific language (e.g., 'bullish', 'bearish') is not correctly classified. They have 200 labeled examples. Which approach minimizes coding effort while improving accuracy?

Accepted Answer

Use AutoML Natural Language to train a custom model. AutoML Natural Language enables you to train a custom model on your 200 labeled examples without writing code, directly improving accuracy for domain-specific terms like 'bullish' and 'bearish'. This approach leverages transfer learning from Google's pre-trained models, minimizing coding effort while adapting to your unique vocabulary and sentiment patterns.

Answer

Submit a feature request to Google for domain-specific terms

Answer

Create a custom sentiment dictionary and pass it to the Natural Language API

Answer

Build a custom TensorFlow model for sentiment

Question 8

Which TWO are best practices for building ML pipelines on Vertex AI Pipelines?

Accepted Answer

Use a container-based approach for each component. Vertex AI Pipelines is designed to run container-based components, where each step in the pipeline is a Docker container that encapsulates its dependencies and execution logic. This approach ensures reproducibility, isolation, and scalability, aligning with best practices for ML pipelines on Vertex AI.

Answer

Store all trained models in Cloud Storage without versioning

Answer

Use Cloud Build as the pipeline orchestrator

Answer

Use Cloud Composer as the primary pipeline tool

Question 9

A team wants to share a trained model with another team who will deploy it to a different Google Cloud project. Which is the recommended way to transfer the model?

Accepted Answer

Use Vertex AI Model Registry with cross-project IAM permissions to allow the second project to access the model.. Vertex AI Model Registry supports cross-project access via IAM permissions, allowing the second project to directly deploy the model without copying artifacts. This approach maintains a single source of truth, avoids data duplication, and leverages Vertex AI's built-in versioning and lineage tracking. It is the recommended pattern for sharing models across projects in Google Cloud.

Answer

Copy the model artifact from one project's Cloud Storage to another using gsutil.

Answer

Export the model as a SavedModel, store in a shared Cloud Storage bucket, and import into the second project.

Answer

Package the model in a Docker container and push to a cross-project Container Registry.

Answer

Use Cloud Marketplace to publish the model.

Question 10

Drag and drop the steps to set up a BigQuery ML linear regression model for forecasting in the correct order.

Accepted Answer

Prepare training data → Create model → Evaluate model → Use model for predictions. For BigQuery ML linear regression, the correct order is: 1) Prepare training data by selecting and preprocessing features in a SQL query; 2) Create the model using `CREATE MODEL` with the training data; 3) Evaluate the model using `ML.EVALUATE` to check metrics like R²; 4) Use the model for predictions with `ML.PREDICT`. This sequence ensures the model is built on clean data, validated, and then applied.

Answer

Prepare training data → Create model → Use model for predictions → Evaluate model

Answer

Create model → Prepare training data → Evaluate model → Use model for predictions

Answer

Prepare training data → Evaluate model → Create model → Use model for predictions

Question 11

Match each Google Cloud storage option to its best use case.

Accepted Answer

Cloud Storage: Storing unstructured data like images, videos, and documents. Correct matches: A (Cloud Storage for unstructured data), C (Bigtable for low-latency analytical workloads), E (BigQuery for serverless data warehousing). Incorrect options: B (Cloud Storage lacks transactional consistency for structured data), D (Bigtable is NoSQL, not relational), F (BigQuery is for analytical queries, not real-time transaction processing).

Answer

Cloud Storage: Storing structured transactional data with strong consistency

Answer

Bigtable: Storing relational data with complex joins and transactions

Answer

BigQuery: Real-time transaction processing for small, frequently updated datasets

Question 12

Match each optimization algorithm to its characteristic.

Accepted Answer

SGD: Uses a constant learning rate for all parameters. The correct matches are: A (SGD uses a constant learning rate for all parameters), B (Adam combines momentum and RMSProp with bias correction), and C (RMSProp uses exponential moving average of squared gradients). Option D is incorrect because Adam does not use only momentum; it also includes adaptive learning rates. Option E is incorrect because SGD uses a fixed learning rate, not adaptive.

Answer

Adam: Uses only momentum

Answer

SGD: Uses adaptive learning rate

Question 13

You are A/B testing a new model version (challenger) against the current version (champion) on Vertex AI. You want to gradually shift traffic from champion to challenger while measuring business metrics. Which approach should you use?

Accepted Answer

Deploy both models to the same endpoint and use the traffic split feature to allocate percentages.. Vertex AI endpoints support traffic splitting between deployed models. By updating the traffic percentage, you can gradually shift traffic and monitor performance.

Answer

Deploy the challenger to a separate endpoint and use a load balancer to route a percentage of requests.

Answer

Use Cloud Armor to route traffic based on headers.

Answer

Create a new endpoint for the challenger and gradually shift DNS records.

Question 14

An ML engineer is building a pipeline component that takes a dataset URI and a model URI as inputs, and outputs a classification metrics artifact. Which KFP SDK v2 type should the output artifact be annotated with?

Accepted Answer

ClassificationMetrics. In KFP SDK v2, the `ClassificationMetrics` type is specifically designed to output classification metrics such as confusion matrix, ROC curve, and AUC. The question asks for a component that outputs classification metrics, so `ClassificationMetrics` is the correct artifact type. Using `Metrics` would be too generic and not provide the structured schema needed for classification-specific visualizations in the KFP UI.

Answer

Dataset

Answer

Metrics

Answer

Model

Question 15

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