PMLE Practice Test 7 — 10 Questions

Question 1

A data analyst wants to build a binary classification model to predict customer churn using SQL queries in BigQuery. Which BigQuery ML model type should they use?

Accepted Answer

LOGISTIC_REG. BigQuery ML supports LOGISTIC_REG for binary classification via SQL. LINEAR_REG is for regression, K_MEANS for clustering, and MATRIX_FACTORIZATION for recommendations.

Answer

MATRIX_FACTORIZATION

Answer

LINEAR_REG

Answer

K_MEANS

Question 2

A retail company deploys a new recommendation model alongside the current champion on Vertex AI Endpoints. They want to gradually shift traffic to the challenger while monitoring business metrics (conversion rate). Which two steps are required? (Choose 2)

Accepted Answer

Deploy the challenger model to the same endpoint as the champion with a separate deployed model.. Vertex AI Endpoints support deploying multiple model versions (champion and challenger) to the same endpoint, each as a separate deployed model. This allows the endpoint to serve both models simultaneously, enabling traffic splitting without requiring separate endpoints or infrastructure.

Answer

Use Vertex AI Experiments to track the traffic split percentages.

Answer

Enable Cloud Memorystore to cache identical requests for both models.

Answer

Use Cloud Monitoring to track custom metrics like conversion rate per model version.

Question 3

A data scientist deployed a classification model on Vertex AI Endpoints. After a week, the model's accuracy drops significantly from 92% to 78%. The data scientist suspects training-serving skew. What is the first step to confirm this?

Accepted Answer

Compare feature distributions between training and serving data using Vertex AI Model Monitoring. Vertex AI Model Monitoring provides a built-in capability to automatically detect training-serving skew by comparing feature distributions between the training data and the live serving data. This is the most direct and efficient first step to confirm whether the accuracy drop is due to a shift in the input data distribution, which is the hallmark of training-serving skew. The data scientist can set up monitoring jobs that compute statistical distance metrics (e.g., Jensen-Shannon divergence) and alert when significant deviations occur.

Answer

Look for data leakage in the training pipeline

Answer

Examine the feature importance of the model

Answer

Check the prediction confidence over time

Question 4

You are monitoring a machine learning pipeline that runs on Vertex AI Pipelines. The pipeline occasionally fails with a 'ResourceExhausted' error when attempting to read data from BigQuery. Which action should you take to resolve this issue?

Accepted Answer

Reduce the complexity of the BigQuery query or increase the reservation size. The 'ResourceExhausted' error when reading from BigQuery indicates that the query is consuming more resources than the BigQuery reservation allows. Option C is correct because reducing query complexity (e.g., using fewer JOINs, aggregations, or partitions) or increasing the reservation size directly addresses the root cause by either lowering resource demand or allocating more capacity. Other options like switching to Cloud Storage or adjusting pipeline memory do not fix the BigQuery-specific quota or slot exhaustion.

Answer

Switch from BigQuery to Cloud Storage for data source

Answer

Increase the memory allocated to the pipeline step

Answer

Reduce the batch size of the data being read

Question 5

A data science team collaborates using Vertex AI Workbench user-managed notebooks. They want to version control their notebook code and share it with team members. Which TWO tools should they use? (Choose 2)

Accepted Answer

Git integration in Vertex AI Workbench. Git integration in Workbench allows version control, and sharing via Cloud Source Repositories or GitHub provides collaboration.

Answer

Cloud Functions

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 6

A company needs to serve a model for low-frequency inference requests (a few hundred per month) from multiple regions. The priority is simplicity and minimal cost without maintaining infrastructure. Which serving option should they choose?

Accepted Answer

Use Vertex AI Batch Prediction triggered as needed.. Vertex AI Batch Prediction runs on-demand jobs without any idle infrastructure cost, making it ideal for low-frequency inference requests (a few hundred per month) from multiple regions. Option A is wrong because a real-time endpoint with min replicas=1 incurs per-hour cost even when idle. Option B is wrong because Dataflow streaming pipelines are designed for continuous, real-time data processing, which adds complexity and cost for infrequent requests. Option D is wrong because Cloud Run scales to zero but is still a real-time serving option designed for online inference, not batch; it also requires containerized application and is not optimized for batch workloads.

Answer

Deploy a real-time Vertex AI Endpoint with min replicas set to 1.

Answer

Set up a Dataflow streaming pipeline to process requests.

Answer

Use Cloud Run with serving container and scale to zero.

Question 7

An ML engineer is building a monitoring dashboard for a Vertex AI pipeline that includes training, evaluation, and batch prediction. Which THREE components should be included to provide comprehensive observability? (Select THREE.)

Accepted Answer

Pipeline execution status, duration, and failure rates for each component.. Pipeline execution status, duration, and failure rates are fundamental metrics for monitoring the health and performance of a Vertex AI pipeline. These metrics allow the ML engineer to quickly identify bottlenecks, track overall workflow progress, and detect failures in training, evaluation, or batch prediction steps, which is essential for comprehensive observability.

Answer

Compute engine CPU and memory logs for each pipeline step.

Answer

Online prediction latency and request count from the deployed model endpoint.

Question 8

Refer to the exhibit. A user is trying to upload a Vertex AI pipeline definition. The error indicates an invalid dependency order. What should the user do to fix this?

Accepted Answer

Reorder the tasks in the YAML so that task1 is defined before task2.. Vertex AI pipeline definitions require that tasks be declared in the order they appear in the dependency graph. The YAML parser validates the `dependentTasks` field by checking that referenced tasks are already defined. Defining `task1` before `task2` ensures that when `task2` declares a dependency on `task1`, `task1` is already in scope, resolving the invalid dependency order error.

Answer

Rename task1 to a name that comes alphabetically before task2.

Answer

Change the dependency of task2 to be independent of task1.

Answer

Remove the dependentTasks field from task2 and rely on implicit ordering.

Question 9

You are designing an ML pipeline for a large-scale recommendation system that runs weekly retraining on historical user interaction data. The pipeline uses TensorFlow and is deployed on Google Cloud. The pipeline must be orchestrated and automated with minimal manual intervention. Which THREE options should you include in your design? (Choose three.)

Accepted Answer

Use Vertex AI Pipelines to define the ML pipeline as a Directed Acyclic Graph (DAG) of components.. Vertex AI Pipelines (option B) is correct because it provides a managed, serverless orchestration service for building, testing, and deploying ML pipelines as Directed Acyclic Graphs (DAGs). This directly supports the requirement for automated, minimal-intervention weekly retraining by allowing you to define reusable components and schedule pipeline runs via Cloud Scheduler or event triggers, integrating natively with TensorFlow and Google Cloud services.

Answer

Use BigQuery scheduled queries to run the training script on a schedule.

Answer

Use AI Platform Notebooks to schedule the training job on a recurring basis.

Question 10

You have a very large language model that does not fit on a single GPU. You need to train it efficiently across multiple GPUs on a single machine. Which approach should you use?

Accepted Answer

Model parallelism using pipeline parallelism. When a model is too large for a single GPU, model parallelism (specifically pipeline parallelism) splits the model across GPUs. Data parallelism would require each GPU to hold the full model, which is not possible.

Answer

Data parallelism with MirroredStrategy

Answer

Data parallelism with MultiWorkerMirroredStrategy

Answer

Use TPU training as TPUs have more memory