PMLE Practice Test 10 — 10 Questions

Question 1

A large organization uses a multi-project setup with a central data lake. Different teams manage their own models. To enable cross-team sharing of features, they want to use Vertex AI Feature Store. What is the best practice to manage access?

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

Accepted Answer

Create a single Feature Store in a central project and grant fine-grained IAM roles. Creating a single Feature Store in a central project with fine-grained IAM roles is the best practice because it centralizes feature management while allowing cross-team access control at the feature group or feature level. Vertex AI Feature Store supports IAM roles like `aiplatform.featureStoreAdmin` and `aiplatform.featureStoreDataViewer` to grant granular permissions, enabling teams to share features without duplicating data or exposing sensitive information. This approach avoids data silos and ensures consistent governance across the organization.

Answer

Export features to Cloud Storage

Answer

Create separate Feature Stores per team project

Answer

Use BigQuery authorized views

Question 2

A large enterprise has multiple ML models deployed in production across different regions. They want to implement a centralized monitoring dashboard that tracks key performance indicators such as prediction accuracy, latency, and error rates for all models, with the ability to drill down into individual model versions. Which approach best meets these requirements?

Accepted Answer

Use Cloud Monitoring with custom metrics reported by each model deployment, and create a unified dashboard with filterable resources. Cloud Monitoring with custom metrics allows each model deployment to report key performance indicators (e.g., prediction accuracy, latency, error rates) as metric time series. These custom metrics can be aggregated into a single unified dashboard, and the dashboard can be configured with filterable resources (e.g., region, model version) to enable drill-down into individual model versions. This approach provides centralized, real-time monitoring without relying on log-based or batch analytics.

Answer

Use Vertex AI Experiments to log metrics and compare across runs

Answer

Use Cloud Logging to search logs from each model and create a dashboard

Answer

Use BigQuery to store prediction logs and then visualize in Looker

Question 3

An engineer needs to perform sentiment analysis on customer reviews. They have a large volume of text and need a solution that requires minimal customisation. Which option is most efficient?

Accepted Answer

Use the Natural Language API. The Natural Language API provides pre-built sentiment analysis with minimal setup. AutoML NLP would require custom training, BigQuery ML is for tabular data, and Vertex AI Prediction needs a deployed model.

Answer

Use Vertex AI Prediction with a pre-trained model

Answer

Use BigQuery ML with LOGISTIC_REG

Answer

Train a custom model using AutoML NLP

Question 4

A company uses Cloud Composer to orchestrate their ML pipelines. They notice that tasks are being queued but not executed, causing delays. What is the most likely cause?

Accepted Answer

The Airflow worker resources are exhausted. When tasks are queued but not executed, it typically indicates that the Airflow workers have no available slots to pick up new tasks. In Cloud Composer, the Celery executor distributes tasks to workers; if all worker concurrency slots are saturated or the worker node pool is under-provisioned, tasks remain in the 'queued' state until a worker becomes free. This is the most likely cause given the symptom of tasks being queued without execution.

Answer

The Airflow web server is down

Answer

The DAG file is corrupted

Answer

The Cloud Storage bucket containing DAGs is not accessible

Question 5

An ML engineer is using Vertex AI Pipelines and wants to reuse a trained model across multiple pipeline runs without retraining each time. Which artifact management strategy should be used?

Accepted Answer

Use Vertex AI ML Metadata to track and retrieve model artifacts. Vertex AI ML Metadata is the correct artifact management strategy because it is purpose-built for tracking and retrieving model artifacts across pipeline runs. It stores metadata about models, datasets, and other artifacts in a lineage graph, enabling you to query and reuse a specific model version without retraining. This integrates natively with Vertex AI Pipelines, allowing you to pass model artifacts between components and retrieve them by ID or custom properties.

Answer

Store the model in BigQuery as a ML model

Answer

Use Cloud Functions to cache the model

Answer

Save the model to a Cloud Storage bucket and reference by path

Question 6

A machine learning engineer is preparing to train a Transformer-based model using TensorFlow on a single TPU v3-8 pod slice. The training script uses tf.distribute.TPUStrategy. Which environment variable must be set in Vertex AI to enable TPU training with the appropriate topology?

Accepted Answer

TPU_CONFIG. Vertex AI automatically sets the TPU_CONFIG environment variable to communicate the TPU worker IP address and port to the training container. TF_CONFIG is used for distributed training with CPUs/GPUs, but TPU_CONFIG is the correct one for TPU training.

Answer

TPU_NAME

Answer

XRT_TPU_CONFIG

Answer

TF_CONFIG

Question 7

A financial services company has deployed a credit risk ML model on Vertex AI. They want to monitor the model for fairness across demographic groups to ensure no biased outcomes. Which TWO actions should they take as best practices? (Choose TWO.)

Accepted Answer

Use Vertex Explainable AI to understand feature attributions and compare their distributions across demographic groups.. Vertex Explainable AI provides feature attribution scores that can be compared across demographic groups to detect if the model relies on sensitive attributes or proxies. This enables fairness auditing by revealing whether the model's decision logic differs systematically for protected groups, which is a best practice for monitoring bias.

Answer

Eliminate all features that are correlated with protected attributes from the model input to ensure fairness.

Answer

Periodically compare the model's performance metrics (e.g., AUC) on the overall population versus the holdout test set.

Answer

Store all model predictions in BigQuery but do not capture ground truth labels to avoid privacy issues.

Question 8

A company uses Vertex AI Pipelines to train and deploy models. The pipeline has a step that runs a custom container. The step fails intermittently with a timeout error. Which approach should be taken to robustly handle this?

Accepted Answer

Increase the timeout for the step in the pipeline definition. Vertex AI Pipelines (built on Kubeflow Pipelines) allows you to define a `timeout` parameter for each pipeline step. Increasing this timeout directly addresses the intermittent timeout error by giving the custom container more time to complete its work, without changing the pipeline architecture or introducing external monitoring components. This is the most robust and minimal-change solution for a step that occasionally exceeds its current time limit.

Answer

Switch to Kubeflow Pipelines

Answer

Set up a Cloud Composer DAG to monitor and rerun the pipeline

Answer

Reduce the size of the training data

Answer

Use Cloud Functions to retry the step

Question 9

A data scientist wants to train a TensorFlow model on Vertex AI using a pre-built container. Which of the following pre-built containers is NOT available for custom training in Vertex AI?

Accepted Answer

Apache Spark. Vertex AI provides pre-built containers for TensorFlow, PyTorch, scikit-learn, and XGBoost. There is no pre-built container for Apache Spark in the Vertex AI training service; Spark jobs are typically run on Dataproc.

Answer

TensorFlow

Answer

PyTorch

Answer

scikit-learn

Question 10

Which Vertex AI feature allows you to reduce the size of a trained model to improve inference speed on edge devices without significant accuracy loss?

Accepted Answer

Vertex AI Model Optimization. Vertex AI Model Optimization is the correct feature because it provides model quantization, pruning, and distillation techniques specifically designed to reduce model size and improve inference latency on edge devices. This service applies post-training quantization (e.g., FP32 to INT8) and structured weight pruning to shrink the model footprint while maintaining accuracy within acceptable thresholds, directly addressing the need for efficient deployment on resource-constrained hardware.

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Matching Engine

Answer

Vertex AI Continuous Training