PMLE Practice Test 14 — 15 Questions

Question 1

A team is using Cloud Composer to orchestrate ML workflows. They want to allow multiple data scientists to contribute DAGs without interfering with each other. What is the recommended approach?

Accepted Answer

Store DAGs in a source control repository and use CI/CD to deploy to Cloud Composer. Cloud Composer (based on Apache Airflow) recommends managing DAGs via source control and CI/CD pipelines to ensure version control, code review, and consistent deployment. This prevents conflicts when multiple data scientists contribute, as each change is tracked and tested before being synced to the DAGs folder in Cloud Storage, avoiding overwrites or broken workflows.

Answer

Give each data scientist write access to the DAGs folder in Cloud Storage

Answer

Use a complex naming convention for DAG files to avoid overwriting

Answer

Create a separate Cloud Composer environment for each data scientist

Question 2

A machine learning engineer notices that the Vertex AI Prediction endpoint's error rate has increased over the past week. The model was retrained with new data and redeployed. Which step should the engineer take first to diagnose the issue?

Accepted Answer

Check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs.. The correct first step in any incident diagnosis is to gather observability data — Cloud Monitoring metrics (error rate, latency, error codes) and prediction logs — to characterize the failure before changing anything. This reveals whether errors are 4xx (bad input), 5xx (model/server), timeouts, or a specific code path, and whether the issue correlates with the redeployment. Only after this evidence is collected should the engineer consider rollback or data-distribution analysis.

Answer

Increase the number of replicas to reduce error rate.

Answer

Compare the input data distribution of recent requests to the training data distribution using Explainable AI.

Answer

Roll back to the previous model version immediately.

Question 3

An ML engineer is using Vertex AI for distributed training of a PyTorch model across multiple nodes. The training job must use TPUs for high throughput. The engineer sets up the job configuration. Which THREE components are required for the training to work correctly? (Select 3)

Accepted Answer

A startup script to configure the TPU pod (e.g., `xla_lib.sh`). A is correct because TPU pods require a startup script (e.g., `xla_lib.sh`) to initialize the XLA runtime, configure the TPU mesh, and set environment variables like `XRT_TPU_CONFIG`. Without this script, the TPU devices will not be discoverable by the PyTorch/XLA process, causing the training to fail with device-not-found errors.

Answer

A MultiWorkerMirroredStrategy configuration

Answer

A TF_CONFIG environment variable set for each worker

Question 4

Which TWO actions should be taken to ensure reproducibility of ML experiments when collaborating across teams on Vertex AI?

Accepted Answer

Lock dependency versions in a container image used for training. Option A is correct because locking dependency versions inside a container image used for training guarantees that every team member executes the same libraries and framework versions, eliminating environment drift as a source of non-reproducible results on Vertex AI. Option C is correct because versioning datasets with DVC or tracking them through Vertex AI ML Metadata pins the exact data lineage and artifact versions, so any experiment can be re-run against the identical input data. Options B, D, and E do not ensure reproducibility: real-time notebook sharing (B) is a collaboration feature, not a versioning mechanism; letting each team use its own environment (D) introduces dependency divergence; and always using random seeds (E) is misstated, since reproducibility requires fixing a consistent seed value, not merely using random seeds.

Answer

Share notebooks via Colab Enterprise with real-time editing

Answer

Allow each team to use their own preferred environment

Answer

Always use random seeds for all random operations

Question 5

What does the `ML.PREDICT` command do in BigQuery ML?

Accepted Answer

Makes predictions using the model. The command is likely a BigQuery ML prediction query (e.g., using `ML.PREDICT`) that uses a trained model to generate predictions on new input data, making option D correct. It does not train, export, or evaluate the model.

Answer

Trains a new BigQuery ML model

Answer

Exports the model to Cloud Storage

Answer

Evaluates the model's performance

Question 6

You need to deploy a PyTorch model for online inference on Vertex AI but the model was trained using custom ops that are not natively supported. You want to use NVIDIA Triton Inference Server for optimisation. How should you proceed?

Accepted Answer

Build a custom container with NVIDIA Triton Inference Server and deploy it to Vertex AI.. Vertex AI supports custom containers for prediction, so you can package NVIDIA Triton Inference Server with your PyTorch model and any custom ops/libraries it needs, then deploy that container as a Vertex AI Model. Triton natively supports PyTorch (via TorchScript), ONNX, TensorRT, and custom backends, making it the right choice when the model relies on non-standard operators that Vertex AI's pre-built PyTorch/TensorFlow containers cannot execute.

Answer

Convert the model to TFLite and deploy on an edge device.

Answer

Export the model to ONNX and deploy using Vertex AI's built-in TensorFlow serving.

Answer

Use Vertex AI Model Optimisation to automatically quantise the model.

Question 7

A data engineer wants to orchestrate a complex workflow that includes running a Vertex AI pipeline, then a BigQuery job, and finally a Dataflow pipeline. The workflow must handle dependencies, retries, and monitoring. Which Google Cloud service is most suitable for this orchestration?

Accepted Answer

Cloud Composer. Cloud Composer (based on Apache Airflow) is the most suitable service for orchestrating a complex workflow with dependencies, retries, and monitoring across Vertex AI, BigQuery, and Dataflow. It provides a managed Airflow environment that natively supports DAG-based orchestration, built-in retry logic, and integration with Google Cloud services via operators like VertexAIPipelineOperator, BigQueryOperator, and DataflowTemplatedJobStartOperator.

Answer

Cloud Tasks

Answer

Cloud Scheduler

Answer

Workflows

Question 8

A company needs to perform sentiment analysis on streaming social media data. Which architecture should they use?

Accepted Answer

Pub/Sub → Dataflow → Natural Language API → BigQuery. Streaming social media data requires a scalable, ordered ingestion pipeline. Pub/Sub ingests the stream, Dataflow processes it in real-time (e.g., windowing, deduplication), the Natural Language API performs sentiment analysis, and BigQuery stores results for querying. This decouples ingestion from processing and storage, enabling exactly-once semantics and auto-scaling.

Answer

Dataflow → Pub/Sub → Natural Language API → BigQuery

Answer

Pub/Sub → Cloud Functions → Natural Language API → Cloud Storage

Answer

Cloud Functions → Pub/Sub → Natural Language API → BigQuery

Question 9

A team uses Cloud Build to automatically trigger a Vertex AI pipeline when changes are pushed to the model code repository. They have a cloudbuild.yaml file that builds a container image and submits the pipeline. However, they want to run the pipeline only if the commit includes changes to the 'training/' directory. Which Cloud Build configuration option should be used to filter the trigger?

Accepted Answer

Set the 'includedFiles' field to 'training/**' in the trigger configuration.. Cloud Build triggers support an `includedFiles` field that specifies a glob pattern. When set to `training/**`, the trigger will only fire if the commit includes changes to files under the `training/` directory. This is the native, declarative way to filter triggers based on changed file paths without additional infrastructure.

Answer

Add a 'ignoreFiles' field with 'training/**' to the trigger.

Answer

Use a 'substitutions' field with a regex pattern to filter commits.

Answer

Configure a Cloud Function to check the commit diff and call Cloud Build API conditionally.

Question 10

A company uses Vertex AI Pipelines to train models on a daily schedule. The pipeline includes a component that runs a BigQuery query to extract features. The team wants to ensure that if the BigQuery component fails due to transient network errors, the pipeline automatically retries it. How can they configure retries in Vertex AI Pipelines?

Accepted Answer

Set the `retry` parameter of the component to a positive integer, for example `retry=3`.. Vertex AI Pipelines natively supports a `retry` parameter on pipeline components. Setting `retry=3` instructs the pipeline to automatically retry the component up to three times if it fails due to transient errors, such as network timeouts. This is the simplest and most direct way to handle retries within the Vertex AI Pipelines orchestration framework.

Answer

Deploy the component as a Cloud Function and configure Cloud Functions retry.

Answer

Wrap the component in a `dsl.If` conditional that checks for failure and re-submits the component.

Answer

Use Cloud Composer with a task retry policy in Airflow.

Question 11

A financial institution needs to deploy a fraud detection model with strict latency <100ms per prediction and high throughput (1000 predictions/sec). The model is a deep neural network. Which architecture on Google Cloud meets these requirements?

Accepted Answer

Use Vertex AI Prediction with autoscaling enabled and GPU machine types. Vertex AI Prediction with autoscaling and GPU machine types is correct because it provides low-latency online serving with autoscaling to handle high throughput (1000 predictions/sec) while keeping latency under 100ms. GPUs accelerate deep neural network inference, and autoscaling ensures resources match demand without over-provisioning.

Answer

Deploy the model on AI Platform Training with a single large VM

Answer

Deploy the model as a Cloud Function triggered by Cloud Pub/Sub

Answer

Use Vertex AI Batch Prediction with a fixed number of machines

Question 12

A company needs to serve a model for real-time predictions with a strict latency SLA of 100ms at the 99th percentile. The model is lightweight and traffic patterns are highly variable with occasional spikes. Which deployment strategy best meets the SLA while controlling cost?

Accepted Answer

Deploy to Vertex AI Endpoint with min_replica_count=3 and autoscaling enabled.. Setting a minimum number of replicas ensures baseline capacity to handle initial spikes without cold start delays, while autoscaling handles larger spikes. Option A is wrong because Cloud Run with autoscaling to zero may cause cold start delays, which could violate the strict latency SLA. Option B is wrong because manual scaling with a fixed number of replicas may lead to over-provisioning or under-provisioning. Option C is wrong because batch prediction is not real-time.

Answer

Deploy the model as a Cloud Run service with autoscaling to zero.

Answer

Deploy to Vertex AI Endpoint with manual scaling and a fixed number of replicas.

Answer

Use Vertex AI Batch Prediction.

Question 13

A company wants to implement continuous delivery (CD) for ML models, where a model is automatically deployed to a staging environment and only promoted to production after passing an evaluation gate. Which combination of GCP services is BEST suited for orchestrating this CD pipeline?

Accepted Answer

Cloud Build with Vertex AI Pipelines and Cloud Deploy. Cloud Build can trigger on code/model changes and run a pipeline that deploys to staging. After evaluation, if successful, it can promote to production using Cloud Deploy or directly update Vertex AI endpoints. Cloud Composer (Airflow) is also a good option for complex orchestration, but for CI/CD, Cloud Build is a natural fit. The combination of Cloud Build, Cloud Deploy, and Vertex AI provides a robust CD pipeline.

Answer

Cloud Scheduler and Pub/Sub

Answer

Cloud Composer (Airflow) with Cloud Functions

Answer

Vertex AI Pipelines with Cloud Run

Question 14

A team uses Vertex AI Pipelines to automate training and deployment. They need to ensure that only models that pass a set of quality checks (e.g., accuracy > 0.9, latency < 100ms) are deployed to production. How should they implement this?

Accepted Answer

Add a Pipeline component that evaluates metrics and uses a conditional gate to deployment. Vertex AI Pipelines supports conditional logic through components and pipeline control flow, allowing a component to evaluate model metrics and a downstream conditional gate to promote the model only if thresholds (accuracy > 0.9, latency < 100ms) are met. This embeds quality gates directly into the MLOps pipeline, ensuring automated, repeatable promotion decisions.

Answer

Manually review each model before promotion

Answer

Use Cloud Functions to deploy only if accuracy is reported in BigQuery

Answer

Set up Cloud Build triggers to deploy every model version

Question 15

A company needs to serve a high-throughput prediction service with strict latency requirements. They want to minimize cold starts and ensure consistent performance. Which endpoint configuration is most appropriate?

Accepted Answer

Set min_replicas to an estimated baseline and max_replicas to a higher number. Setting min_replicas to an estimated baseline ensures that a minimum number of instances are always running, eliminating cold starts for baseline traffic. Setting max_replicas to a higher number allows the service to scale up to handle traffic spikes while maintaining consistent performance. This configuration balances cost and latency by avoiding the overhead of scaling from zero while still accommodating bursts.

Answer

Set min_replicas and max_replicas equal to a fixed number

Answer

Set min_replicas to 0 and max_replicas to a high number

Answer

Do not set min_replicas; let Vertex AI automatically determine