PMLE Practice Test 16 — 15 Questions

Question 1

A data scientist wants to define a lightweight Python function component in Vertex AI Pipelines using Kubeflow Pipelines SDK v2. Which decorator should be applied to the function to make it a pipeline component?

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

Accepted Answer

@dsl.component. In Kubeflow Pipelines SDK v2, the @dsl.component decorator converts a lightweight Python function into a pipeline component. This is the standard v2 approach for defining function-based components. @dsl.pipeline is used to define the pipeline itself, not individual components.

Answer

@dsl.pipeline

Answer

@kfp.v2.components.func_to_component

Answer

@kfp.dsl.component

Question 2

A data science team wants to share a set of engineered features across multiple projects and teams to reduce training-serving skew and ensure consistency. They need low-latency serving (single-digit milliseconds) for online predictions and also need to retrieve historical feature values for training. Which approach should they take?

Accepted Answer

Use Vertex AI Feature Store to define features once, serve online predictions from the online store, and retrieve historical features from the offline store for training.. Vertex AI Feature Store is a fully managed, purpose-built feature store that centralizes feature definitions and provides both an online store for low-latency serving and an offline store for historical retrieval. The online store is optimized for single-digit millisecond reads, while the offline store (backed by BigQuery) allows point-in-time correct feature retrieval for training, which directly addresses training-serving skew. By defining features once and reusing them across projects, it ensures consistency between training and serving.

Answer

Create a shared BigQuery dataset where each team writes features; serve predictions by querying BigQuery synchronously.

Answer

Store features in Cloud Storage Parquet files and load them into BigQuery for training; serve predictions from a custom microservice that reads from Cloud Storage.

Answer

Use Cloud Memorystore (Redis) to store the latest feature values for low-latency serving; each team independently computes features and pushes to Redis.

Question 3

A company uses Vertex AI Model Monitoring to detect training-serving skew. They have a categorical feature 'product_category' with high cardinality. The monitoring job alerts for skew, but the data scientists believe the model performance is still acceptable. Which THREE actions should the team take to investigate and resolve the alert?

Accepted Answer

Examine which categories have the largest distribution changes to understand the nature of the shift.. Examining which categories have the largest distribution changes allows the team to pinpoint the root cause of the training-serving skew. In Vertex AI Model Monitoring, the skew alert is based on statistical distance metrics (e.g., Jensen-Shannon divergence) between training and serving distributions. By drilling down into the specific categories driving the divergence, the team can assess whether the shift is benign (e.g., seasonal) or problematic, rather than relying on aggregate model performance alone.

Answer

Remove the drifted categories from the feature set to eliminate the alert.

Answer

Ignore the alert because the model is performing well; monitoring alerts are often false positives.

Question 4

A team has a pipeline that trains a model and then evaluates it. They want to conditionally deploy the model to a staging endpoint only if evaluation metrics exceed a threshold. Which KFP feature should they use?

Accepted Answer

Use dsl.Condition (deprecated) or dsl.If to check metrics and conditionally run deployment.. KFP provides `dsl.Condition` (deprecated) and `dsl.If` as first-class pipeline constructs to conditionally execute pipeline components based on runtime metrics or other pipeline outputs. By wrapping the deployment step inside a `dsl.If` block that checks whether evaluation metrics exceed a threshold, the pipeline can deploy the model to a staging endpoint only when the condition is met, avoiding unnecessary deployments for underperforming models.

Answer

Use dsl.ParallelFor to evaluate and deploy in parallel.

Answer

Use an exit handler to deploy regardless of metrics.

Answer

Split the pipeline into two separate pipelines and run the second only if metrics are good.

Question 5

You need to query a Vertex AI Vector Search index for nearest neighbours. The index is deployed on an endpoint. Which API method should you use to perform the query?

Accepted Answer

projects.locations.indexEndpoints.findNeighbors. The correct API method to query a deployed Vertex AI Vector Search index for nearest neighbors is `projects.locations.indexEndpoints.findNeighbors`. This method is specifically designed for vector similarity search against an index endpoint, returning the nearest neighbors for a given query vector. The other options either target the wrong resource (indexes instead of indexEndpoints) or use methods intended for different purposes like model prediction.

Answer

projects.locations.indexes.match

Answer

projects.locations.indexes.query

Answer

projects.locations.endpoints.predict

Question 6

An organization wants to implement central governance for ML models across teams. Which TWO services should they use together to achieve model versioning, lineage, and deployment management? (Select 2)

Accepted Answer

Vertex AI Model Registry. Vertex AI Model Registry (B) is correct because it provides a central repository for managing the lifecycle of ML models, including versioning, tracking model artifacts, and managing deployment to endpoints, which directly satisfies the model versioning and deployment management requirements. Vertex AI Metadata (C) is correct because it captures and stores metadata about artifacts, executions, and contexts, enabling lineage tracking across the ML workflow so teams can trace how models were produced and what data or training runs contributed to them. Together, Model Registry handles versioning and deployment while Metadata provides the lineage and governance context needed for central oversight. Vertex AI Feature Store (A) is for organizing, serving, and sharing feature data, not for model versioning or deployment management. Vertex AI Experiments (D) is for tracking and comparing training experiment runs and metrics, which supports experimentation but not centralized model deployment governance. Cloud Data Catalog (E) is a data discovery and metadata management service for data assets, not a model registry or ML lineage/deployment tool.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Cloud Data Catalog

Question 7

Which TWO actions are recommended to detect and mitigate data drift in a production ML system on Vertex AI?

Accepted Answer

Automatically retrain the model when drift exceeds thresholds. Option D is correct because Vertex AI Model Monitoring is the purpose-built service for detecting data drift in production; it continuously analyzes incoming prediction requests against a baseline and alerts when feature distribution changes (e.g., training-serving skew or drift) exceed configured thresholds. Option C is correct because automated retraining triggered when drift exceeds thresholds is the recommended mitigation action, closing the loop from detection to remediation so the model stays aligned with current data. Together, D provides detection and C provides mitigation, which is exactly what the question asks for. Option A is not recommended because ensembling multiple models addresses prediction variance/accuracy, not data drift detection or mitigation. Option B is not recommended because manual daily review does not scale and cannot reliably quantify distributional drift. Option E is not recommended because monitoring prediction errors or low confidence detects performance degradation but does not specifically identify feature distribution drift or mitigate it.

Answer

Deploy multiple models and use an ensemble to average predictions

Answer

Manually review model predictions daily

Answer

Monitor prediction errors and flag when confidence is low

Question 8

A team is building a CI/CD pipeline for an ML model. They want to automatically trigger a Vertex AI pipeline for retraining whenever new training data arrives in a Cloud Storage bucket, but only if a specific Pub/Sub notification is published by a data ingestion process. Which approach meets these requirements with minimal operational overhead?

Accepted Answer

Use Eventarc to route the Pub/Sub notification to a Cloud Function that calls the Vertex AI pipeline creation API.. Eventarc can directly listen to a Pub/Sub topic and route matching messages to a Cloud Function, which then calls the Vertex AI pipeline creation API. This serverless approach triggers the pipeline only when the specific Pub/Sub notification is published, meeting the requirement with zero infrastructure to manage and no polling overhead.

Answer

Use Cloud Scheduler to run a job every hour that checks for new files in Cloud Storage and starts the pipeline if new files exist.

Answer

Configure a Cloud Build trigger that listens to the Pub/Sub topic and executes a build step that submits the pipeline run.

Answer

Create a Dataflow streaming pipeline that reads from Pub/Sub and triggers the Vertex AI pipeline via a custom sink.

Question 9

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). Option B is correct because a production ML monitoring dashboard must track system resource utilization such as CPU, memory, and latency to detect infrastructure bottlenecks, scaling issues, and SLA violations that degrade inference serving. Option C is correct because input data quality metrics like missing values and outliers enable detection of data drift, schema violations, and upstream pipeline failures before they corrupt predictions. Option E is correct because tracking model performance metrics such as accuracy, precision, and recall over time is essential to detect model degradation, concept drift, and performance regressions in production. Option A is not a monitoring dashboard component; team roles and responsibilities belong to governance and RACI documentation, not runtime observability. Option D is not appropriate for a monitoring dashboard either, since training pipeline code version is a lineage/reproducibility artifact tracked in model registries or CI/CD metadata, not a live production monitoring signal.

Answer

Team member roles and responsibilities

Answer

Training pipeline code version

Question 10

You are using Vertex AI Training to train a model and then automatically deploy the best candidate to a Vertex AI Prediction endpoint via the Vertex AI Model Registry. However, after deployment, you notice that the endpoint returns predictions for the new model, but they are significantly different from the evaluation metrics computed during training. The training scripts used TensorFlow with a serving input function. What is the most likely issue and how would you fix it?

Accepted Answer

The serving input function's preprocessing steps do not match the training preprocessing; you should verify and align them.. The classic cause of a train/serve skew in Vertex AI is that the serving input function applies different preprocessing than the training pipeline — for example, different tokenization, normalization, or feature scaling. TensorFlow's serving_input_fn defines the graph that runs at prediction time, so any divergence from the training input_fn produces predictions that look correct structurally but are numerically off. Aligning the two preprocessing paths (ideally by sharing a single feature-engineering module) resolves the discrepancy.

Answer

The endpoint is using a different machine type affecting numerical precision; you should use the same machine type as training.

Answer

The model registry deployed a different version; you should check the alias.

Answer

The model was saved with training-only metrics; you should retrain with evaluation metrics.

Question 11

A data scientist trained a model on a single GPU but needs to train on multiple GPUs for a larger dataset. They observe that training time does not decrease linearly with additional GPUs. Which common issue is most likely?

Accepted Answer

Data pipeline bottleneck.. The most likely issue is a data pipeline bottleneck. When training on multiple GPUs, if the data input pipeline cannot feed data fast enough, the GPUs will idle waiting for data, leading to sublinear scaling. This is a common problem in distributed training where I/O or preprocessing becomes the limiting factor.

Answer

Overfitting.

Answer

Model architecture too simple.

Answer

Learning rate too high.

Question 12

Your team is deploying a large recommendation model on Vertex AI endpoints using GPUs. You need to minimise latency while optimising cost. The model serves many similar requests from the same users within short time windows. Which additional service would best reduce latency and cost?

Accepted Answer

Use Cloud Memorystore to cache prediction results.. Cloud Memorystore (Redis) in front of the Vertex AI endpoint lets you cache prediction results keyed by user/request signature, so repeated similar requests within short windows are served from cache instead of hitting the GPU-backed model. This reduces both latency (cache hit is sub-millisecond) and cost (fewer GPU inference calls).

Answer

Switch to CPU-only instances to reduce cost.

Answer

Increase maxReplicas to handle the load without caching.

Answer

Set up a Cloud CDN in front of the endpoint.

Question 13

Which TWO statements are true about canary deployments for Vertex AI endpoints?

Accepted Answer

You can roll back a canary by resetting traffic to 0% for the new version.. Option B is correct because in Vertex AI traffic splitting, a canary version is simply a DeployedModel receiving a percentage of prediction traffic; setting that version's traffic split to 0% (and returning 100% to the stable version) effectively rolls back the canary without undeploying it. Option C is correct because Vertex AI endpoints support traffic splitting where you assign each deployed model a percentage from 0 to 100, letting you gradually shift traffic from the existing version to the new version in controlled increments. Option A is wrong because canary/traffic splitting works with any deployed model on an endpoint, including AutoML and prebuilt-framework models, not only custom containers. Option D is wrong because traffic splitting operates on DeployedModels on an endpoint and does not require the model to be registered in Vertex AI Model Registry. Option E is wrong because traffic percentages are configurable at any time up to 100%, so a canary at 50% can be increased further.

Answer

Canary deployments are only supported for custom containers, not prebuilt frameworks.

Answer

Canary deployments require the use of Vertex AI Model Registry.

Answer

Once a canary receives 50% traffic, you cannot increase it further.

Question 14

A team wants to implement continuous training for their ML model. The pipeline should be triggered when new training data arrives in a Cloud Storage bucket. Which combination of services should they use?

Accepted Answer

Cloud Storage → Cloud Functions → Vertex AI Pipelines. The correct combination is Cloud Storage → Cloud Functions → Vertex AI Pipelines. Cloud Storage hosts the new training data; a Cloud Function triggered by an object-finalize event (or Pub/Sub notification) detects the new data and invokes a Vertex AI Pipeline to retrain the model. This creates a fully event-driven continuous training pipeline without polling or manual intervention.

Answer

Cloud Storage → BigQuery → Vertex AI Pipelines

Answer

Cloud Storage → Cloud Build → Vertex AI Pipelines

Answer

Cloud Storage → Cloud Scheduler → Vertex AI Pipelines

Question 15

An ML engineer is using Cloud Build to trigger a Vertex AI Pipeline on every commit to a repository. The pipeline takes 2 hours. The engineer wants to only run the pipeline when changes are made to specific directories. How can this be achieved?

Accepted Answer

Configure Cloud Build trigger with included file globs. Cloud Build triggers support 'included file globs' and 'ignored file globs' to filter which file changes should invoke the trigger. By specifying glob patterns for the directories of interest, the trigger will only fire when commits modify files matching those patterns, avoiding unnecessary pipeline runs for unrelated changes.

Answer

Use Cloud Composer to poll the repository periodically

Answer

Use a Cloud Function to evaluate changes and invoke the pipeline

Answer

Modify the pipeline to ignore unrelated changes

Answer

Add a conditional step in the pipeline to abort if no relevant changes