PMLE Collaborating to manage data and models Practice Test 4 — 15 Questions

Question 1

A company has multiple teams working on different models. They want to enforce consistent data preprocessing steps across all teams. Which approach should they take?

Accepted Answer

Create shared Vertex AI Pipelines components. Vertex AI Pipelines components allow teams to define reusable, versioned, and parameterized preprocessing steps that can be shared across models and pipelines. This ensures consistent execution of data transformations because each component encapsulates the exact code and environment, and pipelines enforce the same DAG of steps regardless of which team triggers them.

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Use Cloud Composer to orchestrate preprocessing

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Write shared Python packages in Artifact Registry

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Use Cloud Dataflow templates

Question 2

A data scientist wants to track the lineage of a dataset used in a training run. Which Vertex AI feature should they use?

Accepted Answer

Vertex ML Metadata. Vertex ML Metadata is the correct choice because it is specifically designed to track the lineage of datasets, models, and other artifacts throughout the ML lifecycle. It records metadata about each step in a pipeline, including the source dataset used for a training run, enabling full provenance tracking. This allows data scientists to trace back which data was used, how it was transformed, and which model version it produced.

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Vertex AI Feature Store

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Vertex AI Experiments

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Vertex AI Model Registry

Question 3

An MLOps team needs to automatically retrain a model when new training data becomes available. They use Vertex AI Pipelines. What is the recommended way to trigger the pipeline?

Accepted Answer

Cloud Functions triggered by Cloud Storage events. Vertex AI Pipelines does not natively support event-driven triggers. The recommended pattern is to use Cloud Functions, which can be triggered by Cloud Storage events (e.g., object finalize/create) when new training data is uploaded. The Cloud Function then programmatically submits the pipeline run via the Vertex AI Pipelines client library or REST API, enabling an automated retraining workflow.

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Use Model Evaluation to decide

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Set up a trigger in Vertex AI Pipelines

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Cloud Scheduler on a daily basis

Question 4

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?

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.

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Export features to Cloud Storage

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Create separate Feature Stores per team project

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Use BigQuery authorized views

Question 5

When distributing training across multiple workers using Vertex AI Training, how should the team share the training dataset?

Accepted Answer

Use Cloud Storage. Vertex AI Training workers need shared, concurrent read access to the training dataset without manual replication. Cloud Storage (GCS) is the recommended and fully integrated solution because it provides a distributed, highly available object store that all workers can read from in parallel via the `tf.io.gfile` API or GCS connector, eliminating data duplication and ensuring consistency across the cluster.

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Copy the dataset to each worker's local disk

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Use NFS

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Use Google Drive

Question 6

A team is using Vertex AI Experiments to compare different hyperparameters. They want to automatically record the hyperparameters. What is the correct way?

Accepted Answer

Use the `aiplatform.start_run()` context manager. Vertex AI Experiments provides a native `aiplatform.start_run()` context manager that automatically captures hyperparameters passed as key-value arguments, logging them to the experiment run metadata without manual intervention. This integrates directly with the Vertex AI SDK, ensuring consistency and traceability across runs.

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Manually log to console

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Write to a CSV file

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Use BigQuery

Question 7

A data engineering team uses Dataflow for preprocessing and wants to integrate with Vertex AI Pipelines. They need to pass the preprocessed data location to the training step. What is the best practice?

Accepted Answer

Use PipelineParam to pass the output path. PipelineParam is the native mechanism in Vertex AI Pipelines (Kubeflow Pipelines SDK) to pass runtime outputs—such as a Cloud Storage path—between components. It creates a dependency graph that ensures the training step receives the exact output path from the preprocessing step, enabling dynamic, reproducible pipelines without hardcoding.

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Store the path in Data Catalog

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Use Cloud Pub/Sub

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Write the output to a fixed Cloud Storage path and hardcode it in the pipeline

Question 8

Which TWO practices help ensure reproducible ML experiments?

Accepted Answer

Use Vertex AI Experiments to track parameters and metrics. Vertex AI Experiments automatically logs parameters, metrics, and artifacts for each run, creating a complete lineage that enables exact reproduction of results. By tracking these details alongside the code version, you can recreate the exact environment and configuration that produced a given outcome, which is essential for reproducibility.

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Store all artifacts in a temporary bucket

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Use a random seed for each run

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Use preemptible VMs

Question 9

Which THREE actions should be taken to manage model versions effectively?

Accepted Answer

Use Vertex AI Model Registry. Vertex AI Model Registry is a centralized repository that tracks, versions, and manages ML models. It enables you to organize models, assign aliases (like 'champion' or 'experiment'), and control deployment, ensuring reproducibility and governance across the model lifecycle.

Answer

Delete old versions immediately

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Use the same model name for all versions

Question 10

Refer to the exhibit. The team wants to automatically deploy the best-performing model version to production. They have set up a Cloud Function triggered by Model Registry events. Which alias should they use in the function to get the latest champion?

Accepted Answer

'champion'. The 'champion' alias is specifically reserved in Vertex AI Model Registry to denote the best-performing model version in production. By configuring the Cloud Function to trigger on the assignment of the 'champion' alias, the team ensures that only the model version promoted as the production champion is automatically deployed, aligning with MLOps best practices for staged model promotion.

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''

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'experiment'

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'latest'

Question 11

Refer to the exhibit. The team notices that the pipeline fails to read data from the specified Cloud Storage path. What is the most likely issue?

Accepted Answer

The service account lacks `storage.objectViewer` permission. The pipeline fails to read data from Cloud Storage because the service account lacks the `storage.objectViewer` IAM role, which grants the `storage.objects.get` and `storage.objects.list` permissions required to read objects. Without this role, the pipeline cannot authenticate or authorize the read operation, even if the bucket and path are correct.

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The bucket does not exist

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The pipeline runner is incorrect

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The region is mismatched

Question 12

A team uses Vertex AI Feature Store for storing features. They want to share feature definitions with other teams in a collaborative manner. What is the best way to collaborate on feature definitions?

Accepted Answer

Use a shared repository with feature definition files and CI/CD to update the feature store.. Using a shared repository with feature definition files and CI/CD pipelines enables version control, peer review, and automated deployment to Vertex AI Feature Store. This approach ensures consistency, traceability, and collaboration without risking direct, uncoordinated changes to the production feature store.

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Grant all teams write access to the same feature store so they can modify definitions directly.

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Export the feature definitions as CSV and email them to the other teams.

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Use a wiki page to document feature definitions and update it manually.

Question 13

A company uses BigQuery to store feature data for ML training. A data engineer notices that a Vertex AI Training job is failing with 'Access Denied' errors when reading from a BigQuery table. The training job uses a custom service account that has been granted the 'bigquery.dataViewer' role on the dataset. What is the most likely cause of the failure?

Accepted Answer

The service account lacks the 'bigquery.jobs.create' permission in the project.. The 'bigquery.dataViewer' role grants permissions to read BigQuery data (e.g., bigquery.tables.getData), but it does not include the 'bigquery.jobs.create' permission. When a Vertex AI training job reads from BigQuery, it must first create a BigQuery job (a query job) to retrieve the data. Without 'bigquery.jobs.create' at the project level, the service account cannot initiate the read operation, resulting in an 'Access Denied' error even though it has data-level access.

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The service account is not in the same project as the BigQuery dataset.

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The BigQuery table is partitioned and requires row-level access.

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The training job does not have the required network access to BigQuery.

Question 14

An organization uses Cloud Dataflow to preprocess training data. Dataflow jobs are often failing because of insufficient quota for certain resources. The team has requested a quota increase, but the jobs still fail with 'quota exceeded' errors for a different resource. They want to proactively monitor and manage quotas to avoid failures. What is the best approach?

Accepted Answer

Set up Cloud Monitoring alerts for quota usage and automate quota increase requests.. The best approach is to set up Cloud Monitoring alerts for quota usage and automate quota increase requests. This provides proactive visibility into all resource quotas (not just the one initially increased) and enables automated remediation before jobs fail. Cloud Monitoring can track quota metrics for services like Compute Engine, and you can use Cloud Functions or Pub/Sub to trigger quota increase requests via the Service Usage API.

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Configure Dataflow to use a different pipeline type that avoids the quota.

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Use Dataflow's autoscaling feature to reduce resource usage.

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Increase the maximum number of workers in the Dataflow job.

Question 15

Which TWO of the following are best practices for managing data in a collaborative machine learning environment on Google Cloud?

Accepted Answer

Use Cloud Data Catalog to discover and annotate datasets.. Cloud Data Catalog provides a managed metadata management service that allows teams to discover, annotate, and manage datasets across Google Cloud. It enables data scientists to search for datasets by tags, descriptions, and schema, which is essential for collaboration and data governance in a multi-user ML environment.

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Always replicate data across multiple regions to ensure low latency.

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Implement fine-grained access control using IAM conditions.

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Store all raw data in a single Cloud Storage bucket for easy access.