PMLE Practice Test 49 — 15 Questions

Question 1

An ML engineer is designing a Vertex AI Pipeline that includes a custom training component. The component must read a dataset from a Cloud Storage bucket and write the trained model to another Cloud Storage location. The engineer wants the component to be reusable across pipelines and to ensure that the pipeline tracks the exact dataset and model artifacts. Which approach should the engineer take?

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

Accepted Answer

Pass the Cloud Storage URIs as component inputs and outputs, and declare them as artifacts of type Dataset and Model.. Using artifact inputs and outputs with types Dataset and Model ensures that Vertex AI Pipelines tracks lineage and metadata. It keeps the component reusable because the actual URIs are passed at runtime. Declaring artifacts also enables the pipeline to display them in the UI and to use them for caching and conditional execution.

Answer

Hardcode the Cloud Storage URIs inside the component's container code and use environment variables for configuration.

Answer

Use a single string parameter for the dataset URI and a single string parameter for the model URI, without declaring artifact types.

Answer

Mount a Cloud Storage bucket as a volume in the component container and write the model directly to the mounted path.

Question 2

You are preparing to train a large image classification model on Vertex AI using a custom training job. You want to optimize the training job for cost and performance. The dataset is stored in Cloud Storage as TFRecords and is about 2 TB. You plan to use a machine with 4 NVIDIA V100 GPUs. Which two actions should you take to improve training efficiency? (Choose two.)

Accepted Answer

Use a larger batch size and scale the learning rate accordingly.. Mixed precision training and increasing batch size with learning rate scaling are both effective ways to improve training efficiency on V100 GPUs. Mixed precision leverages tensor cores for faster computation and reduced memory, while larger batches improve GPU utilization. These actions directly target performance and cost without altering the model architecture or data storage.

Answer

Use a smaller model architecture to reduce training time.

Answer

Store the TFRecords in a regional Cloud Storage bucket instead of a multi-regional bucket.

Answer

Increase the number of vCPUs on the machine to improve data loading throughput.

Question 3

You are a machine learning engineer working on a team that uses Vertex AI Feature Store. A colleague has created a new feature and wants to make it available to other teams for training and serving. You need to ensure that the feature can be discovered and reused across projects. What should you do?

Accepted Answer

Create the feature in Vertex AI Feature Store and use Dataplex to catalog it, making it searchable and accessible to other teams.. Using Dataplex to catalog Vertex AI Feature Store features enables centralized discovery and metadata management. Other teams can search the catalog, find the feature, and request access, which fosters reuse and collaboration. This approach integrates with existing governance and access controls, unlike ad-hoc sharing methods.

Answer

Use Vertex AI Feature Store's built-in feature sharing capability by setting the feature's visibility to 'Public' within the organization.

Answer

Create the feature in a Vertex AI Feature Store instance and share the instance's project ID with other teams.

Answer

Register the feature in Vertex AI Feature Store and assign it a label that other teams can search for in the Vertex AI console.

Question 4

A data scientist finishes training a model in a Vertex AI Workbench notebook and wants to save it so that the deployment team can later deploy it to an endpoint without re-running the notebook. The deployment team needs to see the model's version history and assign a production alias. Which action should the data scientist take?

Accepted Answer

Register the model in Vertex AI Model Registry and share the model resource name with the deployment team.. The handoff from experimentation to deployment is standardized through Vertex AI Model Registry, which versions models and supports aliases. Sharing raw artifacts or retraining pipelines does not provide the deployment team with a governed, aliasable model resource, and Feature Store is unrelated to model storage.

Answer

Upload the model to a Vertex AI Feature Store entity type so the deployment team can retrieve it.

Answer

Create a Vertex AI Pipeline that retrains the model and send the pipeline template to the deployment team.

Answer

Export the model artifacts to a Cloud Storage bucket and share the bucket path with the deployment team.

Question 5

A team is using Vertex AI Pipelines to orchestrate a training workflow. They want to ensure that the pipeline can be reproduced exactly six months later for auditing purposes. They need to capture all necessary information to rerun the pipeline and obtain identical results. (Choose two.)

Accepted Answer

Use a fixed pipeline template with pinned component versions and container image digests.. To reproduce a pipeline run exactly, you must pin the pipeline template and container images to immutable versions, and store the exact parameters and input artifacts in a versioned location. These two practices ensure that both the code and data are preserved, allowing an identical rerun. Other options like caching or experiment tracking do not provide the necessary immutability.

Answer

Rely on the default caching behavior of Vertex AI Pipelines to reuse previous step outputs.

Answer

Use the same pipeline name and run it again with the same parameters.

Answer

Enable Vertex AI Experiments to log metrics and parameters for each run.

Question 6

An ML engineer manages a Vertex AI Endpoint serving a recommendation model. The team wants to detect when the distribution of a specific numerical feature, average session duration, shifts significantly from its training distribution. They have configured Vertex AI Model Monitoring with a training dataset baseline and a monitoring frequency of one hour. After a week, no drift alerts have fired even though the feature's daily mean has visibly moved. What is the most likely cause?

Accepted Answer

The feature was not included in the monitoring configuration's feature list, so it is not being analyzed.. Vertex AI Model Monitoring only evaluates features that are explicitly included in the monitoring configuration. If average session duration was left out of the monitored feature list, drift for that feature is never computed, so no alert can fire even when the mean shifts. The other options either describe non-existent behavior or misattribute the cause to frequency or baseline updates.

Answer

The monitoring frequency of one hour is too infrequent to detect daily mean shifts.

Answer

Drift alerts require at least 30 days of live data before they can trigger.

Answer

The training dataset baseline was overwritten by the latest live data automatically.

Question 7

A marketing team wants to build a model to predict which customers are likely to churn. They have a BigQuery table with customer demographics, usage metrics, and a binary churn label. They want to use BigQuery ML and need to evaluate the model's performance. Which two statements are true regarding model evaluation in BigQuery ML? (Choose two.)

Accepted Answer

ML.CONFUSION_MATRIX can be used to visualize the confusion matrix of a classification model.. ML.EVALUATE computes standard classification metrics, and ML.CONFUSION_MATRIX provides a detailed breakdown of prediction outcomes. Both are essential for assessing a churn model. The other functions serve different purposes: ML.PREDICT for scoring, ML.TRAINING_INFO for training details, and ML.FEATURE_INFO for feature statistics.

Answer

ML.FEATURE_INFO returns the importance of each feature in the model.

Answer

ML.PREDICT automatically calculates the model's accuracy on the input data.

Answer

ML.TRAINING_INFO provides the evaluation metrics for the trained model.

Question 8

A small marketing team has a CSV file of 2,000 labeled customer support tickets (each with a category such as 'billing' or 'technical'). They have no ML engineers and want a fully managed, low-code way to train a text classification model that they can later call from their internal web app. Which Google Cloud service should they use?

Accepted Answer

Vertex AI AutoML text classification. AutoML text classification is purpose-built for teams with labeled text and no ML expertise: it ingests a CSV, trains, tunes, and serves a model behind a managed endpoint. The other services either require custom code, expect structured numeric features, or rely on a fixed taxonomy that cannot represent the team's custom ticket categories.

Answer

BigQuery ML with a CREATE MODEL statement using the LOGISTIC_REG model type

Answer

Vertex AI Pipelines with a custom Kubeflow component for text preprocessing

Answer

Cloud Natural Language API classifyText method

Question 9

An ML engineer is using Vertex AI Pipelines to orchestrate a training workflow. The pipeline includes a step that trains a model and a subsequent step that evaluates the model. The engineer wants to ensure that the evaluation step runs only if the training step succeeds and that the pipeline fails if the model's accuracy is below a threshold. Which approach should the engineer use?

Accepted Answer

Use a condition in the pipeline that checks the training step's status and a custom component that raises an exception if accuracy is below the threshold.. To conditionally run a step and fail the pipeline based on a metric, the engineer should use a condition to check the training step's status and a custom component that raises an exception if accuracy is too low. Vertex AI Pipelines conditions allow steps to run only if previous steps succeed, and raising an exception in a component causes the pipeline to fail. This combination provides the required control flow and failure semantics.

Answer

Set the evaluation step's retry policy to zero and use the pipeline's built-in accuracy threshold parameter.

Answer

Use a Vertex AI Model resource to store the model and enable Model Monitoring to trigger a pipeline failure if accuracy drops.

Answer

Configure the evaluation step to always run and rely on Vertex AI Pipelines to automatically fail the pipeline if the accuracy metric is below the threshold.

Question 10

A junior engineer on your team has a trained scikit-learn model saved as a local joblib file and wants other teams to be able to discover it, view its evaluation metrics, and deploy it to a Vertex AI Endpoint. Which action should they take first?

Accepted Answer

Upload the model artifact to Vertex AI Model Registry with its serving container and metadata.. To let other teams discover, evaluate, and deploy a model, it must be registered in a shared catalog. Vertex AI Model Registry stores the artifact, its serving container, and metadata such as evaluation metrics, and it integrates directly with endpoints for deployment. Git, raw Cloud Storage objects, and manual node manipulation all lack the cataloging, versioning, and deployment integration the scenario requires.

Answer

Create a Vertex AI Endpoint and manually copy the joblib file onto the underlying prediction nodes.

Answer

Copy the joblib file into a shared Cloud Storage bucket and email the object path.

Answer

Commit the joblib file to the team's Git repository and share the repository link.

Question 11

A team wants to run a Vertex AI pipeline that deploys a model, runs a smoke test against the endpoint, and automatically rolls back if the smoke test fails. They need the deployment step to be reversible and the endpoint to remain available during the update. Which approach should they use?

Accepted Answer

Deploy the new model as a second deployment resource on the endpoint with 0% traffic, run the smoke test, then shift traffic and delete the old deployment if the test passes.. Adding a new deployment resource to the existing endpoint lets the team validate the new model without disrupting current traffic. Traffic can be moved only after the smoke test succeeds, and removing the old resource completes the rollout; if the test fails, the old resource is still serving and no rollback action is needed.

Answer

Deploy the new model to a separate endpoint, run the smoke test there, and then update the application's endpoint URL after the test passes.

Answer

Use a Vertex AI pipeline with a custom training component that trains and deploys the model in a single step, and rely on pipeline retry policies for rollback.

Answer

Undeploy the current model, deploy the new model on the same endpoint, and run the smoke test; if it fails, redeploy the previous model.

Question 12

An ML engineer is designing a Vertex AI Pipeline that includes a custom training component. The component must read a dataset from a Cloud Storage bucket and write the trained model to another Cloud Storage location. The engineer wants the component to be reusable across pipelines and to ensure that the pipeline tracks the exact dataset and model artifacts. Which approach should the engineer take?

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

Accepted Answer

Specify the pipeline's service account when submitting the pipeline run.. To securely access Cloud Storage and Vertex AI Model Registry, the pipeline's service account must have the required IAM roles, and that service account must be specified when submitting the pipeline run. This ensures the component authenticates with the correct permissions without embedding credentials.

Answer

Use the default Compute Engine service account for all pipeline runs.

Answer

Set the component's environment variable GOOGLE_APPLICATION_CREDENTIALS to point to a mounted secret.

Answer

Embed a service account key file in the component's container image.

Question 13

You are preparing to deploy a trained scikit-learn model to Vertex AI for online prediction. You need to create a custom container that serves the model. Which two of the following steps are required to ensure the container works with Vertex AI? (Choose two.)

Accepted Answer

Include the model artifacts in the container image or ensure they are accessible at runtime.. For a custom container to work with Vertex AI, it must listen on the port specified by the AIP_HTTP_PORT environment variable and have access to the model artifacts. These are the two essential steps. Other aspects like health endpoints, SDK usage, or non-root user are optional or best practices but not mandatory for the container to serve predictions on Vertex AI.

Answer

Ensure the container runs as a non-root user for security.

Answer

Expose a /health endpoint that returns a 200 status code.

Answer

Use the Vertex AI SDK to build and push the container image.

Question 14

A company runs a Vertex AI endpoint that serves a model for real-time predictions. The endpoint uses a custom container that loads a 10 GB model into memory. During a traffic spike, the autoscaler adds new replicas, but each new replica takes 8 minutes to become ready because it must download the model from Cloud Storage. The team wants to reduce scale-up time. Which approach is most effective?

Accepted Answer

Bake the model artifacts into the custom container image and push it to Artifact Registry, then deploy the endpoint with that image.. The slow scale-up is caused by downloading a 10 GB model from Cloud Storage at container startup. By embedding the model into the container image and storing it in Artifact Registry, the model is available locally when the container starts. This removes the network transfer from the critical path and dramatically reduces the time for a new replica to become ready, allowing the autoscaler to respond faster to traffic spikes.

Answer

Increase the minReplicaCount to a value that covers peak traffic so that new replicas are not needed.

Answer

Store the model artifacts in a regional Cloud Storage bucket and enable parallel downloads in the custom container.

Answer

Enable Vertex AI Model Monitoring to detect when the model is slow to load and trigger a preemptive scale-up.

Question 15

An ML engineer is authoring a Vertex AI Pipelines component that runs a custom Python script. The component must accept a GCS path to training data and output a model artifact. The engineer wants the component interface to be strongly typed and to automatically generate the component specification from the Python function. Which approach should the engineer use?

Accepted Answer

Define the component using the @dsl.component decorator from the kfp package, annotating the function parameters with types like str and Output[Model].. The Kubeflow Pipelines SDK v2 @dsl.component decorator introspects Python type annotations to build a component specification, including input and output artifacts. This provides strong typing and automatic generation, which is exactly what the engineer needs. Other approaches either require manual YAML definition or are intended for different use cases like custom training jobs.

Answer

Define the component using the @component decorator from the google.cloud.aiplatform.v1alpha1 package, specifying the input and output types as function annotations.

Answer

Use the google.cloud.aiplatform.CustomContainerTrainingJob class to package the script and run it as a pipeline step.

Answer

Write a Dockerfile that installs the required dependencies and exposes the script as an entrypoint, then build and push the image to Artifact Registry.