PMLE Timed Practice Test 3 — 10 Questions

Question 1

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.

Answer

Use Model Evaluation to decide

Answer

Set up a trigger in Vertex AI Pipelines

Answer

Cloud Scheduler on a daily basis

Question 2

A financial institution needs to extract structured data from scanned PDFs of loan applications, including text fields and tables. They require a human review step for high-risk applications. Which Google Cloud service and configuration should they use?

Accepted Answer

Document AI with a form parser processor and enable Human-in-the-Loop for high-risk applications. Document AI's Form Parser processor is purpose-built to extract structured key-value pairs and tables from forms like loan applications, and it natively integrates with Human-in-the-Loop (HITL) to route low-confidence or high-risk documents to human reviewers. This combination directly satisfies both the extraction and human review requirements without custom code.

Answer

Document AI with a custom extractor processor and use Cloud Functions for human review

Answer

Cloud Vision API to detect text and tables, then send to Cloud Dataflow for processing

Answer

Vertex AI AutoML Vision to train a custom model for document parsing

Question 3

A machine learning team is collaborating on a project using Vertex AI Experiments to track model training runs. They want to ensure that all team members can reproduce any experiment by using the same code, data, and environment. Which THREE actions should the team take?

Accepted Answer

Store the training code in a Cloud Source Repository and tag commits with the experiment ID.. Option A is correct because storing the training code in Cloud Source Repositories and tagging commits with the experiment ID creates an immutable, versioned link between the exact code revision and the Vertex AI Experiment run, which is essential for reproducibility. Option B is correct because packaging the training environment in a custom container image pushed to Artifact Registry with a fixed (immutable) tag guarantees every team member runs the identical dependencies, libraries, and runtime, eliminating environment drift. Option C is correct because logging the dataset path and version as experiment parameters in Vertex AI Experiments captures the exact data snapshot used, so the same inputs can be retrieved and reused. Option D is not appropriate because sharing a service account key is an insecure credential-management anti-pattern and does not itself contribute to code, data, or environment reproducibility. Option E is not appropriate because hyperparameter tuning optimizes model performance and does not by itself ensure that code, data, and environment are pinned and reproducible.

Answer

Share a service account key with all team members so they can access the same resources.

Answer

Use Vertex AI's hyperparameter tuning job to automatically find the best parameters.

Question 4

Which THREE practices improve collaboration when using Cloud Composer for ML pipelines?

Accepted Answer

Use a shared Cloud Storage bucket for intermediate artifacts with appropriate permissions.. Cloud Composer workflows often require sharing intermediate data (e.g., transformed datasets, model checkpoints) across multiple DAGs or team members. A shared Cloud Storage bucket with fine-grained IAM permissions enables secure, centralized artifact exchange without duplicating data or exposing it to unauthorized users. This practice avoids hard-coded paths and ensures that all pipeline stages can reliably access the same artifacts, which is critical for reproducibility and collaboration in ML pipelines.

Answer

Keep all pipeline logic in a single large DAG for simplicity.

Answer

Embed service account keys directly in DAG code for authentication.

Question 5

You are using KFP SDK v2 to define a pipeline. You need to pass a large dataset between components. What is the best practice for passing data?

Accepted Answer

Write the data to Cloud Storage and pass the GCS URI as an artifact.. In KFP SDK v2, passing large datasets between components is best done by writing the data to Cloud Storage and passing the GCS URI as an artifact. This approach leverages KFP's built-in artifact tracking, ensures data persistence across container restarts, and avoids memory or disk limitations of ephemeral containers. The artifact is automatically serialized and passed as an input/output parameter, enabling efficient, scalable data exchange.

Answer

Use the component's temporary directory to share data between containers.

Answer

Pass the data as a serialized Python object in memory.

Answer

Store the data in a BigQuery table and pass the table reference.

Question 6

A company is deploying a new model version to an existing Vertex AI endpoint. They want to test the new version with 5% of traffic before fully rolling it out. What is the correct approach?

Accepted Answer

Deploy the new version as a separate model on the same endpoint and use the `traffic_split` parameter in the deployment request.. Vertex AI endpoints support traffic splitting between multiple deployed models. By deploying the new model version to the same endpoint and setting `traffic_split` to 5% for the new version and 95% for the existing version, the endpoint automatically routes a corresponding proportion of inference requests to each model without any client-side changes.

Answer

Create a new endpoint for the new version and update the client to call both endpoints.

Answer

Deploy the new version and set the minimum replicas to 0, then gradually increase.

Answer

Use Cloud Load Balancing to distribute traffic between two endpoints.

Question 7

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 8

A data scientist wants to perform A/B testing between two model versions deployed on the same Vertex AI endpoint. They need to route 10% of traffic to the challenger model. Which approach should they use?

Accepted Answer

Deploy both models to the same endpoint and set traffic_split to 90 for champion and 10 for challenger. Vertex AI endpoints support traffic splitting directly, allowing you to route a percentage of requests to different model versions deployed on the same endpoint. By setting `traffic_split` to 90 for the champion and 10 for the challenger, the data scientist can perform online A/B testing without additional infrastructure. This is the simplest and most cost-effective approach, as it avoids managing separate endpoints or load balancers.

Answer

Use Vertex AI Experiments to compare models offline, then deploy the winner

Answer

Deploy the challenger model to a separate endpoint and use a load balancer to split traffic

Answer

Update the champion model with a new version and use model version aliases

Question 9

You want to use a pre-trained model from TensorFlow Hub for image classification, but you need to adapt it to classify your own custom categories with a small dataset. Which Vertex AI approach is most appropriate?

Accepted Answer

Write a custom training script that loads the pre-trained model and fine-tunes it on your dataset. Fine-tuning a pre-trained model on your custom dataset is the most appropriate approach when you have a small dataset and need to adapt the model to new categories. This leverages transfer learning, where the pre-trained weights are used as a starting point and updated with your data.

Answer

Deploy the pre-trained model as-is via Vertex AI JumpStart

Answer

Build a custom container with the pre-trained model and deploy to Vertex AI Endpoints

Answer

Use Vertex AI AutoML for image classification

Question 10

A data engineer wants to use BigQuery ML to train a model for predicting customer churn (binary classification) using a large dataset. They want the model to be automatically tuned. Which model type should they choose?

Accepted Answer

AUTOML_CLASSIFIER. (AUTOML_CLASSIFIER) is correct because it automatically performs architecture search and hyperparameter tuning to find the best model for binary classification tasks, such as customer churn prediction. This is ideal when the data engineer wants the model to be automatically tuned without manual intervention, as AutoML handles feature engineering, model selection, and tuning under the hood.

Answer

LOGISTIC_REG

Answer

BOOSTED_TREE_CLASSIFIER

Answer

DNN_CLASSIFIER