PMLE Practice Questions 2 — 50 Questions

Question 1

You are designing a batch prediction pipeline using Vertex AI. The input data is 100 TB of images stored in Cloud Storage. The model is a custom TensorFlow model that expects TFRecord format. The pipeline must be cost-effective and run within a time window of 2 hours. Which THREE steps should you include?

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

Accepted Answer

Create a Vertex AI batch prediction job with input from GCS (TFRecord files).. Vertex AI batch prediction jobs natively accept TFRecord files stored in Cloud Storage as input, which aligns with the requirement for a custom TensorFlow model. This approach is cost-effective and can complete within 2 hours by leveraging Vertex AI's managed infrastructure, avoiding the need to spin up and manage compute resources manually.

Answer

Store batch prediction results in BigQuery.

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Use Cloud Functions to convert images to TFRecord.

Question 2

A company needs to reduce inference latency for their online prediction service on Vertex AI. Which two actions would help? (Choose 2)

Accepted Answer

Deploy the model on a GPU-enabled machine. Deploying the model on a GPU-enabled machine significantly accelerates matrix operations and parallel computations inherent in deep learning inference, directly reducing per-request latency. Option C is correct because model quantization reduces the precision of model weights (e.g., from FP32 to INT8), which decreases memory footprint and speeds up computation, especially on compatible hardware like TPUs or GPUs.

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Increase the maximum number of replicas

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Use a smaller machine type with less memory

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Enable autoscaling with a lower target CPU utilization

Question 3

You have a Vertex AI pipeline that trains a model and outputs a Model artifact. You want to register this model in the Vertex AI Model Registry. Which pre-built Google Cloud Pipeline Components component should you use?

Accepted Answer

VertexModelUploadOp. The correct component is VertexModelUploadOp because it is specifically designed to upload a trained model artifact to the Vertex AI Model Registry, creating a new model version or a new model if one does not exist. This component takes the model artifact from a pipeline step and registers it, making it available for deployment or version management.

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VertexEndpointDeployOp

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CreateModelVersionsOp

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ModelRegisterOp

Question 4

You have an online prediction model that is showing increasing prediction latency. You have already verified that the request rate and input data size are unchanged. Which of the following should you investigate next?

Accepted Answer

Check if the model was recently updated to a larger version. If request rate and input data size are unchanged, increased prediction latency often points to a change in the model itself. A larger model (e.g., deeper neural network, more parameters) requires more computation per inference, directly increasing latency. This is a common root cause when monitoring ML pipelines, as model version updates can silently alter performance characteristics.

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Check the monitoring dashboard configuration

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Check if the feature engineering logic was changed

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Check the geographic location of the endpoint

Question 5

An organization wants to implement continuous delivery for their ML model. After a new model is trained and evaluated, they want to automatically deploy it to a staging endpoint, run validation tests, and if passed, promote to production. Which two components should they include in their delivery pipeline? (Choose two.)

Accepted Answer

Conditional component that checks evaluation metrics and promotes if successful. A conditional component that checks evaluation metrics (e.g., accuracy, precision, recall) against a predefined threshold is essential for automated promotion. This component acts as a gate, ensuring only models that meet quality criteria are promoted to production, which is a core requirement for continuous delivery in ML pipelines.

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Dataflow job to preprocess data

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Importer component to bring in the model

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A/B testing component to split traffic

Question 6

A data scientist wants to deploy a model trained with PyTorch to a Vertex AI endpoint for online predictions. What is the recommended approach?

Accepted Answer

Package the model in a custom container with a web server (e.g., FastAPI) and deploy to Vertex AI.. Vertex AI requires a custom container for PyTorch models, as it does not provide a pre-built PyTorch serving container. The recommended approach is to package the trained PyTorch model with a web server like FastAPI (or Flask) that loads the model and exposes an HTTP endpoint for predictions. This container is then deployed to Vertex AI for online predictions, giving full control over the inference environment and dependencies.

Answer

Use Vertex AI's pre-built PyTorch container and upload the state dictionary.

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Export the PyTorch model to a SavedModel format and deploy using Vertex AI's pre-built TensorFlow container.

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Convert the model to TensorFlow.js and deploy to Cloud Functions.

Question 7

A company wants to automatically retrain their model every night at 2 AM using Vertex AI Pipelines. Which approach should they use to trigger the pipeline on a schedule?

Accepted Answer

Use Cloud Scheduler to call the Vertex AI pipeline creation API. Cloud Scheduler is the correct approach because it can directly invoke the Vertex AI Pipeline creation API via an HTTP trigger at a specified cron schedule (e.g., 2 AM daily). This integrates natively with Vertex AI's pipeline orchestration, allowing the scheduler to submit a pipeline run without additional infrastructure. The other options either lack native Vertex AI pipeline support or introduce unnecessary complexity.

Answer

Deploy the pipeline as a Cloud Run job with a cron trigger

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Use Vertex AI Experiments to schedule runs

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Configure a cron job inside the pipeline definition

Question 8

A company uses Vertex AI Prediction with a custom container for a TensorFlow model. They notice that after deploying a new model version, requests still go to the old version. What is the most likely cause?

Accepted Answer

Traffic is not split to the new model version. In Vertex AI Prediction, when you deploy a new model version to an existing endpoint, you must explicitly allocate traffic to it. By default, the new version receives 0% traffic, so all requests continue to be served by the old version. The correct fix is to update the endpoint's traffic split, for example via the console or the `gcloud ai endpoints update` command with the `--traffic-split` flag.

Answer

The custom container is not compatible with Vertex AI

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The model is cached and needs cache invalidation

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The new model version was not deployed to the same endpoint

Question 9

A data science team needs to serve multiple versions of the same ML model on Vertex AI Endpoints for A/B testing. They want to gradually shift traffic from the current 'champion' model to a new 'challenger' model. Which feature should they use?

Accepted Answer

Deploy both models to the same endpoint and use traffic splitting.. Vertex AI Endpoints natively support traffic splitting, allowing you to deploy multiple model versions (e.g., champion and challenger) to the same endpoint and assign a percentage of traffic to each. This enables gradual A/B testing without additional infrastructure, as the endpoint automatically routes requests based on the configured split. Option C is correct because it leverages this built-in feature, which is designed specifically for this use case.

Answer

Deploy the challenger to a separate endpoint and use a proxy to split traffic.

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Use Cloud Load Balancing with weighted backend services.

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Use Vertex AI Experiments to manage model versions.

Question 10

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.

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

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Use a complex naming convention for DAG files to avoid overwriting

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Create a separate Cloud Composer environment for each data scientist

Question 11

A data analyst wants to build a binary classification model to predict customer churn using SQL queries in BigQuery. Which BigQuery ML model type should they use?

Accepted Answer

LOGISTIC_REG. BigQuery ML supports LOGISTIC_REG for binary classification via SQL. LINEAR_REG is for regression, K_MEANS for clustering, and MATRIX_FACTORIZATION for recommendations.

Answer

MATRIX_FACTORIZATION

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LINEAR_REG

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K_MEANS

Question 12

A retail company deploys a new recommendation model alongside the current champion on Vertex AI Endpoints. They want to gradually shift traffic to the challenger while monitoring business metrics (conversion rate). Which two steps are required? (Choose 2)

Accepted Answer

Deploy the challenger model to the same endpoint as the champion with a separate deployed model.. Vertex AI Endpoints support deploying multiple model versions (champion and challenger) to the same endpoint, each as a separate deployed model. This allows the endpoint to serve both models simultaneously, enabling traffic splitting without requiring separate endpoints or infrastructure.

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Use Vertex AI Experiments to track the traffic split percentages.

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Enable Cloud Memorystore to cache identical requests for both models.

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Use Cloud Monitoring to track custom metrics like conversion rate per model version.

Question 13

A data scientist deployed a classification model on Vertex AI Endpoints. After a week, the model's accuracy drops significantly from 92% to 78%. The data scientist suspects training-serving skew. What is the first step to confirm this?

Accepted Answer

Compare feature distributions between training and serving data using Vertex AI Model Monitoring. Vertex AI Model Monitoring provides a built-in capability to automatically detect training-serving skew by comparing feature distributions between the training data and the live serving data. This is the most direct and efficient first step to confirm whether the accuracy drop is due to a shift in the input data distribution, which is the hallmark of training-serving skew. The data scientist can set up monitoring jobs that compute statistical distance metrics (e.g., Jensen-Shannon divergence) and alert when significant deviations occur.

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Look for data leakage in the training pipeline

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Examine the feature importance of the model

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Check the prediction confidence over time

Question 14

You are monitoring a machine learning pipeline that runs on Vertex AI Pipelines. The pipeline occasionally fails with a 'ResourceExhausted' error when attempting to read data from BigQuery. Which action should you take to resolve this issue?

Accepted Answer

Reduce the complexity of the BigQuery query or increase the reservation size. The 'ResourceExhausted' error when reading from BigQuery indicates that the query is consuming more resources than the BigQuery reservation allows. Option C is correct because reducing query complexity (e.g., using fewer JOINs, aggregations, or partitions) or increasing the reservation size directly addresses the root cause by either lowering resource demand or allocating more capacity. Other options like switching to Cloud Storage or adjusting pipeline memory do not fix the BigQuery-specific quota or slot exhaustion.

Answer

Switch from BigQuery to Cloud Storage for data source

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Increase the memory allocated to the pipeline step

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Reduce the batch size of the data being read

Question 15

A data science team collaborates using Vertex AI Workbench user-managed notebooks. They want to version control their notebook code and share it with team members. Which TWO tools should they use? (Choose 2)

Accepted Answer

Git integration in Vertex AI Workbench. Git integration in Workbench allows version control, and sharing via Cloud Source Repositories or GitHub provides collaboration.

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Cloud Functions

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Vertex AI Model Registry

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Vertex AI Experiments

Question 16

A company needs to serve a model for low-frequency inference requests (a few hundred per month) from multiple regions. The priority is simplicity and minimal cost without maintaining infrastructure. Which serving option should they choose?

Accepted Answer

Use Vertex AI Batch Prediction triggered as needed.. Vertex AI Batch Prediction runs on-demand jobs without any idle infrastructure cost, making it ideal for low-frequency inference requests (a few hundred per month) from multiple regions. Option A is wrong because a real-time endpoint with min replicas=1 incurs per-hour cost even when idle. Option B is wrong because Dataflow streaming pipelines are designed for continuous, real-time data processing, which adds complexity and cost for infrequent requests. Option D is wrong because Cloud Run scales to zero but is still a real-time serving option designed for online inference, not batch; it also requires containerized application and is not optimized for batch workloads.

Answer

Deploy a real-time Vertex AI Endpoint with min replicas set to 1.

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Set up a Dataflow streaming pipeline to process requests.

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Use Cloud Run with serving container and scale to zero.

Question 17

An ML engineer is building a monitoring dashboard for a Vertex AI pipeline that includes training, evaluation, and batch prediction. Which THREE components should be included to provide comprehensive observability? (Select THREE.)

Accepted Answer

Pipeline execution status, duration, and failure rates for each component.. Pipeline execution status, duration, and failure rates are fundamental metrics for monitoring the health and performance of a Vertex AI pipeline. These metrics allow the ML engineer to quickly identify bottlenecks, track overall workflow progress, and detect failures in training, evaluation, or batch prediction steps, which is essential for comprehensive observability.

Answer

Compute engine CPU and memory logs for each pipeline step.

Answer

Online prediction latency and request count from the deployed model endpoint.

Question 18

Refer to the exhibit. A user is trying to upload a Vertex AI pipeline definition. The error indicates an invalid dependency order. What should the user do to fix this?

Accepted Answer

Reorder the tasks in the YAML so that task1 is defined before task2.. Vertex AI pipeline definitions require that tasks be declared in the order they appear in the dependency graph. The YAML parser validates the `dependentTasks` field by checking that referenced tasks are already defined. Defining `task1` before `task2` ensures that when `task2` declares a dependency on `task1`, `task1` is already in scope, resolving the invalid dependency order error.

Answer

Rename task1 to a name that comes alphabetically before task2.

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Change the dependency of task2 to be independent of task1.

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Remove the dependentTasks field from task2 and rely on implicit ordering.

Question 19

You are designing an ML pipeline for a large-scale recommendation system that runs weekly retraining on historical user interaction data. The pipeline uses TensorFlow and is deployed on Google Cloud. The pipeline must be orchestrated and automated with minimal manual intervention. Which THREE options should you include in your design? (Choose three.)

Accepted Answer

Use Vertex AI Pipelines to define the ML pipeline as a Directed Acyclic Graph (DAG) of components.. Vertex AI Pipelines (option B) is correct because it provides a managed, serverless orchestration service for building, testing, and deploying ML pipelines as Directed Acyclic Graphs (DAGs). This directly supports the requirement for automated, minimal-intervention weekly retraining by allowing you to define reusable components and schedule pipeline runs via Cloud Scheduler or event triggers, integrating natively with TensorFlow and Google Cloud services.

Answer

Use BigQuery scheduled queries to run the training script on a schedule.

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Use AI Platform Notebooks to schedule the training job on a recurring basis.

Question 20

You have a very large language model that does not fit on a single GPU. You need to train it efficiently across multiple GPUs on a single machine. Which approach should you use?

Accepted Answer

Model parallelism using pipeline parallelism. When a model is too large for a single GPU, model parallelism (specifically pipeline parallelism) splits the model across GPUs. Data parallelism would require each GPU to hold the full model, which is not possible.

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Data parallelism with MirroredStrategy

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Data parallelism with MultiWorkerMirroredStrategy

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Use TPU training as TPUs have more memory