PMLE Practice Test 28 — 15 Questions

Question 1

A machine learning engineer notices that the online prediction latency for a custom TensorFlow model deployed on Vertex AI has increased significantly over the past week. Cloud Monitoring shows that the CPU utilization of the endpoints remains below 40%, but the number of concurrent requests has doubled. What is the most likely cause of the latency increase?

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

Accepted Answer

Insufficient number of replicas for autoscaling. The CPU utilization remains below 40% while concurrent requests have doubled, indicating that the existing replicas are not saturated on CPU but are bottlenecked by request queuing or thread contention. Vertex AI autoscaling scales based on CPU utilization by default; if the threshold is not crossed, new replicas are not provisioned, causing requests to queue and latency to spike. The engineer should verify the autoscaling configuration and consider scaling on request count or reducing the CPU utilization target.

Answer

Data skew causing longer inference time

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Memory leak in the serving container

Answer

Model overfitting

Question 2

You need to run batch predictions on 10 TB of text data stored in BigQuery using a custom container model hosted in Vertex AI. What is the most cost-effective and simple approach?

Accepted Answer

Use Vertex AI batch prediction with BigQuery source and sink.. Vertex AI batch prediction natively supports BigQuery as both source and sink, allowing you to run predictions on 10 TB of text data without any data movement or intermediate storage. This is the most cost-effective and simple approach because it eliminates the need for exporting data, managing infrastructure, or calling online endpoints, and it leverages Vertex AI's optimized batch inference infrastructure that scales automatically.

Answer

Use Cloud Run jobs to read from BigQuery and write results back.

Answer

Export BigQuery data to GCS, then run a Dataflow pipeline to call the model's online prediction endpoint for each row.

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Use Cloud Dataproc to spin up a Spark cluster and run the model inference in parallel.

Question 3

A team uses Cloud Composer to orchestrate a complex ML pipeline with many tasks. They notice that the DAG parsing time is very high, causing delays in task scheduling. Which action would most effectively reduce DAG parsing time?

Accepted Answer

Optimize DAG files to avoid heavy top-level imports and database queries. Heavy top-level imports and database queries in DAG files are executed every time the scheduler parses the DAG, which happens frequently (default every 30 seconds). By moving imports inside Python callables or using lazy loading, the parsing time is drastically reduced, allowing the scheduler to process DAGs faster and trigger tasks without delay.

Answer

Remove all DAG files that are not currently needed from the bucket

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Increase the parallelism of the Airflow scheduler

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Combine all DAGs into a single file

Question 4

A data analyst wants to use Vision API to detect custom objects in manufacturing images, but the pre-trained API does not recognize their specific components. They have 1000 labeled images. Which path offers the fastest time-to-value with minimal coding?

Accepted Answer

Use AutoML Vision for object detection. AutoML Vision for object detection is the fastest path because it requires no custom coding—users simply upload labeled images, and the platform automatically trains a model tailored to their custom components. This directly addresses the need to detect objects the pre-trained Vision API cannot recognize, while minimizing time-to-value compared to manual TensorFlow training or custom infrastructure setup.

Answer

Store images in BigQuery and use ML.PREDICT with a custom model

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Use a Cloud Function to call the Vision API and post-process results

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Train a custom object detection model using TensorFlow on Vertex AI

Question 5

Which TWO options are best practices for building ML pipelines on Vertex AI?

Accepted Answer

Use custom container components to encapsulate reusable logic. Option C is correct because custom container components let you package arbitrary dependencies, libraries, and code into a portable, versioned image that Vertex AI Pipelines can reuse across multiple pipelines, which is a recommended way to encapsulate and share reusable logic. Option E is correct because Vertex ML Metadata automatically records artifacts, executions, and their lineage for Vertex AI Pipelines runs, enabling reproducibility, auditing, and comparison of models and datasets across experiments. Option A is not a best practice because Cloud Functions are event-driven, short-lived, and not designed to run heavy or stateful ML pipeline steps; Vertex AI Pipelines components (or Vertex AI Training jobs) should execute those steps. Option B is wrong because hardcoding parameters in component definitions reduces reusability and makes experimentation and CI/CD harder; parameters should be passed at pipeline submission time via PipelineJob runtime parameters. Option D is wrong because training and serving often require different compute profiles (for example, GPUs for training versus optimized CPUs or different accelerators for serving), and consistency is achieved through containerized artifacts and pinned dependencies, not by forcing identical compute environments.

Answer

Use Cloud Functions to execute individual pipeline steps

Answer

Hardcode pipeline parameters in the component definitions

Answer

Always use the same compute environment for training and serving to ensure consistency

Question 6

A data science team wants to build a machine learning pipeline on Vertex AI Pipelines that preprocesses data, trains a model, and evaluates it. They need to ensure that components can be reused across multiple pipelines and that outputs from one component can be passed as inputs to another. Which approach should they take?

Accepted Answer

Use Kubeflow Pipelines SDK v2 to create Python function components decorated with @dsl.component and compose them into a pipeline using @dsl.pipeline.. Kubeflow Pipelines SDK v2 with @dsl.component and @dsl.pipeline decorators is the native way to define reusable, composable components in Vertex AI Pipelines. This approach allows each component to be a self-contained Python function that can be independently versioned and reused across multiple pipelines, with outputs automatically serialized and passed as inputs to downstream components via the pipeline graph.

Answer

Write each component as a Cloud Composer DAG task using Python operators and manage dependencies via Airflow.

Answer

Use Vertex AI pre-built components exclusively and chain them using the Vertex AI SDK without a pipeline definition.

Answer

Define each step as a separate Cloud Build step and chain them via build triggers.

Question 7

A machine learning pipeline in Vertex AI produces a dataset artifact, a trained model, and evaluation metrics. The team wants to query the lineage to find all downstream artifacts that depend on a particular dataset. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata is the service that records and stores ML metadata — artifacts, executions, and contexts — and their relationships, forming the lineage graph. It lets you query upstream and downstream dependencies of any artifact, such as finding all models and metrics derived from a dataset. This is exactly the lineage-query capability the team needs.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

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Vertex AI Model Registry

Question 8

Match each feature engineering technique to its description.

Accepted Answer

Polynomial Features: Creates new features as powers of existing features.. This matching question tests your understanding of common feature engineering techniques. Polynomial Features create new features by raising existing features to a power (e.g., x², x³). One-Hot Encoding converts categorical variables into binary columns (0/1) for each category. Feature Scaling standardizes numerical features to have zero mean and unit variance (z-score normalization). Options D and E are incorrect: D confuses Polynomial Features with Label Encoding (which assigns numerical codes), and E confuses One-Hot Encoding with Polynomial Features.

Answer

Polynomial Features: Converts categorical variables into numerical codes.

Answer

One-Hot Encoding: Creates polynomial combinations of features.

Question 9

A company has an existing TensorFlow model for fraud detection that they want to use for predictions in BigQuery. They want to call the model from SQL queries without moving data out of BigQuery. How should they deploy the model?

Accepted Answer

Import the TensorFlow model directly into BigQuery ML. BigQuery ML (BQML) natively supports importing TensorFlow models directly, allowing you to use them for predictions via SQL without moving data out of BigQuery. This is the simplest and most efficient approach because it eliminates the need for external services or data export, leveraging BQML's built-in `CREATE MODEL` statement with the `OPTIONS(model_type = 'TENSORFLOW')` clause.

Answer

Deploy the model to Vertex AI Prediction and use a remote model in BigQuery ML

Answer

Export BigQuery data to Cloud Storage and use AI Platform Prediction

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Use AutoML Tables to retrain the model in BigQuery ML

Question 10

You are designing a distributed training job on Vertex AI for a PyTorch model using DataDistributedParallel (DDP). You have 4 nodes, each with 4 GPUs. What is the total number of workers that should be configured in the TF_CONFIG equivalent for PyTorch?

Accepted Answer

16. In PyTorch DDP on Vertex AI, the number of workers equals the total number of processes across all nodes, which is nodes × GPUs per node. With 4 nodes and 4 GPUs each, the total is 16 workers, so the TF_CONFIG-equivalent worker count should be 16. Each GPU runs one DDP process, and all 16 processes participate in the all-reduce synchronization.

Answer

4

Answer

8

Answer

1

Question 11

Two teams are collaborating on a project and want to use a shared Feature Store in Vertex AI. They need to ensure that features are discoverable and that access is controlled. What is the best practice?

Accepted Answer

Use Vertex AI Feature Store and grant appropriate IAM roles to each team. Vertex AI Feature Store provides a managed service for sharing features with access controls via IAM roles and enables feature discovery through the UI and API. Option A is wrong because CSV files in Cloud Storage lack feature store metadata, versioning, and online serving capabilities. Option B is wrong because building a custom pipeline with Dataflow and storing in Cloud SQL is not a managed feature store solution and does not provide the same discovery or access control features. Option C is wrong because each team maintaining their own BigQuery table does not offer centralized feature discovery or unified access control; a Feature Store centralizes metadata and permissions.

Answer

Export features to CSV files in Cloud Storage and share the bucket

Answer

Build a custom feature pipeline using Dataflow and store in Cloud SQL

Answer

Each team stores features in their own BigQuery table and shares the table

Question 12

A company has a TensorFlow model trained outside of Google Cloud and wants to use it for online predictions on Vertex AI. They have saved the model in SavedModel format. What is the most efficient way to deploy this model?

Accepted Answer

Upload the saved model to Vertex AI and create an endpoint for online predictions. The most efficient way to deploy a TensorFlow SavedModel for online predictions on Vertex AI is to upload the SavedModel to Vertex AI and create an endpoint. Vertex AI supports importing custom models in SavedModel format and deploying them to endpoints for low-latency online predictions. This leverages Vertex AI's managed infrastructure for scaling and serving.

Answer

Import the model into BigQuery ML using CREATE MODEL with model_type='TENSORFLOW'

Answer

Use Vertex AI AutoML Tables to retrain the model

Answer

Use Cloud Functions to run the model for each prediction request

Question 13

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.

Answer

The bucket does not exist

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The pipeline runner is incorrect

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The region is mismatched

Question 14

An organization uses Vertex AI Pipelines and wants to track the lineage of datasets, models, and metrics across pipeline runs. They need to query upstream and downstream dependencies of an artifact. Which service should they use?

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata stores the ML metadata graph produced by Vertex AI Pipelines, including artifacts (datasets, models, metrics), executions, and their input/output relationships. It exposes APIs to traverse this graph in both directions, so you can query upstream sources and downstream dependents of any artifact. This is the correct service for cross-run lineage queries.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Vertex AI Model Registry

Question 15

A company needs to serve a model with strict latency requirements (<100ms). They are using Vertex AI Prediction with CPU. During testing, latency is 150ms. What should they do?

Accepted Answer

Switch to a GPU machine type. The model's latency of 150ms exceeds the 100ms requirement. Switching to a GPU machine type (Option D) is correct because GPUs are optimized for parallel computation, significantly reducing inference latency for many ML models, especially deep learning models, compared to CPUs. Vertex AI Prediction supports GPU machine types, and this change directly addresses the latency bottleneck without altering the model or its serving configuration.

Answer

Enable batching to improve throughput

Answer

Use a smaller machine type with more replicas

Answer

Export the model to TensorFlow Lite