PMLE Practice Exam 58 — 20 Questions

Question 1

A data engineer is setting up a data pipeline for ML training. The raw data is in Cloud Storage, and they need to transform it into features stored in Vertex AI Feature Store. The pipeline should run daily. Which service should they use?

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

Accepted Answer

Dataflow with Apache Beam pipeline.. Dataflow with Apache Beam is the correct choice because it provides a fully managed, serverless service for both batch and streaming data processing, which is ideal for transforming raw data from Cloud Storage into features for Vertex AI Feature Store on a daily schedule. Dataflow handles auto-scaling, exactly-once processing, and integrates natively with Google Cloud services, making it efficient for ETL pipelines that need to run reliably at scale.

Answer

Cloud Composer with Airflow DAG.

Answer

Cloud Dataproc with Spark.

Answer

Vertex AI Pipelines with custom components.

Answer

Cloud Functions on a schedule.

Question 2

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 3

A team has set up the IAM policy above on a Vertex AI project. Alice, a data scientist, reports that she cannot create a Vertex AI Training custom job using a pre-built container. Other data scientists in the group 'data-scientists@example.com' have the same issue. What is the most likely cause?

Accepted Answer

The 'roles/aiplatform.user' role does not grant the permission to create custom training jobs.. The 'roles/aiplatform.user' role does not include the 'aiplatform.customJobs.create' permission needed to create custom training jobs. To create custom training jobs, users need the 'roles/aiplatform.customJobUser' role. The correct answer is A because Alice and her group only have the basic user role, which lacks the necessary permission.

Answer

The Vertex AI Custom Code Service Agent service account is missing the 'roles/aiplatform.user' role.

Answer

Alice is not included in the 'data-scientists@example.com' group.

Answer

The service account 'vertex-ai@project.iam.gserviceaccount.com' does not have permission to access the training data.

Question 4

Two teams independently develop two different versions of a model for the same use case. They both deploy to the same Vertex AI endpoint, causing conflicts. What is the best way to manage multiple model versions and avoid conflicts in a collaborative environment?

Accepted Answer

Use Vertex AI Model Registry with staging and production channels, and implement CI/CD to control promotions. Vertex AI Model Registry with staging and production channels provides a centralized system to manage model versions, track lineage, and control promotions via CI/CD pipelines. This prevents conflicts by enforcing a structured workflow for version updates. Option A is wrong because separate projects increase management overhead and do not address versioning within the same endpoint. Option B is wrong because custom metadata lacks enforcement of deployment order and can lead to manual errors. Option C is wrong because deploying to separate endpoints does not resolve version conflicts; it merely isolates models, increasing complexity and cost.

Answer

Have each team work on a separate Google Cloud project

Answer

Use custom metadata to tag each version and rely on team coordination

Answer

Deploy each team's model to a separate endpoint

Question 5

A data scientist needs to share a BigQuery dataset with a colleague in a different team so they can run queries. What is the simplest and most secure way to grant access?

Accepted Answer

Add the colleague's account as a BigQuery Data Viewer on the dataset. Adding the colleague's account as a BigQuery Data Viewer on the dataset grants read-only access to the specific dataset, which is the simplest and most secure method. Option A is wrong because exporting to Cloud Storage adds complexity and data staleness. Option C is wrong because sharing the entire project grants excessive permissions. Option D is wrong because sharing the service account key poses a security risk.

Answer

Export the dataset to Cloud Storage and share the bucket

Answer

Share the service account key of a BigQuery job user with the colleague

Answer

Add the colleague's account as a Project Viewer on the entire project

Question 6

Drag and drop the steps to set up a distributed training job on Vertex AI using a custom container in the correct order.

Accepted Answer

Prepare training code and create Dockerfile, then build and push the container image to Artifact Registry, then configure the distributed training job on Vertex AI, then submit the job.. The correct sequence for setting up a distributed training job on Vertex AI using a custom container is to first prepare the training code and Dockerfile, then build and push the container image to a registry, then configure the job with the image URI and distributed settings, and finally submit the job. This order ensures that all dependencies are met at each step.

Answer

Build and push the container image first, then write the training code and create Dockerfile, then configure the job, then submit.

Answer

Configure the distributed training job on Vertex AI first, then prepare code and container, then push, then submit.

Answer

Submit the job first, then build and push the container, then write code, then configure.

Question 7

A recommendation system model is updated daily via a retraining pipeline. After each update, the online prediction latency increases significantly for about 30 minutes before returning to normal. What is the most likely cause and solution?

Accepted Answer

The new model version causes cold start in the serving infrastructure; pre-warm the model by sending a dummy request after deployment.. The most likely cause is a cold start when the new model version is deployed. The serving infrastructure needs to load the model into memory, which takes time and causes increased latency for the first requests. Pre-warming the model by sending dummy requests after deployment can mitigate this. Option A is incorrect because autoscaling policy affecting scale-down would not cause a temporary spike after each update; it would be more persistent. Option B is incorrect because GKE cluster sharing resources would cause consistent latency issues, not just for 30 minutes after update. Option C is incorrect because CPU-to-GPU switching is not a typical cause for temporary latency spikes.

Answer

The Vertex AI endpoint autoscaling policy is too aggressive, causing scale-down during retraining.

Answer

The retraining pipeline runs on a GKE cluster that shares resources with the serving endpoint.

Answer

The model is being switched from CPU to GPU at deployment.

Question 8

A retail company wants to build a customer churn prediction model using BigQuery ML. The data is stored in BigQuery tables and includes customer demographics, purchase history, and support interactions. The data scientist wants to experiment with different model types quickly without moving data to another environment. Which approach should they use?

Accepted Answer

Use BigQuery ML to create and evaluate models directly in BigQuery.. BigQuery ML (BQML) allows data scientists to create, train, and evaluate machine learning models directly in BigQuery using SQL, without moving data to another environment. This approach supports rapid experimentation with various model types (e.g., logistic regression, boosted trees, deep neural networks) and is ideal for the stated requirement of quick iteration while keeping data in place.

Answer

Use Cloud Composer to orchestrate a custom training pipeline on Vertex AI.

Answer

Use AI Platform Notebooks with pandas and scikit-learn.

Answer

Export the data to Cloud Storage and use Vertex AI AutoML Tables.

Question 9

What does the `ML.PREDICT` command do in BigQuery ML?

Accepted Answer

Makes predictions using the model. The command is likely a BigQuery ML prediction query (e.g., using `ML.PREDICT`) that uses a trained model to generate predictions on new input data, making option D correct. It does not train, export, or evaluate the model.

Answer

Trains a new BigQuery ML model

Answer

Exports the model to Cloud Storage

Answer

Evaluates the model's performance

Question 10

A manufacturing company wants to predict equipment failure using sensor data stored in BigQuery. They have limited ML expertise and want to use AutoML Tables. The data includes timestamps, numerical sensor readings, and a boolean 'failure' column. The dataset is highly imbalanced with only 1% failure cases. Which of the following is the most effective approach to handle the imbalance in AutoML Tables?

Accepted Answer

Let AutoML Tables handle the imbalance automatically; it has built-in techniques for class imbalance.. AutoML Tables has built-in techniques to handle class imbalance, such as automatically adjusting class weights and using stratified sampling during training. This allows the model to learn from the minority class without requiring manual data preprocessing, making it the most effective and simplest approach for users with limited ML expertise.

Answer

Downsample the majority class to balance the dataset.

Answer

Use a custom loss function in the training configuration.

Answer

Oversample the minority class using SQL before training.

Question 11

Match each ML acronym to its definition.

Accepted Answer

ML: A subset of AI that enables systems to learn from data.. The correct matches are A (ML), B (AI), C (NLP), and D (CNN). Options E and F are incorrect because they swap definitions: E assigns the definition of AI to ML, and F assigns the definition of CNN to NLP.

Answer

ML: The simulation of human intelligence by machines.

Answer

NLP: A type of neural network designed for processing grid-like data, especially images.

Question 12

A company deploys a model on Vertex AI Prediction with autoscaling enabled. They notice that during a traffic spike, new instances take several minutes to become available, causing high latency. What is the best solution?

Accepted Answer

Set a higher min replicas to maintain a baseline of warm instances. Setting a higher min replicas ensures that a baseline number of instances are always warm and ready to serve traffic. During a traffic spike, new instances still take time to provision (cold start), but the warm instances handle the initial surge without latency spikes. This directly addresses the observed high latency during spikes.

Answer

Disable autoscaling and use a fixed number of replicas

Answer

Increase the max replicas setting

Answer

Decrease the machine type to reduce provisioning time

Question 13

A company wants to use ML to predict customer churn. They have user activity logs in Cloud Storage, account data in BigQuery, and want an automated pipeline. Which pipeline architecture on Google Cloud should they use?

Accepted Answer

Use BigQuery to join logs and account data, train on Vertex AI, deploy to an endpoint. It leverages BigQuery's ability to join structured account data with semi-structured logs (via federated queries or external tables), then uses Vertex AI for end-to-end ML training and deployment. This architecture minimizes data movement, keeps the pipeline serverless, and directly addresses the requirement for an automated pipeline with both data sources.

Answer

Load both data sources into AutoML Tables and train directly

Answer

Export logs from Cloud Storage to Cloud Dataproc for preprocessing, then train

Answer

Use Cloud Functions to preprocess data, then train on AI Platform

Question 14

A machine learning engineer is building a Vertex AI pipeline that uses a pre-built AutoML Tables component to train a classification model. The pipeline also includes a conditional step that deploys the model to an endpoint only if the evaluation metrics exceed a threshold. Which KFP feature should be used to implement the conditional deployment?

Accepted Answer

dsl.Condition. The `dsl.Condition` feature from KFP (Kubeflow Pipelines) is specifically designed to conditionally execute pipeline steps based on the output of a previous component. In this scenario, the AutoML Tables component produces evaluation metrics; `dsl.Condition` allows the pipeline to check whether those metrics exceed a threshold and, if true, run the deployment step. This is the correct, native KFP construct for implementing branching logic within a pipeline.

Answer

dsl.ParallelFor

Answer

dsl.ExitHandler

Answer

dsl.Collected

Question 15

After setting up model monitoring on Vertex AI for a classification model, the engineer sees a high number of anomaly alerts for the "age" feature. Upon investigation, the age distribution in recent predictions is similar to training data. What might be the cause?

Accepted Answer

The monitoring threshold for age is too low. The high number of anomaly alerts despite the age distribution being similar to training data indicates that the monitoring threshold for the 'age' feature is set too low. In Vertex AI Model Monitoring, anomaly detection compares recent prediction distributions against a baseline using statistical tests (e.g., the Kolmogorov-Smirnov test for numerical features). If the threshold is too sensitive, even minor, statistically insignificant deviations can trigger alerts, leading to false positives even when the distribution is essentially unchanged.

Answer

The feature importance of age has changed

Answer

The monitoring baseline was incorrectly set

Answer

The model is overfitting to age

Question 16

You are setting up a hyperparameter tuning job on Vertex AI for a large neural network. The objective is to minimize validation loss. You want to explore the hyperparameter space efficiently with a limited budget of 100 trials. Which THREE settings should you configure in the study?

Accepted Answer

Enable early stopping. Enabling early stopping in Vertex AI hyperparameter tuning terminates poorly performing trials early, saving the trial budget for more promising hyperparameter configurations. This is critical when the objective is to minimize validation loss with a limited budget of 100 trials, as it prevents wasting resources on suboptimal runs and allows the search to focus on the most promising regions of the hyperparameter space.

Answer

Algorithm: GRID_SEARCH

Answer

Disable early stopping

Question 17

A large e-commerce company deploys multiple ML models on Vertex AI Endpoints. They use Vertex AI Model Registry to manage model versions. Recently, a team accidentally deployed an unvalidated model to production, causing a service outage. They want to implement a governance process where models must pass certain validation checks before deployment. The validation includes unit tests, fairness checks, and performance benchmarks. They use CI/CD pipelines (Cloud Build). They also need to allow manual approval for critical models. Which combination of Vertex AI features and Cloud Build steps would enforce the required governance?

Accepted Answer

Implement Cloud Build triggers that run validation steps, then use Vertex AI Model Registry 'state' to mark models as 'validated' before allowing deployment to endpoints.. It combines Cloud Build triggers to run validation steps (unit tests, fairness checks, performance benchmarks) and uses Vertex AI Model Registry's 'state' field to mark models as 'validated' only after passing those checks. This state then acts as a gate in the deployment pipeline, ensuring that only validated models can be deployed to Vertex AI Endpoints. The manual approval for critical models can be integrated as a Cloud Build approval step before the state is set to 'validated'.

Answer

Use Vertex AI Experiments to log validation results and require manual checks before deployment.

Answer

Set up Cloud Armor to block deployment of unvalidated models.

Answer

Use Vertex AI Continuous Monitoring to automatically detect issues and roll back deployments.

Question 18

An ML engineer is using Vertex AI Pipelines with Kubeflow Pipelines SDK (KFP) to orchestrate a training and deployment workflow. They want to reuse a custom component across multiple pipelines. The component is defined in a Python file 'preprocess.py' that includes a function decorated with @kfp.components.create_component_from_func. How should they package this component for reuse?

Accepted Answer

Import the preprocess module and call create_component_from_func on the function, then use the resulting component in pipeline definitions.. The recommended way to reuse a custom component defined via `@kfp.components.create_component_from_func` is to import the Python module containing the decorated function and call `create_component_from_func` on that function in each pipeline definition. This creates a reusable component object that can be used directly in the pipeline's `@dsl.pipeline` definition without additional packaging steps. The KFP SDK treats the function as the source of truth, and re-importing ensures the component logic is always current.

Answer

Save the component as a YAML file using kfp.components.ComponentStore and load it in other pipelines.

Answer

Compile the pipeline that uses the component into a JSON file and upload it to Vertex AI.

Answer

Build a custom container image with the function and use it as a base image in other pipelines.

Question 19

A data science team uses a shared Cloud Storage bucket to store training datasets. They notice that some team members accidentally overwrite existing datasets, causing issues with reproducibility. Which approach best prevents accidental overwrites while maintaining collaboration?

Accepted Answer

Enable object versioning on the bucket and use lifecycle rules to manage versions.. Enabling object versioning on a Cloud Storage bucket preserves all versions of an object, so even if a team member overwrites a dataset, the previous version remains accessible. This maintains collaboration (anyone can upload) while preventing permanent data loss. Lifecycle rules can then be used to manage storage costs by automatically deleting old versions after a specified period.

Answer

Use a single shared service account with strict IAM roles that allow only append operations.

Answer

Require team members to manually rename files before uploading.

Answer

Set bucket permissions to read-only for all team members except the data owner.

Question 20

You have a Vertex AI endpoint with two deployed models: a champion (v1) and a challenger (v2). You set the traffic split to 90% v1 and 10% v2. After a week, you observe that v2 has better business metrics. You want to shift all traffic to v2 gradually over 3 days to avoid any risk. What should you do?

Accepted Answer

Update the traffic split configuration on the endpoint multiple times over the 3 days to gradually increase v2's percentage.. Vertex AI endpoints support live traffic splitting between deployed models, allowing you to gradually shift traffic from v1 to v2 by updating the traffic split configuration multiple times over the 3-day period. This approach minimizes risk by enabling incremental rollouts and immediate rollback if issues arise, without requiring client-side changes or downtime.

Answer

Deploy v2 to a new endpoint and update your clients to use the new endpoint.

Answer

Use Vertex AI Experiments to compare v1 and v2, then redeploy v2 with 100% traffic.

Answer

Delete v1 from the endpoint so that all traffic automatically goes to v2.