PMLE Practice Exam 51 — 20 Questions

Question 1

A machine learning team uses Vertex AI Pipelines to orchestrate training workflows. They want to share pipeline runs and artifacts with stakeholders who do not have Google Cloud accounts. What should they do?

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

Accepted Answer

Generate a pipeline run report using the Vertex AI Pipelines SDK and export it as a static HTML file to share.. Exporting a pipeline run report as a static HTML file using the Vertex AI Pipelines SDK enables sharing with stakeholders who lack Google Cloud accounts. This method provides a comprehensive view of the run's artifacts and metrics without requiring access to the cloud environment, ensuring both security and accessibility.

Answer

Use Vertex AI Pipelines' built-in sharing feature to generate a public URL for the pipeline run.

Answer

Export the pipeline run's metadata and artifacts to a Cloud Storage bucket and grant public access.

Answer

Create a custom dashboard in Looker Studio that reads from Vertex ML Metadata and share it with the stakeholders.

Question 2

A marketing team wants to build a model that predicts customer lifetime value (CLV) using historical transaction data. They are comfortable with spreadsheets but have no coding experience. They need a low-code solution that automatically handles feature engineering and model selection. Which Google Cloud service should they use?

Accepted Answer

Vertex AI AutoML. Vertex AI AutoML is designed for users with limited ML expertise to build models without writing code. It automates feature engineering, model selection, and hyperparameter tuning. For predicting CLV from historical data, the marketing team can simply upload a dataset and let AutoML train a regression model. Other options require SQL or Python coding, which the team lacks.

Answer

AI Platform Training

Answer

BigQuery ML

Answer

Vertex AI Workbench

Question 3

You are training a TensorFlow model on Vertex AI using a custom container with a single Tesla T4 GPU. You notice that training is slower than expected, and GPU utilization is consistently below 20%. Profiling shows that the input pipeline is the bottleneck. Which change should you make to improve GPU utilization?

Accepted Answer

Use tf.data with prefetching and parallel data extraction to overlap data loading with GPU computation.. When the input pipeline is the bottleneck, the GPU waits for data. Using tf.data with prefetching and parallel extraction overlaps CPU preprocessing with GPU computation, ensuring the GPU is continuously fed. This is the standard TensorFlow optimization for such scenarios and directly improves GPU utilization.

Answer

Switch to a larger GPU instance with more memory to reduce data loading overhead.

Answer

Move the dataset to a local SSD on the training VM to reduce I/O latency.

Answer

Increase the batch size to fully utilize GPU memory.

Question 4

You are deploying a custom model to Vertex AI for online prediction. The model requires a preprocessing step that normalizes input features. You want to ensure that the same preprocessing is applied during both training and serving to avoid training-serving skew. What should you do?

Accepted Answer

Export the preprocessing as a TensorFlow Transform (tf.Transform) function and include it in both training and serving graphs.. Using TensorFlow Transform (tf.Transform) to define preprocessing ensures that the same transformations are applied during both training and serving. The transform function is included in the model graph, so the serving container automatically applies the same logic. This eliminates manual replication and reduces the risk of training-serving skew.

Answer

Use Vertex AI Feature Store to serve features and apply preprocessing at training time only.

Answer

Perform preprocessing on the client side before sending data to the model for prediction.

Answer

Include the preprocessing logic in the training script and replicate it in the serving container.

Question 5

An ML engineer is running a Vertex AI pipeline that includes a data validation component and a training component. The engineer wants the pipeline to stop before training if data validation fails, but wants the validation component to record its result as an output artifact for later inspection. Which combination of pipeline features should the engineer use?

Accepted Answer

Have the validation component raise an exception on failure and write a validation report artifact before raising.. In Vertex AI Pipelines, a task that raises an exception fails, and dependent tasks are not executed. Writing the validation report artifact before raising ensures the artifact is captured for later inspection. The other options either let training proceed, rely on retries that do not address deterministic data failures, or move the validation check into the training component, none of which stop the pipeline before training while preserving the report.

Answer

Configure the validation component with a retry policy of 3 so transient validation errors are retried, then let training proceed.

Answer

Use a dsl.Condition to skip training when validation fails, and have the validation component always succeed while writing the report.

Answer

Have the validation component write the report artifact and return a success status, then check the artifact contents in the training component before training.

Question 6

A team is using Vertex AI Pipelines to orchestrate a machine learning workflow. They want to ensure that the pipeline can be reproduced and that artifacts are tracked. Which two of the following practices should they follow? (Choose two.)

Accepted Answer

Version all pipeline components and store them in a version control system.. Versioning pipeline components and using Vertex AI Metadata are key practices for reproducibility and artifact tracking. Versioning ensures that the exact code and dependencies are captured, while Metadata automatically records parameters, metrics, and artifacts for each run, providing lineage and enabling reproducibility.

Answer

Use the same container image tags for all pipeline steps to ensure consistency.

Answer

Manually copy all artifacts to a Cloud Storage bucket after each run.

Answer

Disable caching for all pipeline steps to ensure fresh executions.

Question 7

An ML engineer has a prototype that trains a TensorFlow model on a single CPU machine using Vertex AI custom training. The job now needs to train on a larger dataset and must use multiple GPUs on one machine. The training script already uses tf.distribute.MirroredStrategy. What change is required to scale the job?

Accepted Answer

Set the worker pool machine type to a GPU machine and specify the number of accelerators and accelerator type.. Scaling a single-machine TensorFlow job that already uses MirroredStrategy to multiple GPUs is primarily a resource configuration task. You set the machine type to a GPU-capable machine and specify the accelerator type and count in the worker pool spec. The strategy code detects the local GPUs automatically. Multi-worker strategies, TPU strategies, and disk or image changes are not required for this scenario.

Answer

Add a second worker pool with one replica and set the distribution strategy to MultiWorkerMirroredStrategy.

Answer

Enable TPU training by specifying a TPU machine type and changing the script to use TPUStrategy.

Answer

Increase the boot disk size and set the training container to use the GPU-enabled base image.

Question 8

A company runs a Vertex AI Pipeline that includes a custom component for hyperparameter tuning. The component uses a large search space and runs many trials. The pipeline is taking too long to complete, and the team wants to reduce the execution time without sacrificing model quality. They have already optimized the training code. Which Vertex AI Pipelines feature should they use to speed up the tuning component?

Accepted Answer

Use the ParallelFor loop in Vertex AI Pipelines to run multiple trials concurrently as separate tasks.. ParallelFor in Vertex AI Pipelines enables concurrent execution of independent tasks, which is well-suited for hyperparameter tuning trials. By running multiple trials in parallel, the overall tuning time is significantly reduced while maintaining the same search space and model quality. This leverages the pipeline's orchestration capabilities and is more effective than caching or increasing machine type.

Answer

Enable pipeline caching for the tuning component so that repeated runs with identical parameters are skipped.

Answer

Reduce the number of trials by using a random search instead of a grid search.

Answer

Increase the machine type of the tuning component to a higher CPU or GPU configuration.

Question 9

A team is using Vertex AI Model Registry to manage models. They need to ensure that when a new model version is registered, it is automatically evaluated for fairness and bias before being deployed. Which two Google Cloud services should they integrate to achieve this? (Choose two.)

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines can orchestrate an automated workflow triggered by model registration, and Vertex AI Model Evaluation provides the fairness and bias metrics. Together, they enable pre-deployment evaluation. Other services like Model Monitoring and Feature Store focus on different aspects and cannot perform fairness assessment at registration time.

Answer

Vertex AI Feature Store

Answer

Cloud Build

Answer

Vertex AI Model Monitoring

Question 10

You have a Vertex AI endpoint serving a model that returns predictions in about 200 ms. During a marketing campaign, traffic increases tenfold for short bursts. You want the endpoint to handle the bursts without manual intervention and without over-provisioning for the entire day. What should you do?

Accepted Answer

Configure autoscaling on the endpoint with a minimum replica count of one and a maximum replica count that covers peak load.. Vertex AI endpoints support autoscaling based on metrics such as CPU utilization or request concurrency. By setting a low minimum replica count, you keep baseline cost low during normal periods. A maximum replica count sized for peak load allows the endpoint to scale out automatically when traffic increases tenfold. This elastic behavior handles short bursts without manual intervention and avoids paying for peak capacity all day, which is exactly what the scenario requires.

Answer

Increase the machine type to a larger instance with more memory and vCPUs.

Answer

Deploy the model to a batch prediction job and schedule it to run every hour.

Answer

Enable request logging and set up an alert when latency exceeds a threshold.

Question 11

You are training a scikit-learn random forest model on a large tabular dataset using a Vertex AI custom training job. The training script reads a CSV file from a Cloud Storage bucket and writes the trained model artifact to the same bucket. You need to ensure the training job can access the Cloud Storage bucket without embedding long-lived credentials in the container. What should you do?

Accepted Answer

Attach a custom service account to the Vertex AI training job that has the Storage Object Viewer and Storage Object Creator roles on the bucket, and let the Vertex AI Training service use the service account's credentials automatically.. The correct approach is to attach a custom service account with least-privilege Cloud Storage roles to the Vertex AI training job. Vertex AI training jobs automatically use the credentials of the attached service account via the metadata server, so no key files are needed. This ensures secure, short-lived authentication and follows Google Cloud best practices for access control.

Answer

Grant the Vertex AI Service Agent role to the default Compute Engine service account and rely on the default credentials provided by the training environment to access the bucket.

Answer

Configure the training job to use a user-managed service account that has the Editor role on the project, ensuring it has sufficient permissions to read and write to any Cloud Storage bucket.

Answer

Create a service account key file, store it in Secret Manager, and mount it into the training container as a volume so the scikit-learn script can read the key and authenticate to Cloud Storage.

Question 12

An ML engineer is preparing to train a large model on Vertex AI using a custom training job. The training data is stored in a Cloud Storage bucket as a set of TFRecord files. The engineer wants to optimize the training job to reduce cost and improve performance. Which two actions should the engineer take? (Choose two.)

Accepted Answer

Use a TPU or GPU accelerator and ensure the input pipeline is optimized to keep the accelerator busy.. To optimize training cost and performance, the engineer should use accelerators and ensure the input pipeline is efficient, and store data in the same region as the training job. These actions reduce training time and avoid unnecessary network overhead. Other options like using broad service accounts or increasing steps do not contribute to efficiency and may introduce security or cost issues.

Answer

Enable Vertex AI Model Monitoring on the training job to detect anomalies during training.

Answer

Use the default Compute Engine service account with broad permissions to simplify access to Cloud Storage.

Answer

Increase the number of training steps to improve model accuracy, regardless of cost.

Question 13

You are running a distributed training job on Vertex AI using PyTorch and the DistributedDataParallel (DDP) strategy across 4 nodes, each with 8 GPUs. You notice that the training loss is not decreasing as expected and the job occasionally hangs. You suspect a communication issue between nodes. Which of the following should you check first?

Accepted Answer

Ensure that the MASTER_ADDR and MASTER_PORT environment variables are correctly set and that all nodes can communicate over the network.. Communication issues in multi-node PyTorch DDP training often stem from incorrect MASTER_ADDR or MASTER_PORT settings, or network connectivity problems. These variables are critical for establishing the rendezvous point for all processes. Checking them first is a logical diagnostic step before considering other changes, as they are common causes of hangs and synchronization failures.

Answer

Switch to using the Horovod framework for distributed training.

Answer

Reduce the number of nodes to 2 to decrease communication overhead.

Answer

Increase the batch size per GPU to improve gradient synchronization.

Question 14

A company has deployed a model to a Vertex AI Endpoint and wants to receive an alert when the model's prediction latency exceeds a threshold. They have configured Cloud Monitoring to track the endpoint's latency metrics. They now need to create a notification channel to send alerts to their on-call team. Which Cloud Monitoring resource should they use to define the condition that triggers the alert?

Accepted Answer

An alerting policy with a condition based on the endpoint's latency metric.. Cloud Monitoring alerting policies are used to define conditions that trigger alerts. To alert on prediction latency, you create an alerting policy with a condition based on the endpoint's latency metric and attach a notification channel to it. Dashboards and notification channels do not define conditions.

Answer

A notification channel that sends emails to the on-call team.

Answer

A dashboard that displays the endpoint's latency over time.

Answer

A log-based metric that counts the number of high-latency requests.

Question 15

A financial services firm wants to predict loan default risk using a dataset with 30,000 labeled examples and 25 numeric and categorical features. Their team includes SQL analysts but no Python developers, and they want to minimize operational overhead. They decide to use BigQuery ML. Which model type should they use to achieve the best predictive performance while keeping the solution low-code?

Accepted Answer

BOOSTED_TREE_CLASSIFIER. For a binary classification task with a moderate-sized tabular dataset and a low-code requirement, BigQuery ML's BOOSTED_TREE_CLASSIFIER offers an excellent balance of accuracy and ease of use. It automatically handles categorical features and non-linear relationships, often outperforming linear models. It requires only SQL to train and deploy, aligning with the team's skills and minimizing operational overhead.

Answer

DNN_CLASSIFIER

Answer

KMEANS

Answer

LOGISTIC_REG

Question 16

A team runs a Vertex AI pipeline that includes a component which downloads a large dataset from BigQuery and writes it to Cloud Storage. The pipeline's caching is enabled by default. During iterative development, the engineer modifies the SQL query inside the component to include an additional feature column, but the pipeline still uses the previously cached output because the component's input parameters and code hash are unchanged. The engineer needs the component to re-execute with the updated query without disabling caching for the entire pipeline. What should the engineer do?

Accepted Answer

Add the SQL query as a pipeline parameter and pass it to the component as an input.. Vertex AI Pipelines caching uses a fingerprint derived from the component's code and input parameters. When a component's internal logic changes but its inputs and code hash remain the same, the cache is considered valid. To force re-execution without disabling caching entirely, the engineer should expose the changing element—the SQL query—as an input parameter. This changes the cache key only when the query changes, preserving caching for other runs. Rebuilding the image or disabling caching are less precise and more disruptive.

Answer

Set the component's `enable_caching` argument to False in the pipeline definition.

Answer

Modify the component's container image tag to a new version and rebuild the pipeline.

Answer

Clear the pipeline's cache by deleting the pipeline run's metadata from Vertex ML Metadata.

Question 17

An ML engineer has a prototype scikit-learn model that must be served on Vertex AI. The model requires a custom preprocessing step that cannot be expressed in a scikit-learn Pipeline. The engineer wants to package the model with this preprocessing logic and deploy it to a Vertex AI Endpoint for online predictions. Which approach should they take?

Accepted Answer

Create a custom container that includes the scikit-learn model and the preprocessing code, push it to Artifact Registry, import it as a Vertex AI Model, and deploy to an Endpoint.. When a model requires custom preprocessing that is not part of the model artifact itself, the most reliable way to serve it on Vertex AI is to build a custom container. The container can include the model, the preprocessing code, and all dependencies, ensuring consistent behavior between training and serving. Vertex AI then deploys this container as a Model resource and makes it available via an Endpoint.

Answer

Use Vertex AI Model Monitoring to apply the preprocessing transformations before the model receives the request.

Answer

Save the model with joblib, upload it as a Vertex AI Model, and deploy it to an Endpoint; Vertex AI automatically applies the preprocessing.

Answer

Deploy the model as a batch prediction job, which automatically applies preprocessing from a saved scikit-learn Pipeline.

Question 18

A marketing team wants to build a model that predicts whether a customer will click on an ad, using a dataset in BigQuery. They have limited ML expertise and want to avoid writing complex code. They decide to use BigQuery ML with a logistic regression model. Which SQL statement should they use to create the model?

Accepted Answer

CREATE MODEL `project.dataset.model` OPTIONS(model_type='logistic_reg', input_label_cols=['clicked']) AS SELECT * FROM `project.dataset.training_data`. The correct SQL statement creates a logistic regression model in BigQuery ML with the proper model type and label column specification. Logistic regression is ideal for binary classification tasks such as predicting ad clicks. The input_label_cols option identifies the target variable, and the SELECT statement supplies the training data. This approach allows users with limited ML expertise to build a model using only SQL.

Answer

CREATE MODEL `project.dataset.model` OPTIONS(model_type='linear_reg', input_label_cols=['clicked']) AS SELECT * FROM `project.dataset.training_data`

Answer

CREATE MODEL `project.dataset.model` OPTIONS(model_type='logistic_reg') AS SELECT * FROM `project.dataset.training_data`

Answer

CREATE MODEL `project.dataset.model` OPTIONS(model_type='kmeans', input_label_cols=['clicked']) AS SELECT * FROM `project.dataset.training_data`

Question 19

A financial institution wants to detect fraudulent transactions in real-time. They have a labeled dataset of historical transactions and want to use a low-code solution that can automatically handle feature engineering and model selection. They also need to deploy the model for online predictions with low latency. Which Google Cloud service should they use?

Accepted Answer

Vertex AI AutoML Tables. Vertex AI AutoML Tables is the correct choice because it provides automatic feature engineering and model selection, and supports low-latency online predictions when deployed to an endpoint. It is designed for tabular data like financial transactions and requires minimal coding. Other options either lack automatic feature engineering, require significant coding, or are not intended for building predictive models, making AutoML Tables the best fit for real-time fraud detection.

Answer

Vertex AI Pipelines with Kubeflow

Answer

Cloud Functions with a custom Python script

Answer

BigQuery ML with a logistic regression model

Question 20

You are scaling a prototype recommendation model to production on Vertex AI. The model is trained with a custom container and uses a large embedding table that must be updated frequently. You need to serve predictions with low latency and also support online updates to the embeddings without redeploying the model. Which Vertex AI feature should you use?

Accepted Answer

Deploy the model to a Vertex AI Endpoint with a custom container that loads the embedding table from a shared store such as Cloud Storage or a database at prediction time, and update the shared store independently.. To serve low-latency predictions while allowing online embedding updates, the model should run in a custom container that can read and refresh the embedding table from an external store. This keeps the model deployed while the embeddings change independently. The other options either do not update internal embeddings, require redeployment, or are batch-oriented and therefore unsuitable for low-latency online serving.

Answer

Deploy the model to a Vertex AI Endpoint with a pre-built container and use Vertex AI Feature Store to serve the embeddings as features at prediction time.

Answer

Deploy the model to a Vertex AI Endpoint and use the endpoint's built-in support for model updates through Vertex AI Model Registry versioning.

Answer

Use Vertex AI Batch Prediction with a large batch size so that embeddings are computed in bulk and can be updated between batches.