PMLE Practice Test 18 — 15 Questions

Question 1

A data science team has trained a TensorFlow model and wants to serve it online with minimal latency. Which Vertex AI deployment option should they use to ensure the model can handle traffic spikes without manual scaling?

Accepted Answer

Deploy the model to a Vertex AI Endpoint with automatic scaling.. Vertex AI Endpoints with automatic scaling (option B) are designed for online serving with minimal latency and can automatically adjust the number of replicas based on traffic load, handling spikes without manual intervention. This is the correct choice for a TensorFlow model requiring real-time inference and elastic scaling.

Answer

Use Vertex AI Model Garden.

Answer

Use Vertex AI Batch Prediction for offline inference.

Answer

Deploy the model to a Compute Engine VM with a load balancer.

Question 2

A financial services company deploys a fraud detection model on Vertex AI. The model must make predictions in under 100ms. After deployment, latency spikes to 300ms during peak hours. The model is a large ensemble with 500MB size. Which action is most likely to reduce latency?

Accepted Answer

Optimize the model using TensorFlow Lite and convert to a smaller format.. The primary cause of latency is the large model size (500MB) combined with real-time inference constraints. Optimizing the model with TensorFlow Lite reduces the model size and computational overhead, directly decreasing inference time. This addresses the root cause—model complexity—rather than scaling infrastructure around an inefficient model.

Answer

Switch to batch prediction to process requests asynchronously.

Answer

Reduce the machine type to a smaller instance.

Answer

Increase the number of replicas on the endpoint.

Question 3

A team is serving a large language model (LLM) on Vertex AI using a custom container. They want to reduce tail latency. Which THREE strategies should they consider?

Accepted Answer

Use dynamic batching to combine requests.. Dynamic batching (B) reduces tail latency by grouping multiple inference requests into a single batch, which improves GPU utilization and amortizes overhead across requests. This is particularly effective for LLMs because it allows the model to process more tokens per forward pass, reducing the per-request latency variance that contributes to tail latency.

Answer

Increase the number of replicas.

Answer

Upgrade to a more powerful GPU type.

Question 4

A model deployed on Vertex AI Prediction repeatedly exits with code 137. What is the most likely cause?

Accepted Answer

The model is using more memory than allocated (4GB).. Exit code 137 indicates that the container was killed by the Linux kernel's Out-Of-Memory (OOM) killer. In Vertex AI Prediction, each model deployment has a fixed memory allocation (default 4GB for custom containers). When the model's inference process exceeds this limit, the OOM killer terminates the container, resulting in exit code 137. This is the most direct and common cause for this specific exit code in Vertex AI.

Answer

The model has a disk I/O bottleneck.

Answer

The model is using too much CPU.

Answer

The container image is incompatible with the machine type.

Question 5

A company is deploying a machine learning model for real-time fraud detection. The model must respond to requests within 100ms. The model is a TensorFlow model and will be deployed on Google Kubernetes Engine (GKE). Which Google Cloud service should be used to serve the model to minimize latency?

Accepted Answer

Deploy TensorFlow Serving on GKE with a LoadBalancer service.. Option D is correct because deploying TensorFlow Serving directly on GKE with a LoadBalancer service provides the lowest-latency path for real-time inference. TensorFlow Serving is optimized for high-performance model serving with batching and gRPC support, and GKE allows fine-grained control over node placement, autoscaling, and networking to meet the 100ms SLA. In contrast, serverless options like Cloud Run or Cloud Functions add cold-start latency and lack the low-level optimization for TensorFlow models.

Answer

Deploy the model on Cloud Run with minimum instances set to 1.

Answer

Deploy the model as a Cloud Function triggered by HTTP requests.

Answer

Deploy the model on Vertex AI Prediction with a custom container.

Question 6

Refer to the exhibit. A data scientist runs the above BigQuery ML query to create a logistic regression model. After training, the model is evaluated using ML.EVALUATE. The evaluation shows poor performance with high bias. Which action would most likely improve the model's performance?

Accepted Answer

Add more complex features by including polynomial expansions.. High bias indicates the model is underfitting the data, meaning it is too simple to capture underlying patterns. Adding polynomial expansions (feature crosses) in the TRANSFORM clause increases model complexity, allowing the logistic regression to learn non-linear decision boundaries, which directly addresses underfitting.

Answer

Remove the TRANSFORM clause and use raw features.

Answer

Change the model_type to 'linear_reg'.

Answer

Increase the number of training iterations by setting MAX_ITERATIONS.

Question 7

A retail company deployed a demand forecasting model using TensorFlow on Vertex AI Batch Prediction. The model runs weekly on a large dataset stored in BigQuery. Over the past month, the prediction accuracy has degraded significantly. The ML engineer reviews the monitoring dashboard and sees that the feature distribution for 'product_price' has shifted from a mean of $50 to $55, and the new product category 'electronics' now represents 20% of the data, whereas it was only 5% in training. The model was never retrained after initial deployment six months ago. The engineer also notices that the Vertex Explainable AI feature importance scores have changed: 'product_price' used to be the top feature (importance 0.35) but now ranks third (importance 0.20). The company requires minimal downtime and wants to improve accuracy as quickly as possible without incurring high costs from excessive retraining. Which course of action should the ML engineer take?

Accepted Answer

Retrain the model using the most recent 3 months of data, including all new product categories, and deploy the updated model via a new Vertex AI endpoint.. The correct action is to retrain the model with the latest data because the feature distributions and data composition have changed significantly (covariate shift and concept drift). Simply using a more complex model (B) may overfit without addressing the underlying drift. Adjusting thresholds (C) is insufficient because the model's predictions are likely inaccurate. Sending all data to a human review (D) is costly and not scalable; retraining is the proper response.

Answer

Increase the complexity of the model by switching from a feedforward neural network to a gradient boosted tree ensemble, and then deploy without retraining.

Answer

Route all predictions to human reviewers until the model can be re-evaluated, and then manually correct the outputs.

Answer

Adjust the prediction threshold for the 'product_price' feature to account for the price shift, and monitor for another month.

Question 8

Refer to the exhibit. A ML engineer runs this Vertex AI pipeline. After execution, the "train" task fails with a resource exhaustion error. The task consumes more memory than allocated. Which step should the engineer take to fix this issue without increasing the overall quota cost?

Accepted Answer

Configure the 'train-exec' executor to use a machine type with higher memory.. In Vertex AI pipelines, the executorLabel maps to a predefined executor that defines machine type. To increase memory for the train task, the engineer must modify the executor specification (e.g., 'machine_type: n1-highmem-*'). Increasing the task's memory directly is not supported; it's done via the executor. Adding an accelerator does not address memory exhaustion.

Answer

Add a 'memory' field to the train task specification.

Answer

Increase the memory of the train task to 32 GiB.

Answer

Set 'acceleratorType' to 'NVIDIA_TESLA_T4' on the train task.

Question 9

A company has deployed a computer vision model on Vertex AI Prediction using a custom container. The model processes high-resolution images and serves predictions to a mobile application. Recently, users have reported that predictions sometimes take over 10 seconds, and the application times out. The ML engineer's monitoring shows that the endpoint's CPU utilization is consistently high (above 85%) and that the request latency spikes during peak hours. The model is deployed on n1-standard-4 machines with automatic scaling set to minReplicaCount=1 and maxReplicaCount=5. The engineer has observed that the endpoint rarely scales beyond 2 replicas even during peak hours. What should the engineer do to reduce prediction latency?

Accepted Answer

Review the custom container's startup time and consider pre-warming or reducing model loading time.. Option D is correct because the root cause is likely that the custom container takes a long time to start (model loading), preventing the endpoint from scaling quickly. Pre-warming or reducing model loading time addresses this directly. Option A (increasing max replicas) does not solve the scaling delay. Option B (upgrading machine type) may help but does not address the scaling speed. Option C (increasing min replicas) would be costly and still not handle sudden spikes if new replicas start slowly.

Answer

Increase the maxReplicaCount to 20 to allow more instances during spikes.

Answer

Change the machine type to a higher CPU machine like n1-standard-8.

Answer

Set the minReplicaCount to 5 to ensure enough capacity at all times.

Question 10

Which THREE of the following are recommended practices for model governance and lineage in Vertex AI?

Accepted Answer

Enable Vertex AI ML Metadata to track artifacts, executions, and contexts.. Vertex AI ML Metadata is a fully managed service that automatically tracks artifacts, executions, and contexts across the ML workflow. By enabling it, you create a lineage graph that records every step from data preparation to model deployment, which is essential for auditability and reproducibility. This is a core recommended practice for model governance because it provides an immutable, queryable history of all model-related activities.

Answer

Store model artifacts in Cloud Storage with metadata in a database.

Answer

Manually record model lineage in a spreadsheet.

Question 11

A data scientist wants to share a trained model with the team for review before deployment. The model is stored in Vertex AI Model Registry. What is the recommended way to grant the team read access to the model?

Accepted Answer

Grant the IAM role 'roles/aiplatform.viewer' to the team members on the project.. Option A is correct because Vertex AI Model Registry uses Cloud IAM, and granting the 'roles/aiplatform.viewer' role provides read access to all model versions. Option B is wrong because too broad. Option C is wrong because Cloud Storage IAM is separate and not sufficient for Vertex AI models. Option D is wrong because the bucket ACL does not apply to Vertex AI.

Answer

Grant the IAM role 'roles/aiplatform.admin' to the team members.

Answer

Export the model as a local file and share it via a shared drive.

Answer

Add the team members to the Cloud Storage bucket ACL with 'READER' access.

Question 12

Your team is using Vertex AI Feature Store for online predictions. You notice that feature values for some entities are missing in production, leading to failed predictions. Upon investigation, you find that the ingestion pipeline has been failing intermittently. What is the best immediate course of action to prevent prediction failures?

Accepted Answer

Configure default values for missing features in the feature store so that the model can fall back on them.. Option D is correct because using default values in the serving layer ensures predictions can still be made when features are missing. Option A is wrong because recreating features takes time and does not fix the ingestion issue. Option B is wrong because it does not address the missing values. Option C is wrong because monitoring alone does not prevent failures.

Answer

Set up monitoring alerts on the ingestion pipeline to get notified of failures.

Answer

Change the prediction request to ignore missing features.

Answer

Manually re-ingest all missing features by running the ingestion pipeline again.

Question 13

You are using Cloud Datalab for collaborative data exploration with your team. However, some team members cannot access the Datalab instances. What is the most likely issue?

Accepted Answer

The team members have not been granted the necessary IAM roles (e.g., roles/datalab.user) on the project.. Cloud Datalab uses IAM permissions to control access to instances. The most common reason team members cannot access Datalab instances is that they lack the necessary IAM role, such as `roles/datalab.user`, which grants permission to view and connect to Datalab instances. Without this role, even if the instances exist and are running, users will receive permission-denied errors when trying to access them via the Datalab UI or API.

Answer

The Datalab instances have been deleted by another team member.

Answer

The team members need to install the Cloud Datalab SDK locally.

Answer

The Datalab instances were created using an incompatible notebook type.

Question 14

A data scientist wants to track the lineage of a dataset used in a training run. Which Vertex AI feature should they use?

Accepted Answer

Vertex ML Metadata. Vertex ML Metadata is the correct choice because it is specifically designed to track the lineage of datasets, models, and other artifacts throughout the ML lifecycle. It records metadata about each step in a pipeline, including the source dataset used for a training run, enabling full provenance tracking. This allows data scientists to trace back which data was used, how it was transformed, and which model version it produced.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Vertex AI Model Registry

Question 15

Which TWO of the following are best practices for managing data in a collaborative machine learning environment on Google Cloud?

Accepted Answer

Use Cloud Data Catalog to discover and annotate datasets.. Option C is correct because Cloud Data Catalog provides a managed metadata management service that allows teams to discover, annotate, and manage datasets across Google Cloud. It enables data scientists to search for datasets by tags, descriptions, and schema, which is essential for collaboration and data governance in a multi-user ML environment.

Answer

Always replicate data across multiple regions to ensure low latency.

Answer

Implement fine-grained access control using IAM conditions.

Answer

Store all raw data in a single Cloud Storage bucket for easy access.