PMLE Practice Test 9 — 10 Questions

Question 1

An MLOps team needs to automatically retrain a model when new training data becomes available. They use Vertex AI Pipelines. What is the recommended way to trigger the pipeline?

Accepted Answer

Cloud Functions triggered by Cloud Storage events. Vertex AI Pipelines does not natively support event-driven triggers. The recommended pattern is to use Cloud Functions, which can be triggered by Cloud Storage events (e.g., object finalize/create) when new training data is uploaded. The Cloud Function then programmatically submits the pipeline run via the Vertex AI Pipelines client library or REST API, enabling an automated retraining workflow.

Answer

Use Model Evaluation to decide

Answer

Set up a trigger in Vertex AI Pipelines

Answer

Cloud Scheduler on a daily basis

Question 2

A team uses Vertex AI Pipelines. They need to ensure that only certain team members can deploy models to production. What is the best approach?

Accepted Answer

Use IAM roles with custom permissions on the Vertex AI Model Registry. Vertex AI Model Registry supports IAM roles with custom permissions, allowing fine-grained access control over who can promote or deploy models to production. By assigning specific roles (e.g., `roles/aiplatform.modelDeployer`) to only authorized team members, you can restrict deployment actions while still permitting others to view or register models. This approach directly addresses the need to control production deployments without affecting other pipeline stages.

Answer

Use Vertex AI Experiments to track models

Answer

Store model artifacts in a bucket with bucket-level permissions

Answer

Create separate projects for dev and prod

Question 3

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 4

You are a Machine Learning Engineer at a financial services company. You have trained a large language model (LLM) using a custom container on Vertex AI Training. The model is used for sentiment analysis on financial news articles. You have deployed the model to a Vertex AI Endpoint for online prediction. However, during peak trading hours, users report high latency ( > 5 seconds) and occasional timeout errors. The model is deployed on n1-highmem-8 machines with 1 replica. You monitor the endpoint and see that CPU utilization is high ( > 90%) and memory is near capacity. The queries are relatively small text inputs. Which course of action should you take to reduce latency?

Accepted Answer

Use Vertex AI Prediction with GPU accelerators like NVIDIA Tesla T4.. The high CPU utilization and memory pressure indicate that the CPU is the bottleneck for inference, not the model size or input volume. Switching to GPU accelerators like NVIDIA Tesla T4 offloads the computationally intensive matrix operations of the LLM to the GPU, drastically reducing per-query latency and freeing CPU resources for preprocessing and I/O. This directly addresses the root cause of >5-second latency during peak hours.

Answer

Deploy the model to multiple endpoints and use round-robin load balancing.

Answer

Increase the machine type to n1-highmem-16 and keep 1 replica.

Answer

Reduce the batch size for predictions to lower memory usage.

Question 5

When distributing training across multiple workers using Vertex AI Training, how should the team share the training dataset?

Accepted Answer

Use Cloud Storage. Vertex AI Training workers need shared, concurrent read access to the training dataset without manual replication. Cloud Storage (GCS) is the recommended and fully integrated solution because it provides a distributed, highly available object store that all workers can read from in parallel via the `tf.io.gfile` API or GCS connector, eliminating data duplication and ensuring consistency across the cluster.

Answer

Copy the dataset to each worker's local disk

Answer

Use NFS

Answer

Use Google Drive

Question 6

A team is using Cloud Composer to orchestrate ML workflows. They have a DAG that triggers a Vertex AI Training job, then a prediction deployment. The deployment step occasionally fails due to quota limits. What is the best way to handle this?

Accepted Answer

Implement exponential backoff retry in the DAG task. Cloud Composer (Apache Airflow) provides built-in retry mechanisms via task parameters like `retries` and `retry_delay`. Implementing exponential backoff in the DAG task is the best practice for handling transient quota errors, as it automatically retries the deployment step with increasing delays, reducing load on the quota system and increasing the chance of success without manual intervention. This approach aligns with Airflow's native error-handling capabilities and avoids unnecessary complexity or resource waste.

Answer

Increase the quota manually

Answer

Use Vertex AI Pipelines instead of Cloud Composer

Answer

Create a custom sensor to wait for quota to be available

Answer

Catch the exception in the DAG and send an alert

Question 7

Which THREE practices improve collaboration when using Cloud Composer for ML pipelines?

Accepted Answer

Use a shared Cloud Storage bucket for intermediate artifacts with appropriate permissions.. Cloud Composer workflows often require sharing intermediate data (e.g., transformed datasets, model checkpoints) across multiple DAGs or team members. A shared Cloud Storage bucket with fine-grained IAM permissions enables secure, centralized artifact exchange without duplicating data or exposing it to unauthorized users. This practice avoids hard-coded paths and ensures that all pipeline stages can reliably access the same artifacts, which is critical for reproducibility and collaboration in ML pipelines.

Answer

Keep all pipeline logic in a single large DAG for simplicity.

Answer

Embed service account keys directly in DAG code for authentication.

Question 8

An ML engineer wants to monitor a deployed model for fairness across different age groups and genders. Which TWO Vertex AI services should they use together to achieve this? (Choose two.)

Accepted Answer

BigQuery. Vertex AI Model Evaluation provides sliced evaluation when ground truth is available in BigQuery. Vertex AI Explainable AI can help understand feature importance but is not required for fairness monitoring.

Answer

Vertex AI Feature Store

Answer

Vertex AI Explainable AI

Answer

Cloud Monitoring

Question 9

Refer to the exhibit. A user attempts to upload a model to Vertex AI Model Registry using the gcloud CLI. The command fails with the error shown. What is the most likely cause?

Accepted Answer

The --artifact-uri points to a directory instead of a model file. The error indicates that the `--artifact-uri` flag points to a directory (e.g., `gs://bucket/model/`) rather than a specific model file (e.g., `gs://bucket/model/saved_model.pb`). Vertex AI Model Registry requires a direct path to the model artifact file, not a container directory, because the service needs to locate and register the exact model binary for deployment.

Answer

The region us-central1 does not support Vertex AI

Answer

The --container-command flag is misspelled

Answer

The --container-ports flag expects a comma-separated list

Question 10

A company wants to use pre-built Google Cloud APIs for text analysis. Which TWO APIs can they use? (Choose TWO.)

Accepted Answer

Cloud Natural Language API. The Cloud Natural Language API provides pre-built machine learning models for text analysis tasks such as entity recognition, sentiment analysis, and syntax analysis. The Cloud Translation API can translate text between languages, which is a form of text analysis. Both are pre-built Google Cloud APIs that directly address the company's need for text analysis without requiring custom model training.

Answer

Cloud Vision API

Answer

Video Intelligence API

Answer

Document AI