PMLE Practice Test 4 — 25 Questions

Question 1

You need to preprocess a large dataset (terabytes) for training a TensorFlow model. The preprocessing includes scaling and bucketizing features, and the same transformations must be applied during serving. Which tool should you use?

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

Accepted Answer

Dataflow with Apache Beam and tf.Transform. TensorFlow Transform (tf.Transform) allows you to define preprocessing pipelines that are applied consistently during training and serving. It computes statistics over the full dataset and creates a TensorFlow graph for serving.

Answer

Dataproc with Spark ML

Answer

Vertex AI Feature Store

Answer

BigQuery ML

Question 2

A company trains models using Vertex AI Training and wants to share the resulting model artifacts with a different team in another Google Cloud project. What is the most secure way to grant access?

Accepted Answer

Create a new service account in the other project, then grant it the 'roles/storage.objectViewer' role on the bucket.. It follows the principle of least privilege and cross-project access best practices. By creating a dedicated service account in the target project and granting it the 'roles/storage.objectViewer' role on the specific Cloud Storage bucket, you avoid exposing the bucket publicly and avoid sharing the Vertex AI model resource directly, which would grant broader permissions than necessary. This approach ensures that only the service account can read the model artifacts, and the other team can use that service account to access the bucket securely.

Answer

Use BigQuery to copy the model artifacts and share the BigQuery dataset.

Answer

Share the Vertex AI model resource directly by adding the other project's members to the IAM policy on the model.

Answer

Set the Cloud Storage bucket containing the artifacts to 'public' access.

Question 3

Refer to the exhibit. An ML engineer in the team needs to deploy the model to an endpoint. The engineer is assigned the 'roles/aiplatform.user' role at the project level but still cannot deploy. What is the most likely reason?

Accepted Answer

The model's resource-level IAM policy only grants the 'roles/aiplatform.user' role, which does not include deploy permission.. The 'roles/aiplatform.user' role at the project level grants permissions to use AI Platform resources, but it does not include the 'aiplatform.models.deploy' permission required to deploy a model to an endpoint. Model deployment is controlled by resource-level IAM policies, and if the model's resource-level policy only grants 'roles/aiplatform.user', the deploy action is denied. The correct role for deployment is 'roles/aiplatform.admin' or a custom role with the deploy permission.

Answer

The service account 'sa-training' is using all the model's quota.

Answer

Alice does not have any IAM role on the project.

Answer

Alice needs to be granted the 'roles/aiplatform.admin' role at the project level.

Question 4

To enable collaboration on notebook-based experiments across teams, what is the recommended approach in Google Cloud?

Accepted Answer

Use Colab Enterprise notebooks with shared runtimes and IAM permissions. Colab Enterprise notebooks with shared runtimes and IAM permissions is the recommended approach because it provides a fully managed, collaborative environment where multiple users can work on the same notebook simultaneously, with fine-grained access control via IAM and consistent runtime configurations. This eliminates version conflicts and environment drift, which are common in distributed notebook workflows.

Answer

Share Docker images containing the notebook environment

Answer

Each team member works on their own local Jupyter notebook and shares screenshots

Answer

Store notebooks in a Cloud Storage bucket and open them with Vertex AI Workbench

Question 5

A data scientist trained a model on a single GPU but needs to train on multiple GPUs for a larger dataset. They observe that training time does not decrease linearly with additional GPUs. Which common issue is most likely?

Accepted Answer

Data pipeline bottleneck.. Data pipeline bottleneck. When training on multiple GPUs, the expected speedup is linear if data loading and preprocessing can keep up with the GPUs. However, if the data pipeline is slow (e.g., due to disk I/O, network latency, or insufficient CPU preprocessing), GPUs will spend time idle waiting for data, preventing linear scaling. Option A is incorrect because overfitting affects model accuracy, not training speed. Option B is incorrect because model architecture simplicity does not directly cause non-linear speedup; simpler architectures may actually speed up training. Option C is incorrect because learning rate affects convergence behavior, not the efficiency of parallel training.

Answer

Overfitting.

Answer

Model architecture too simple.

Answer

Learning rate too high.

Question 6

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 7

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 8

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 9

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 10

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 11

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 12

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 13

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 14

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 15

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

Question 16

A large organization uses a multi-project setup with a central data lake. Different teams manage their own models. To enable cross-team sharing of features, they want to use Vertex AI Feature Store. What is the best practice to manage access?

Accepted Answer

Create a single Feature Store in a central project and grant fine-grained IAM roles. Creating a single Feature Store in a central project with fine-grained IAM roles is the best practice because it centralizes feature management while allowing cross-team access control at the feature group or feature level. Vertex AI Feature Store supports IAM roles like `aiplatform.featureStoreAdmin` and `aiplatform.featureStoreDataViewer` to grant granular permissions, enabling teams to share features without duplicating data or exposing sensitive information. This approach avoids data silos and ensures consistent governance across the organization.

Answer

Export features to Cloud Storage

Answer

Create separate Feature Stores per team project

Answer

Use BigQuery authorized views

Question 17

A large enterprise has multiple ML models deployed in production across different regions. They want to implement a centralized monitoring dashboard that tracks key performance indicators such as prediction accuracy, latency, and error rates for all models, with the ability to drill down into individual model versions. Which approach best meets these requirements?

Accepted Answer

Use Cloud Monitoring with custom metrics reported by each model deployment, and create a unified dashboard with filterable resources. Cloud Monitoring with custom metrics allows each model deployment to report key performance indicators (e.g., prediction accuracy, latency, error rates) as metric time series. These custom metrics can be aggregated into a single unified dashboard, and the dashboard can be configured with filterable resources (e.g., region, model version) to enable drill-down into individual model versions. This approach provides centralized, real-time monitoring without relying on log-based or batch analytics.

Answer

Use Vertex AI Experiments to log metrics and compare across runs

Answer

Use Cloud Logging to search logs from each model and create a dashboard

Answer

Use BigQuery to store prediction logs and then visualize in Looker

Question 18

An engineer needs to perform sentiment analysis on customer reviews. They have a large volume of text and need a solution that requires minimal customisation. Which option is most efficient?

Accepted Answer

Use the Natural Language API. The Natural Language API provides pre-built sentiment analysis with minimal setup. AutoML NLP would require custom training, BigQuery ML is for tabular data, and Vertex AI Prediction needs a deployed model.

Answer

Use Vertex AI Prediction with a pre-trained model

Answer

Use BigQuery ML with LOGISTIC_REG

Answer

Train a custom model using AutoML NLP

Question 19

A company uses Cloud Composer to orchestrate their ML pipelines. They notice that tasks are being queued but not executed, causing delays. What is the most likely cause?

Accepted Answer

The Airflow worker resources are exhausted. When tasks are queued but not executed, it typically indicates that the Airflow workers have no available slots to pick up new tasks. In Cloud Composer, the Celery executor distributes tasks to workers; if all worker concurrency slots are saturated or the worker node pool is under-provisioned, tasks remain in the 'queued' state until a worker becomes free. This is the most likely cause given the symptom of tasks being queued without execution.

Answer

The Airflow web server is down

Answer

The DAG file is corrupted

Answer

The Cloud Storage bucket containing DAGs is not accessible

Question 20

An ML engineer is using Vertex AI Pipelines and wants to reuse a trained model across multiple pipeline runs without retraining each time. Which artifact management strategy should be used?

Accepted Answer

Use Vertex AI ML Metadata to track and retrieve model artifacts. Vertex AI ML Metadata is the correct artifact management strategy because it is purpose-built for tracking and retrieving model artifacts across pipeline runs. It stores metadata about models, datasets, and other artifacts in a lineage graph, enabling you to query and reuse a specific model version without retraining. This integrates natively with Vertex AI Pipelines, allowing you to pass model artifacts between components and retrieve them by ID or custom properties.

Answer

Store the model in BigQuery as a ML model

Answer

Use Cloud Functions to cache the model

Answer

Save the model to a Cloud Storage bucket and reference by path