PMLE Practice Test 26 — 15 Questions

Question 1

You are using Vertex AI continuous evaluation (model monitoring) for your deployed model. You receive an alert that the prediction distribution is significantly different from the training distribution. What should you do first?

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

Accepted Answer

Analyze the input data to understand if there is a skew or drift.. When a monitoring alert triggers, the first step is to investigate the root cause: check if input data has changed, retraining is needed, or there is a data pipeline issue. Simply rolling back or retraining without analysis might be premature.

Answer

Roll back the model to the previous version immediately.

Answer

Increase the alerting threshold to reduce false positives.

Answer

Retrain the model using the latest data and redeploy.

Question 2

A data science team wants to version control their datasets along with code using Git. They need a tool that integrates with Git and tracks changes to large data files. Which tool should they use?

Accepted Answer

DVC. DVC (Data Version Control) is purpose-built to integrate with Git and version large datasets and ML models by storing metadata and pointers in Git while keeping the actual data in remote storage. It provides commands like dvc add, dvc push, and dvc pull that mirror Git workflows, making it the right choice for versioning datasets alongside code.

Answer

BigQuery table snapshots

Answer

Delta Lake

Answer

Git LFS

Question 3

A team of data scientists and ML engineers is collaborating on a project using Vertex AI Workbench. They need to share notebooks and code, but want to avoid conflicts and maintain a history of changes. Which approach should they use?

Accepted Answer

Use a git repository (e.g., Cloud Source Repositories) to manage code and notebooks.. Using a git repository (e.g., Cloud Source Repositories) provides version control, branching, and a full history of changes, which is essential for collaborative development. This approach avoids conflicts by allowing team members to work on separate branches and merge changes systematically, unlike shared storage or manual methods that lack conflict resolution and audit trails.

Answer

Email notebook files to each other and manually merge changes.

Answer

Store notebooks in a shared Cloud Storage bucket and access them simultaneously.

Answer

Use Vertex AI Experiments to share notebook outputs.

Question 4

A marketing team wants to use a pre-built natural language processing (NLP) model from Vertex AI Model Garden to analyze customer feedback. They need to extract sentiment from text data stored in Cloud Storage. The team has no experience with model serving infrastructure. Which deployment option minimizes operational overhead?

Accepted Answer

Deploy the model to a Vertex AI Endpoint directly from Model Garden.. Deploying directly to a Vertex AI Endpoint from Model Garden eliminates all infrastructure management. Vertex AI handles model serving, scaling, and monitoring automatically, which is ideal for a team with no experience in model serving infrastructure. This is a fully managed, serverless deployment that requires no containerization or server configuration.

Answer

Deploy the model as a Cloud Function invoked by Cloud Storage events.

Answer

Deploy the model as a Cloud Run service using a custom Docker container.

Answer

Deploy the model on App Engine flexible environment.

Question 5

A team trained a TensorFlow model locally and wants to deploy it to BigQuery ML for predictions without retraining. They have exported the SavedModel to Cloud Storage. Which statement is correct?

Accepted Answer

They can create a model using CREATE MODEL with model_type='tensorflow' and the path to the SavedModel.. BigQuery ML supports importing TensorFlow models in SavedModel format directly using CREATE MODEL with model_type='tensorflow' and the path to the SavedModel in Cloud Storage. This allows you to use the model for prediction in BigQuery without retraining or converting to a native format. The model is imported as a BigQuery ML model and can be used with ML.PREDICT.

Answer

They need to convert the model to a BigQuery ML native format first.

Answer

They must first retrain the model using ML.TRAIN on BigQuery.

Answer

They can use ML.PREDICT directly on the SavedModel in Cloud Storage.

Question 6

A team is scaling their prototype inference model to handle high-throughput requests with low latency. They use a custom container on Vertex AI Prediction. They notice that latency spikes occur under heavy load. What is the most effective strategy?

Accepted Answer

Optimize model serving with batching and model warm-up.. Batching groups multiple inference requests into a single forward pass, dramatically improving GPU/CPU utilization and throughput, while model warm-up pre-loads weights and initializes the runtime so the first requests after a scale-out event do not pay cold-start latency. Together they address the latency spikes under heavy load without over-provisioning. This is the standard Vertex AI Prediction optimization pattern for custom containers.

Answer

Enable auto-scaling with a higher minimum number of replicas.

Answer

Use a larger machine type with more CPUs.

Answer

Use a GPU-based machine.

Question 7

A data science team wants to build a Vertex AI pipeline that trains a model, evaluates it, and conditionally deploys it if the accuracy exceeds 0.9. They want to use the Kubeflow Pipelines SDK v2. Which construct allows them to conditionally execute the deployment step based on the evaluation metric?

Accepted Answer

dsl.If. In Kubeflow Pipelines SDK v2, `dsl.If` is the correct construct for conditionally executing pipeline steps based on runtime metrics or parameters. It allows you to define a condition that, when evaluated to true, triggers the deployment step only if the model accuracy exceeds 0.9. This is the standard way to implement branching logic in v2 pipelines.

Answer

dsl.Conditional

Answer

dsl.ExitHandler

Answer

dsl.Collected

Question 8

A data scientist wants to train a PyTorch model on Vertex AI using a pre-built container for GPU training. She needs to use 4 NVIDIA A100 GPUs on a single machine. Which machine configuration should she select?

Accepted Answer

a2-highgpu-4g (4 A100 GPUs). The a2-highgpu-4g machine type is purpose-built for GPU workloads and provides exactly 4 NVIDIA A100 GPUs on a single machine, matching the requirement precisely. Vertex AI supports this machine type for custom training with pre-built containers, so the data scientist can select it directly in the training configuration. Choosing it avoids over-provisioning or mismatching GPU counts.

Answer

n1-highmem-16 with 4 NVIDIA V100 GPUs

Answer

n1-standard-16 with 4 NVIDIA T4 GPUs

Answer

a2-megagpu-16g (16 A100 GPUs)

Question 9

A data science team is using AI Platform for training. They want to track hyperparameters and metrics across multiple experiments. What should they use?

Accepted Answer

Vertex AI Experiments. Vertex AI Experiments is the correct choice because it is the native service within Vertex AI designed specifically for tracking, comparing, and analyzing hyperparameters and metrics across multiple training runs. It provides a centralized UI and SDK to log parameters, metrics, and artifacts, enabling systematic experiment management without manual effort or external tools.

Answer

Cloud Logging with custom metrics

Answer

Store metrics in Cloud Storage and compare manually

Answer

Cloud Monitoring dashboards

Question 10

Which THREE actions are best practices for managing ML models in production on Google Cloud? (Choose 3)

Accepted Answer

Monitor model performance and data drift continuously.. Option B is correct because continuous monitoring of model performance and data drift is essential in production to detect degradation and trigger retraining before business impact occurs. Option C is correct because a central model registry (such as Vertex AI Model Registry) provides governance, lineage, and controlled promotion of models across environments. Option D is correct because versioning model artifacts and training datasets ensures reproducibility, traceability, and rollback capability for every deployed model. Option A is not a best practice because manual hyperparameter tuning does not scale and should be automated with tools like Vertex AI Vizier. Option E is not a best practice because retaining all raw training data indefinitely increases cost and compliance risk; retention should follow defined policies and lifecycle rules.

Answer

Manually tune hyperparameters for each retraining run.

Answer

Store all raw training data indefinitely for auditability.

Question 11

Your organization wants to automate the retraining of a model when new data is available and also on a weekly schedule. Which TWO services would you use together to achieve this? (Choose two.)

Accepted Answer

Cloud Functions. Cloud Scheduler (E) is the correct service for the time-based trigger, since it can invoke a target on a cron schedule such as weekly, which satisfies the 'weekly schedule' requirement. Cloud Functions (A) is correct because it provides the serverless compute that actually executes the retraining logic, and it can be triggered both by Cloud Scheduler for the weekly run and by an event (for example, a Cloud Storage object finalize event when new data lands) for the data-availability requirement. Together, Cloud Scheduler fires on the cron schedule and Cloud Functions runs the retraining code, covering both triggers. Cloud Composer (B) is a managed Airflow workflow orchestrator and could schedule DAGs, but it is heavier than needed and is not the marked pairing for this scenario. Cloud Tasks (C) is a queue for asynchronous task dispatch, not a cron scheduler or compute runtime, and Dataflow (D) is a managed Apache Beam service for data processing pipelines, not for triggering or hosting model-retraining logic.

Answer

Cloud Composer

Answer

Cloud Tasks

Answer

Dataflow

Question 12

An organization uses Cloud Dataflow to preprocess training data. Dataflow jobs are often failing because of insufficient quota for certain resources. The team has requested a quota increase, but the jobs still fail with 'quota exceeded' errors for a different resource. They want to proactively monitor and manage quotas to avoid failures. What is the best approach?

Accepted Answer

Set up Cloud Monitoring alerts for quota usage and automate quota increase requests.. The best approach is to set up Cloud Monitoring alerts for quota usage and automate quota increase requests. This provides proactive visibility into all resource quotas (not just the one initially increased) and enables automated remediation before jobs fail. Cloud Monitoring can track quota metrics for services like Compute Engine, and you can use Cloud Functions or Pub/Sub to trigger quota increase requests via the Service Usage API.

Answer

Configure Dataflow to use a different pipeline type that avoids the quota.

Answer

Use Dataflow's autoscaling feature to reduce resource usage.

Answer

Increase the maximum number of workers in the Dataflow job.

Question 13

You need to serve a TensorFlow model that has a cold start latency of 20 seconds. The model is used for a real-time application with unpredictable traffic, but occasional bursts require immediate responses. What is the best deployment strategy to minimize both cold start impact and cost?

Accepted Answer

Set min_replica_count to 1 to keep at least one instance always warm.. Setting min_replica_count to 1 keeps one model instance always loaded, eliminating the 20-second cold start for the first request and ensuring immediate responses during bursts. Because only one replica is kept warm, cost stays low compared to maintaining multiple always-on replicas, and autoscaling can add more when traffic spikes. This balances latency and cost for unpredictable real-time workloads.

Answer

Use a larger machine type to reduce cold start time.

Answer

Set min_replica_count to 0 and rely on autoscaling to handle bursts.

Answer

Enable serving on Cloud Run for faster cold start.

Question 14

A company uses Vertex AI Model Registry to manage multiple model versions. They want to designate a model version as 'champion' for production deployment and another as 'challenger' for A/B testing. Which feature of the registry should they use?

Accepted Answer

Model aliases. Model aliases in Vertex AI Model Registry let you assign a mutable, named reference (e.g., 'champion', 'challenger') to a specific model version, so deployment endpoints can point to the alias rather than a fixed version ID. This enables seamless promotion or rollback by reassigning the alias, and supports A/B testing by directing traffic to different aliased versions. It is the intended feature for designating champion/challenger roles.

Answer

Model version labels

Answer

Model lineage

Answer

Model evaluation metrics

Question 15

An organisation uses Delta Lake on Dataproc to manage a data lake for ML training. They need ACID transactions for concurrent reads and writes. Which file format does Delta Lake use as the underlying storage?

Accepted Answer

Apache Parquet. Delta Lake stores its data files in Apache Parquet format and layers a transaction log (the _delta_log directory) on top to provide ACID guarantees, schema enforcement, and time travel. Parquet's columnar layout is ideal for ML workloads because it enables efficient column pruning and predicate pushdown during feature engineering. The Delta transaction log, not the file format itself, is what delivers the ACID properties.

Answer

Apache ORC

Answer

CSV

Answer

Apache Avro