PMLE Practice Test 26 — 15 Questions

Question 1

A machine learning engineer needs to pass a large dataset between two components in a Vertex AI pipeline. What is the recommended way to pass this data?

Accepted Answer

Store the dataset as a Dataset artifact and pass the artifact between components.. In Vertex AI Pipelines, the recommended way to pass large datasets between components is to use a `Dataset` artifact. Artifacts are metadata references that point to the underlying data stored in Cloud Storage, enabling efficient, scalable, and type-safe data passing without serialization overhead or size limits. This approach leverages the Kubeflow Pipelines SDK's artifact tracking, which automatically handles lineage and versioning.

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Write the dataset to a temporary BigQuery table and pass the table name.

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Serialize the dataset to a string and pass it as a pipeline parameter.

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Use a Cloud Storage bucket and pass the bucket name as a parameter.

Question 2

An ML engineer needs to update a model deployed on a Vertex AI endpoint without downtime. They want to gradually shift traffic to the new version while monitoring for errors. What is the correct procedure?

Accepted Answer

Deploy the new model to the same endpoint with 0% traffic initially, then gradually increase traffic while monitoring.. The correct procedure is to deploy the new model version to the same endpoint, initially with 0% traffic, then gradually increase its traffic allocation while monitoring.

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Use a canary deployment by deploying to a separate endpoint and using a load balancer with weighted routing.

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Deploy the new model to a new endpoint, then update DNS to point to the new endpoint.

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Delete the old model and deploy the new one with the same endpoint.

Question 3

An organisation wants to use Document AI to process contracts but requires human review for high-risk clauses. Which feature should they enable?

Accepted Answer

Human-in-the-Loop (HITL). Human-in-the-Loop (HITL) is the correct feature because it allows Document AI to automatically process contracts while routing high-risk clauses to human reviewers for validation. This balances automation efficiency with the need for expert oversight on sensitive content, which is a core requirement for compliance-driven document processing.

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Batch Processing

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Online Prediction

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AutoML Training

Question 4

A team is scaling a prototype ML model to production on Vertex AI. The model was developed using scikit-learn and requires custom preprocessing. They want to minimize operational overhead and ensure consistency between training and serving. Which approach should they use?

Accepted Answer

Use a pre-built Vertex AI container for scikit-learn and provide a custom training Python package with preprocessing code included.. Using a pre-built Vertex AI container for scikit-learn with a custom training Python package ensures that the same preprocessing code runs during both training and serving, minimizing operational overhead. This approach leverages Vertex AI's managed infrastructure to handle scaling, monitoring, and consistency without requiring custom container maintenance.

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Train on a local machine and upload the model artifacts to Cloud Storage, then create an endpoint with a pre-built container.

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Deploy the model as a custom prediction routine on Vertex AI Endpoints with a custom container.

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Export the model as a .pkl file and use Vertex AI's 'Import Model' with a default container for inference.

Question 5

A machine learning team is training a large transformer model on Vertex AI. They need to reduce training time by utilizing multiple GPUs across nodes, but the model is too large to fit into a single GPU memory. Which distributed training strategy should they use?

Accepted Answer

Model parallelism using tf.distribute.experimental.PipelineMirroredStrategy. PipelineMirroredStrategy combines model parallelism (splitting the transformer layers across multiple GPUs) with pipeline parallelism to handle models that exceed single GPU memory. This strategy partitions the model into stages, each placed on a different GPU, and uses micro-batching to keep all GPUs busy, which is essential for large transformer models that cannot fit into a single GPU's memory.

Answer

Data parallelism using tf.distribute.MirroredStrategy

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Multi-worker mirrored strategy with a single worker per node

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Hyperparameter tuning with Vertex AI Vizier

Question 6

Which THREE factors should be considered when choosing a compute option for serving a deep learning model in production on Google Cloud? (Choose three.)

Accepted Answer

Integration with Vertex AI for model monitoring. A is correct because Vertex AI provides integrated model monitoring capabilities, including feature drift detection, prediction skew analysis, and outlier detection, which are essential for maintaining model performance in production. Without this integration, you would need to build custom monitoring pipelines, increasing operational complexity.

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The programming language used for training

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The color of the team's logo

Question 7

You need to deploy a TensorFlow model to edge devices for real-time inference with minimal latency. The model is currently trained on Vertex AI. Which approach should you use?

Accepted Answer

Convert the model to TensorFlow Lite using the TF Lite converter, then deploy to edge devices via Vertex AI Edge Manager.. TensorFlow Lite is specifically designed for on-device inference on edge devices, offering reduced model size and optimized performance for low-latency, real-time scenarios. Vertex AI Edge Manager provides a managed service to deploy, monitor, and update models on edge devices, making it the ideal combination for this use case.

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Export the model as a SavedModel and deploy it to Vertex AI Edge Manager using the Edge Manager API.

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Deploy the model to Vertex AI endpoint and use Cloud IoT Core to stream data to the cloud for inference.

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Use Vertex AI Model Optimization to quantize the model to INT8 and then deploy as a web service on a Raspberry Pi.

Question 8

An engineer deploys a model to a Vertex AI endpoint with minReplicas=1 and maxReplicas=3. The endpoint receives a sudden traffic spike, but it does not scale up beyond 1 replica. The CPU utilization target is 60%. What is the most likely cause?

Accepted Answer

The CPU utilization is below the target threshold, so the autoscaler does not add replicas.. Vertex AI's autoscaler uses CPU utilization as a metric to decide when to add replicas. If the CPU utilization remains below the 60% target threshold, the autoscaler will not trigger scale-up, even during a traffic spike. The endpoint is configured with minReplicas=1 and maxReplicas=3, but without exceeding the target, it stays at the minimum.

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The model is not deployed correctly.

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The endpoint is configured with the wrong machine type.

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The endpoint is using GPU which cannot autoscale.

Question 9

A team uses Vertex AI Experiments to track ML training runs. They want to automatically trigger a retraining pipeline when new labeled data arrives in BigQuery, and ensure the pipeline uses only approved libraries from a central artifact registry. Which combination of services should they use?

Accepted Answer

Vertex AI Pipelines with a scheduled trigger, and use Cloud Build to pull libraries from Artifact Registry.. Vertex AI Pipelines provides a managed orchestration service for ML workflows, and a scheduled trigger can be set to run the pipeline when new labeled data arrives in BigQuery (e.g., via a Cloud Scheduler or Eventarc trigger). Cloud Build is used to pull approved libraries from Artifact Registry, ensuring only vetted dependencies are used during pipeline execution, which meets the security and compliance requirement.

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Cloud Composer to orchestrate, with Cloud Storage for libraries.

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Cloud Functions triggered by BigQuery, Cloud Build to run training, and Artifact Registry for libraries.

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Vertex AI Experiments with continuous evaluation, and a Cloud Run job for training.

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Dataflow to preprocess, then trigger a Cloud Run job.

Question 10

A company is deploying multiple models on a single Vertex AI endpoint to reduce costs. Each model has different traffic patterns. Which configuration should they use?

Accepted Answer

Use a single endpoint with multiple deployed models and traffic allocation.. Vertex AI endpoints support deploying multiple models behind a single endpoint with traffic splitting, allowing you to route different percentages of requests to each model based on their traffic patterns. This reduces infrastructure costs compared to separate endpoints, as the endpoint's underlying compute resources are shared. Traffic allocation can be adjusted dynamically to match changing model usage without redeploying.

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Use Cloud Run to serve each model as a separate service.

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Deploy each model to separate endpoints and use a load balancer.

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Use Vertex AI Matching Engine to serve models.

Question 11

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 a minimum number of replicas (min_replica_count) ensures that some instances are always warm, avoiding cold start for the first requests. This balances cost and latency. Prewarming requests or increasing target utilization wouldn't help directly.

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Use a larger machine type to reduce cold start time.

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

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Enable serving on Cloud Run for faster cold start.

Question 12

A financial services company uses Vertex AI Pipelines to train and deploy models for fraud detection. The ML team consists of data scientists who develop models and ML engineers who deploy them. They use a CI/CD pipeline with Cloud Build to build and push Docker images to Artifact Registry, then trigger Vertex AI Pipelines. Recently, the team noticed that a model deployed to production was trained on a dataset that had not been approved by the data governance team. Upon investigation, they found that a data scientist accidentally used an unapproved version of the training data by specifying a Cloud Storage path that was not the latest approved dataset. The company needs to enforce that only approved datasets are used in training jobs. Which approach should they take?

Accepted Answer

Use a curated dataset registry in BigQuery or Cloud Storage with IAM conditions that allow access only to datasets tagged as 'approved'. Modify the CI/CD pipeline to pass only approved dataset references to the training job.. It enforces governance at the source by using IAM conditions to restrict access to only approved datasets, preventing unauthorized data from being used in training. This approach integrates with the CI/CD pipeline to automatically pass only approved dataset references, eliminating the risk of human error in specifying Cloud Storage paths.

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Implement a manual approval process where data scientists request dataset paths from the data governance team before each training run.

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After training, run a validation step that checks if the dataset used matches the latest approved version, and roll back if not.

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Restrict all Cloud Storage buckets to be read-only for the data scientists, and have ML engineers copy approved datasets to a separate bucket.

Question 13

Your model serving endpoint on Vertex AI is experiencing increased memory usage after a recent update. The model was converted from TensorFlow to TF Lite for faster inference. You notice that the endpoint's instances occasionally get killed due to out-of-memory (OOM) errors. What is the most likely cause?

Accepted Answer

The number of inference threads in the TF Lite runtime is set too high, causing memory consumption.. TF Lite models can have different memory footprint depending on the number of threads used for inference. If the custom container or the runtime allocates many threads, memory usage can spike. The model conversion itself may not reduce memory; thread count is a key factor.

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The TF Lite model is larger in size than the original model.

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The Vertex AI endpoint is not configured with enough CPU.

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The traffic to the endpoint has increased significantly.

Question 14

You have a model that requires GPU for efficient inference. You deploy it on Vertex AI with a single NVIDIA T4 GPU accelerator and notice that the GPU utilization hovers around 30%. The endpoint has 10 replicas. What is the best way to improve cost efficiency while maintaining throughput?

Accepted Answer

Reduce the number of replicas to increase GPU utilization per instance.. If GPU utilization is low, you can reduce the number of replicas or increase the batch size per request to fully utilize the GPU. Reducing replicas directly saves cost. Increasing batch size may also help but requires code changes.

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Use a larger GPU like V100 to process requests faster.

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Enable autoscaling to increase the number of replicas.

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Switch to a CPU-only instance; the model can run on CPU.

Question 15

You are using Vertex AI Training to train a model and then automatically deploy the best candidate to a Vertex AI Prediction endpoint via the Vertex AI Model Registry. However, after deployment, you notice that the endpoint returns predictions for the new model, but they are significantly different from the evaluation metrics computed during training. The training scripts used TensorFlow with a serving input function. What is the most likely issue and how would you fix it?

Accepted Answer

The serving input function's preprocessing steps do not match the training preprocessing; you should verify and align them.. The serving input function's preprocessing must match training preprocessing exactly; any mismatch causes prediction errors. Option A is wrong because the model saved includes evaluation metrics. Option C is possible but less likely given the consistency of difference. Option D is unlikely as numerical precision differences are minimal.

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The endpoint is using a different machine type affecting numerical precision; you should use the same machine type as training.

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The model registry deployed a different version; you should check the alias.

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The model was saved with training-only metrics; you should retrain with evaluation metrics.