PMLE Practice Test 8 — 10 Questions

Question 1

An ML team is scaling a prototype to production. The data pipeline currently reads from Cloud Storage and transforms data with a custom Python script. They need to handle higher throughput and add monitoring. Which approach should they take?

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

Accepted Answer

Migrate the pipeline to Apache Beam on Dataflow with Cloud Monitoring. Apache Beam on Dataflow provides a unified programming model for batch and streaming data processing, enabling automatic scaling to handle higher throughput. Cloud Monitoring integrates natively with Dataflow to track pipeline metrics, latency, and error rates, addressing the monitoring requirement. This approach is purpose-built for production-grade data pipelines, unlike ad-hoc solutions.

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Deploy the Python script on a large Compute Engine instance with a cron job

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Rewrite the pipeline to use Pub/Sub and Cloud Functions for processing

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Use Cloud Composer to orchestrate the Python script at scale

Question 2

A team has developed a prototype of a recommendation model using a small dataset on a single VM. They need to scale to a larger dataset for production training. They plan to use Vertex AI training with a custom container. What is the best practice for handling the increased data volume?

Accepted Answer

Use TFRecord format and streaming reads.. When scaling to a larger dataset for production training on Vertex AI with a custom container, the best practice is to use TFRecord format and streaming reads. TFRecord is a binary format optimized for TensorFlow, enabling efficient data serialization and streaming from Cloud Storage without loading the entire dataset into memory. This approach supports large-scale distributed training and reduces I/O bottlenecks.

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Increase the batch size to maximum.

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Store all data in memory before training.

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Use a single powerful VM with high memory.

Question 3

You are responsible for monitoring a batch prediction pipeline that runs daily. Recently, the pipeline started failing intermittently with out-of-memory errors. The input data volume has not changed. What is the most likely cause?

Accepted Answer

A recent code change that loads the entire dataset into memory before processing. A code change that loads the entire dataset into memory before processing would directly cause out-of-memory (OOM) errors, even if the input data volume remains unchanged. In batch prediction pipelines, data is typically streamed or processed in chunks to manage memory efficiently. A change that bypasses this pattern and loads all data at once can exceed the available heap or container memory, leading to intermittent failures depending on data characteristics or concurrent loads.

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Increase in model size due to retraining

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Decrease in the number of worker machines

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Increase in input data size

Question 4

A data scientist has a TensorFlow 2.x model trained on a single GPU. They want to scale training to multiple GPUs on a single Vertex AI machine without code changes. Which strategy should they use?

Accepted Answer

MirroredStrategy. MirroredStrategy is TensorFlow's default single-machine, multi-GPU strategy. It replicates the model on each GPU and synchronizes gradients via all-reduce, and it can be enabled with minimal code changes (typically just wrapping model creation in strategy.scope()), making it the right choice for scaling on one Vertex AI machine.

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MultiWorkerMirroredStrategy

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TPUStrategy

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CentralStorageStrategy

Question 5

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.

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Use Vertex AI Model Garden.

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Use Vertex AI Batch Prediction for offline inference.

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

Question 6

A company is deploying a machine learning model for real-time inference on Vertex AI. Which TWO practices improve serving performance and reliability?

Accepted Answer

Enable autoscaling to handle traffic variations.. Vertex AI's autoscaling dynamically adjusts the number of replicas based on incoming request traffic, ensuring low latency during spikes and cost savings during lulls. This is critical for real-time inference, where consistent response times are required and manual scaling would either over-provision or under-provision resources. Autoscaling uses metrics like CPU utilization or request count to scale up or down, directly improving serving performance and reliability.

Answer

Use batch prediction for all requests.

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Use manual scaling with a fixed number of replicas.

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Deploy all models on the same machine type for consistency.

Question 7

A company serves a PyTorch model using a custom container on Vertex AI Prediction. They notice that after a few hours, the endpoint returns 502 errors. The logs show 'Out of memory' errors. The container has a memory limit of 4GB, and the model loads a 3GB vocabulary file. What is the most likely cause and best fix?

Accepted Answer

Load the vocabulary file once at startup and reuse it.. The 502 errors and 'Out of memory' errors indicate that the container is running out of memory during inference. Since the model loads a 3GB vocabulary file, and the container has only 4GB of memory, loading this file repeatedly for each prediction request (e.g., inside the prediction handler) would quickly exhaust memory. The correct fix is to load the vocabulary file once at container startup and reuse it across all requests, which is a standard best practice for serving models with large static assets.

Answer

Increase the container memory to 8GB.

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Increase the number of replicas to distribute load.

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Switch to Vertex AI Batch Prediction.

Question 8

A data scientist has deployed a classification model on a Vertex AI Endpoint and wants to monitor for feature drift in the serving data compared to the training data. Which Vertex AI service should be used?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the purpose-built service that detects training-serving skew and prediction drift by comparing live serving data distributions against a baseline (typically the training dataset). It computes statistics like Jensen-Shannon divergence and can alert when drift exceeds configured thresholds. Explainable AI, Model Evaluation, and Continuous Training serve different purposes and do not provide ongoing drift detection on deployed endpoints.

Answer

Vertex AI Explainable AI

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Vertex AI Model Evaluation

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Vertex AI Continuous Training

Question 9

A team of ML engineers is collaborating on a project using Vertex AI. They want to ensure that only approved models are deployed to production. Which approach should they use?

Accepted Answer

Use Vertex AI Model Registry with version aliases to manage model versions and promote them after approval.. Vertex AI Model Registry provides a centralized repository for managing model versions, with support for version aliases (e.g., 'champion', 'challenger') that allow teams to promote models to production only after approval. This ensures governance and traceability, meeting the requirement that only approved models are deployed.

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Store all models in a Cloud Storage bucket and manually control access via IAM permissions.

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Deploy models directly from training jobs to an endpoint without version tracking.

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Use Cloud Dataflow to transform raw predictions and then store them in BigQuery for analysis.

Question 10

A company wants to monitor fairness of a model by evaluating performance metrics across demographic subgroups. They have ground truth labels stored in BigQuery. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Model Evaluation. Vertex AI Model Evaluation is the service designed to assess model performance using ground truth labels, including slicing metrics across demographic subgroups to detect fairness issues. It computes metrics like precision, recall, and AUC per slice, directly supporting bias and fairness analysis. This makes it the correct choice when labels are available in BigQuery.

Answer

Vertex AI Model Monitoring

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Vertex AI Prediction

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Vertex AI Explainability