PMLE Multiple Select — 20 Questions

Question 1

A company uses Vertex AI Model Monitoring. Which two configuration options can be set to reduce false positive drift alerts?

Accepted Answer

Use a sample percentage of predictions. Using a sample percentage of predictions reduces the volume of data analyzed for drift, which lowers the chance of detecting statistically insignificant fluctuations that could trigger false positive alerts. This is a common technique to filter out noise in high-throughput production systems.

Answer

Set a shorter alerting window

Answer

Decrease the drift threshold

Answer

Enable feature attribution monitoring

Question 2

Which TWO actions should be taken to ensure reproducibility of ML experiments when collaborating across teams on Vertex AI?

Accepted Answer

Lock dependency versions in a container image used for training. Locking dependency versions in a container image ensures that the exact same software environment (e.g., Python packages, CUDA libraries, system tools) is used every time a training job runs. This eliminates variability from package updates or OS patches, which is a fundamental requirement for reproducibility across teams. Vertex AI supports custom containers for training, making this a direct and reliable method.

Answer

Share notebooks via Colab Enterprise with real-time editing

Answer

Allow each team to use their own preferred environment

Answer

Always use random seeds for all random operations

Question 3

Which TWO strategies can help reduce the cost of running ML pipelines on Vertex AI?

Accepted Answer

Use Vertex AI managed datasets to reduce storage costs. Options B and D are correct. Option B is correct because using Vertex AI managed datasets can reduce storage costs by eliminating duplicate copies and providing efficient storage. Option D is correct because preemptible VMs are significantly cheaper than regular VMs, making them cost-effective for fault-tolerant training tasks. Option A is incorrect because hyperparameter tuning with a large search space can increase cost due to many trials. Option C is incorrect because manual scaling is less efficient and can lead to higher costs compared to automated scaling. Option E is incorrect because using a larger machine type for training usually increases cost, even if it reduces training time.

Answer

Run hyperparameter tuning jobs with a large search space

Answer

Manually scale up resources during peak times and scale down during off-peak

Answer

Use a larger machine type for training to complete faster

Question 4

An ML team is converting a prototype model to a production pipeline using Vertex AI. They want to ensure model versioning and lineage. Which two practices should they adopt? (Select TWO)

Accepted Answer

Use Vertex AI Model Registry to manage model versions.. Options A and C are correct. Using Vertex AI Model Registry allows you to manage and track model versions, while storing model artifacts in Cloud Storage with unique versioned directories ensures lineage and reproducibility. Option B is wrong because keeping only the latest version loses the history and lineage. Option D is wrong because training without tracking violates versioning principles. Option E is wrong because using separate GCP projects per version is unnecessarily complex and does not directly address versioning and lineage.

Answer

Only keep the latest model version to save storage.

Answer

Train models directly in production without tracking.

Answer

Use a separate GCP project for each model version.

Question 5

Which TWO options are best practices for reducing model serving latency on Vertex AI Endpoints? (Choose two.)

Accepted Answer

Optimize the model using quantization or pruning. Options B and C are correct. Optimizing the model using quantization or pruning reduces the model's size and computational requirements, directly decreasing per-request latency. Deploying the model in the same region as the clients minimizes network round-trip time, reducing overall serving latency. Option A (larger machine type) may increase throughput but does not necessarily reduce latency per request; Option D (batch prediction) is designed for high throughput, not low latency; Option E (model caching) is not a standard feature of Vertex AI endpoints for reducing latency.

Answer

Use a larger machine type with more memory

Answer

Use batch prediction instead of online prediction

Answer

Enable model caching at the endpoint

Question 6

A company runs batch predictions on a large dataset using Vertex AI Batch Prediction. They want to reduce costs without significantly increasing processing time. Which three actions should they take? (Choose three.)

Accepted Answer

Use preemptible VMs for the batch prediction job.. Options A, C, and E are correct. A uses preemptible VMs which are cheaper. C uses custom machine types to avoid overprovisioning and reduce costs. E tunes batch size to maximize throughput per worker, reducing the number of workers needed. Option B increases machine size, which may increase cost per worker. Option D uses TPUs, which are more expensive and may not be beneficial for all model types.

Answer

Use a larger machine type to reduce the number of workers.

Answer

Use TPUs instead of GPUs to accelerate processing.

Question 7

Which TWO are best practices for deploying models to Vertex AI Prediction? (Choose 2.)

Accepted Answer

Monitor prediction latency and error rates with Cloud Monitoring alerts.. The correct answers are A and C. Option A is a best practice because monitoring prediction latency and error rates with Cloud Monitoring alerts helps detect performance issues and ensure availability. Option C is a best practice because using a dedicated service account with minimal permissions follows the principle of least privilege, enhancing security. Option B is not a best practice because logging all raw inputs and outputs for every request can cause privacy concerns, increase costs, and is unnecessary for most use cases. Option D is not a best practice because deploying in the same environment as training may not always be feasible; instead, models should be containerized to ensure consistency across environments. Option E is not a best practice because using the default alias 'default' for all deployments can lead to confusion and makes rollbacks more difficult; proper versioning and aliases should be used.

Answer

Log all raw prediction inputs and outputs for every request for auditing.

Answer

Always deploy the model in the same environment as training to avoid incompatibility.

Answer

Use the default model version alias 'default' for all deployments to simplify updates.

Question 8

A company wants to use Vertex AI Vector Search for real-time product recommendations based on user embeddings. They need to update the index frequently with new product embeddings without significant downtime. Which TWO options should they consider? (Choose 2)

Accepted Answer

Maintain two separate indexes: one for building and one for serving, and swap them after building.. Maintaining two separate indexes allows you to build a new index in the background while the existing index continues to serve queries. Once the new index is fully built and validated, you can swap it into production with minimal downtime. This is a common pattern for high-availability systems where index rebuilds are necessary.

Answer

Use batch updates to rebuild the entire index daily.

Answer

Increase the number of replicas on the deployed index.

Answer

Use a GPU-powered endpoint for faster indexing.

Question 9

You are fine-tuning a large language model (LLM) from Vertex AI Model Garden using a custom dataset. You need to minimize training cost while maintaining reasonable throughput. Which THREE strategies should you combine?

Accepted Answer

Use spot VM instances for training. Spot VM instances are significantly cheaper than on-demand instances, reducing training cost. They can be preempted, but for fine-tuning tasks that can checkpoint and resume, this trade-off is acceptable for cost savings.

Answer

Use full fine-tuning of all model parameters

Answer

Use TPU v4 pods for training

Question 10

Which TWO actions can help reduce the latency of online prediction requests for a deep learning model served on Vertex AI?

Accepted Answer

Use a GPU accelerator for the deployed model.. Using a GPU accelerator speeds up inference, and batching requests reduces overhead per request. Minimizing replicas doesn't help latency; increasing CPU doesn't always help if GPU is better.

Answer

Increase the number of CPU vCPUs per machine.

Answer

Set min_replica_count to 0 to avoid idle instances.

Answer

Decrease the number of replicas to reduce resource contention.

Question 11

Which TWO actions are recommended to detect and mitigate data drift in a production ML system on Vertex AI?

Accepted Answer

Automatically retrain the model when drift exceeds thresholds. Vertex AI's automated retraining pipeline can be triggered when data drift exceeds a predefined threshold, ensuring the model adapts to distribution changes without manual intervention. Option D is correct because Vertex AI Model Monitoring continuously tracks feature distribution statistics (e.g., using Jensen-Shannon divergence or L-infinity distance) and sends alerts when drift is detected, enabling proactive mitigation.

Answer

Deploy multiple models and use an ensemble to average predictions

Answer

Manually review model predictions daily

Answer

Monitor prediction errors and flag when confidence is low

Question 12

A company runs a Vertex AI pipeline that uses a container component to preprocess data. The component downloads a large file from a public URL and saves the output to Cloud Storage. The pipeline fails intermittently with a 'timeout' error. Which THREE steps should the team take to improve reliability? (Choose three.)

Accepted Answer

Make the component idempotent by checking for existing output before processing.. Making the component idempotent by checking for existing output before processing prevents redundant work and avoids timeout failures when the file has already been downloaded. In Vertex AI pipelines, idempotent components can safely skip processing if the output already exists in Cloud Storage, reducing the risk of hitting timeout limits on subsequent pipeline runs.

Answer

Reduce the size of the file being downloaded.

Answer

Increase the machine type for the component.

Question 13

You are designing a batch prediction pipeline using Vertex AI. The input data is 100 TB of images stored in Cloud Storage. The model is a custom TensorFlow model that expects TFRecord format. The pipeline must be cost-effective and run within a time window of 2 hours. Which THREE steps should you include?

Accepted Answer

Create a Vertex AI batch prediction job with input from GCS (TFRecord files).. Vertex AI batch prediction jobs natively accept TFRecord files stored in Cloud Storage as input, which aligns with the requirement for a custom TensorFlow model. This approach is cost-effective and can complete within 2 hours by leveraging Vertex AI's managed infrastructure, avoiding the need to spin up and manage compute resources manually.

Answer

Store batch prediction results in BigQuery.

Answer

Use Cloud Functions to convert images to TFRecord.

Question 14

A company needs to reduce inference latency for their online prediction service on Vertex AI. Which two actions would help? (Choose 2)

Accepted Answer

Deploy the model on a GPU-enabled machine. Deploying the model on a GPU-enabled machine significantly accelerates matrix operations and parallel computations inherent in deep learning inference, directly reducing per-request latency. Option C is correct because model quantization reduces the precision of model weights (e.g., from FP32 to INT8), which decreases memory footprint and speeds up computation, especially on compatible hardware like TPUs or GPUs.

Answer

Increase the maximum number of replicas

Answer

Use a smaller machine type with less memory

Answer

Enable autoscaling with a lower target CPU utilization

Question 15

An organization wants to implement continuous delivery for their ML model. After a new model is trained and evaluated, they want to automatically deploy it to a staging endpoint, run validation tests, and if passed, promote to production. Which two components should they include in their delivery pipeline? (Choose two.)

Accepted Answer

Conditional component that checks evaluation metrics and promotes if successful. A conditional component that checks evaluation metrics (e.g., accuracy, precision, recall) against a predefined threshold is essential for automated promotion. This component acts as a gate, ensuring only models that meet quality criteria are promoted to production, which is a core requirement for continuous delivery in ML pipelines.

Answer

Dataflow job to preprocess data

Answer

Importer component to bring in the model

Answer

A/B testing component to split traffic

Question 16

A retail company deploys a new recommendation model alongside the current champion on Vertex AI Endpoints. They want to gradually shift traffic to the challenger while monitoring business metrics (conversion rate). Which two steps are required? (Choose 2)

Accepted Answer

Deploy the challenger model to the same endpoint as the champion with a separate deployed model.. Vertex AI Endpoints support deploying multiple model versions (champion and challenger) to the same endpoint, each as a separate deployed model. This allows the endpoint to serve both models simultaneously, enabling traffic splitting without requiring separate endpoints or infrastructure.

Answer

Use Vertex AI Experiments to track the traffic split percentages.

Answer

Enable Cloud Memorystore to cache identical requests for both models.

Answer

Use Cloud Monitoring to track custom metrics like conversion rate per model version.

Question 17

A data science team collaborates using Vertex AI Workbench user-managed notebooks. They want to version control their notebook code and share it with team members. Which TWO tools should they use? (Choose 2)

Accepted Answer

Git integration in Vertex AI Workbench. Git integration in Workbench allows version control, and sharing via Cloud Source Repositories or GitHub provides collaboration.

Answer

Cloud Functions

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 18

An ML engineer is building a monitoring dashboard for a Vertex AI pipeline that includes training, evaluation, and batch prediction. Which THREE components should be included to provide comprehensive observability? (Select THREE.)

Accepted Answer

Pipeline execution status, duration, and failure rates for each component.. Pipeline execution status, duration, and failure rates are fundamental metrics for monitoring the health and performance of a Vertex AI pipeline. These metrics allow the ML engineer to quickly identify bottlenecks, track overall workflow progress, and detect failures in training, evaluation, or batch prediction steps, which is essential for comprehensive observability.

Answer

Compute engine CPU and memory logs for each pipeline step.

Answer

Online prediction latency and request count from the deployed model endpoint.

Question 19

You are designing an ML pipeline for a large-scale recommendation system that runs weekly retraining on historical user interaction data. The pipeline uses TensorFlow and is deployed on Google Cloud. The pipeline must be orchestrated and automated with minimal manual intervention. Which THREE options should you include in your design? (Choose three.)

Accepted Answer

Use Vertex AI Pipelines to define the ML pipeline as a Directed Acyclic Graph (DAG) of components.. Vertex AI Pipelines (option B) is correct because it provides a managed, serverless orchestration service for building, testing, and deploying ML pipelines as Directed Acyclic Graphs (DAGs). This directly supports the requirement for automated, minimal-intervention weekly retraining by allowing you to define reusable components and schedule pipeline runs via Cloud Scheduler or event triggers, integrating natively with TensorFlow and Google Cloud services.

Answer

Use BigQuery scheduled queries to run the training script on a schedule.

Answer

Use AI Platform Notebooks to schedule the training job on a recurring basis.

Question 20

An ML engineer is deploying a large BERT-based natural language processing model for real-time inference on Vertex AI Prediction. The model has a large memory footprint (2GB) and experiences unpredictable traffic spikes up to 10x the baseline. The engineer needs to minimize latency and cost while handling spiky traffic. Which TWO actions should the engineer take? (Choose two.)

Accepted Answer

Use a custom prediction routine with model quantization to reduce model size.. Model quantization reduces the memory footprint of a BERT model (e.g., from 2GB to ~500MB with INT8 quantization), which directly lowers inference latency and cost by enabling faster loading and more efficient use of hardware. This is critical for real-time inference with unpredictable traffic spikes, as smaller models scale more easily and reduce the need for excessive replicas.

Answer

Configure the endpoint to use manual scaling with a fixed number of replicas equal to peak traffic.

Answer

Enable automatic scaling with a maximum of 3 replicas to limit cost.

Answer

Set up model monitoring to detect prediction drift and retrain regularly.