PMLE Multiple Select — 40 Questions

Question 1

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?

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

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 2

A data analyst wants to use BigQuery ML to train a linear regression model (LINEAR_REG) to predict house prices. They have a table with features like square footage, number of bedrooms, and location. Which TWO statements about the training process are correct?

Accepted Answer

The model is automatically evaluated on a held-out test set if data splitting is enabled. When data splitting is enabled in BigQuery ML, the `CREATE MODEL` statement automatically reserves a portion of the input data as a held-out test set. After training completes, BigQuery ML evaluates the model on this test set and reports metrics like mean absolute error and R², without requiring any manual split or separate evaluation step.

Answer

The analyst must call ML.TRAIN after CREATE MODEL to start training

Answer

The trained model is stored in Cloud Storage

Answer

The model must be exported to Vertex AI for prediction

Question 3

A company has a prototype ML model that predicts equipment failure. They want to deploy it to production using Vertex AI. The model must be retrained weekly with new data. They also need to monitor for data drift and model performance. Which THREE components should they include in their MLOps pipeline? (Choose 3)

Accepted Answer

A scheduled training pipeline that retrains the model weekly.. Option A is correct because the requirement to retrain weekly with new data is met by a scheduled training pipeline in Vertex AI, typically implemented with Vertex AI Pipelines and a Cloud Scheduler or scheduled run that re-executes the training job on the latest data. Option D is correct because automated redeployment when performance drops below a threshold is a core MLOps practice; in Vertex AI this can be done by monitoring model performance and using a trigger (for example, a Cloud Function or Eventarc/Cloud Monitoring alert) to promote a newly trained model to an endpoint. Option E is correct because Vertex AI Model Monitoring detects data drift and can emit alerts via Cloud Monitoring, which is exactly what the scenario requires for drift and performance oversight. Option B is not correct because a manual QA approval step for every deployment is not required by the scenario and would slow down the automated weekly retraining and redeployment workflow. Option C is not correct because manual review of all new data before training is not a required MLOps component here and would prevent the pipeline from being fully automated and scheduled weekly.

Answer

A manual QA step where data scientists approve each deployment.

Answer

A manual review of new data before it is used for training.

Question 4

A machine learning team is collaborating on a project using Vertex AI Experiments to track model training runs. They want to ensure that all team members can reproduce any experiment by using the same code, data, and environment. Which THREE actions should the team take?

Accepted Answer

Store the training code in a Cloud Source Repository and tag commits with the experiment ID.. Option A is correct because storing the training code in Cloud Source Repositories and tagging commits with the experiment ID creates an immutable, versioned link between the exact code revision and the Vertex AI Experiment run, which is essential for reproducibility. Option B is correct because packaging the training environment in a custom container image pushed to Artifact Registry with a fixed (immutable) tag guarantees every team member runs the identical dependencies, libraries, and runtime, eliminating environment drift. Option C is correct because logging the dataset path and version as experiment parameters in Vertex AI Experiments captures the exact data snapshot used, so the same inputs can be retrieved and reused. Option D is not appropriate because sharing a service account key is an insecure credential-management anti-pattern and does not itself contribute to code, data, or environment reproducibility. Option E is not appropriate because hyperparameter tuning optimizes model performance and does not by itself ensure that code, data, and environment are pinned and reproducible.

Answer

Share a service account key with all team members so they can access the same resources.

Answer

Use Vertex AI's hyperparameter tuning job to automatically find the best parameters.

Question 5

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 6

A financial services company has deployed a credit risk ML model on Vertex AI. They want to monitor the model for fairness across demographic groups to ensure no biased outcomes. Which TWO actions should they take as best practices? (Choose TWO.)

Accepted Answer

Use Vertex Explainable AI to understand feature attributions and compare their distributions across demographic groups.. Vertex Explainable AI provides feature attribution scores that can be compared across demographic groups to detect if the model relies on sensitive attributes or proxies. This enables fairness auditing by revealing whether the model's decision logic differs systematically for protected groups, which is a best practice for monitoring bias.

Answer

Eliminate all features that are correlated with protected attributes from the model input to ensure fairness.

Answer

Periodically compare the model's performance metrics (e.g., AUC) on the overall population versus the holdout test set.

Answer

Store all model predictions in BigQuery but do not capture ground truth labels to avoid privacy issues.

Question 7

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.. Option A is correct because tracking pipeline execution status, duration, and failure rates per component gives the operational observability needed to detect where a training, evaluation, or batch prediction step fails or slows down. Option C is correct because model evaluation metrics such as accuracy and AUC after training and validation are essential to monitor model quality and detect degradation before deployment. Option D is correct because data validation reports with anomaly counts and feature statistics surface input data drift, skew, and schema issues that directly affect pipeline and model reliability. Option B is not appropriate because raw Compute Engine CPU and memory logs for each step are low-level infrastructure telemetry, not the pipeline-level observability components the scenario requires. Option E is not appropriate because online prediction latency and request counts apply to a deployed endpoint, whereas this pipeline uses batch prediction and has no online serving endpoint.

Answer

Compute engine CPU and memory logs for each pipeline step.

Answer

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

Question 8

A data scientist needs to scale a prototype deep learning model to train on a massive dataset using multiple GPUs. Which three strategies are essential for efficient distributed training? (Select THREE)

Accepted Answer

Implement data parallelism.. Option B is correct because data parallelism is the foundational strategy for multi-GPU training: the model is replicated on each GPU and each replica processes a different shard of the massive dataset, with gradients aggregated across workers to scale throughput. Option C is correct because at multi-GPU scale the input pipeline can easily starve the accelerators; using tf.data.Dataset with parallel reads (num_parallel_reads / interleave) and prefetching (prefetch(AUTOTUNE)) overlaps data loading and preprocessing with GPU computation so the GPUs are not idle. Option D is correct because synchronous gradient updates (e.g., all-reduce via NCCL, as in tf.distribute.MirroredStrategy) aggregate gradients from all workers before applying the optimizer step, which keeps replicas consistent and yields stable, reproducible convergence for deep learning training. Option A is not correct because a single large batch size is not itself an essential distributed-training strategy; batch size is a tuning choice, and naively enlarging it can hurt convergence and may not fit in memory. Option E is not correct because asynchronous gradient updates are an alternative to, not a requirement for, efficient distributed training, and they can introduce stale gradients that degrade model accuracy and reproducibility.

Answer

Use a single large batch size across all workers.

Answer

Use asynchronous gradient updates to reduce communication overhead.

Question 9

Which THREE actions should be taken to automate a machine learning pipeline using Cloud Build and Vertex AI?

Accepted Answer

Write a cloudbuild.yaml that builds a training container and submits a Vertex AI PipelineJob. Option A is correct because a cloudbuild.yaml file is the declarative configuration Cloud Build uses to define build steps, and one of those steps can build a custom training container image (e.g., via docker build) and then submit a Vertex AI PipelineJob using the gcloud ai pipelines run command or the Vertex AI SDK. Option D is correct because Vertex AI Pipelines is the native service for orchestrating ML workflow steps such as training, evaluation, and deployment as a directed acyclic graph, and Cloud Build can invoke that pipeline as part of CI/CD, giving a fully automated, reproducible ML pipeline. Option E is correct because a Cloud Build trigger bound to the source repository (e.g., a GitHub or Cloud Source Repositories trigger on push or pull request) is what actually initiates the automated build/pipeline run on commits, which is the core of CI/CD automation. Option B is not appropriate because Cloud Functions is not the intended mechanism for retraining models on build completion; Cloud Build triggers or Pub/Sub notifications are the correct integration points, and this adds unnecessary complexity. Option C is not appropriate because polling for build artifacts with Cloud Scheduler is an anti-pattern; event-driven triggers (Cloud Build triggers or Pub/Sub) are preferred over scheduled polling for reacting to new builds.

Answer

Use Cloud Functions to retrain the model each time a build completes

Answer

Set up a Cloud Scheduler job to poll for new build artifacts

Question 10

An ML pipeline must run a set of preprocessing tasks for each data shard in parallel. Which KFP SDK features should they use to implement this? (Choose two.)

Accepted Answer

dsl.ParallelFor. dsl.ParallelFor (A) is correct because it is the KFP SDK construct that iterates over a list (such as the set of data shards) and fans out the loop body into parallel task executions, which is exactly what is needed to run preprocessing per shard concurrently. dsl.Collected (C) is correct because it is used with dsl.ParallelFor to gather the outputs of all parallel iterations into a single list, allowing downstream pipeline steps to consume the aggregated results of the per-shard preprocessing. dsl.PipelineParam (B) is not the mechanism for parallel iteration; it only represents a runtime parameter passed into a pipeline or component. dsl.Condition (D) implements conditional branching (if/else) rather than parallel fan-out, and dsl.ExitHandler (E) defines cleanup logic that runs when a scope exits, neither of which provides the required parallel execution over shards.

Answer

dsl.PipelineParam

Answer

dsl.Condition

Answer

dsl.ExitHandler

Question 11

A company wants to analyze videos to detect objects and track their movement over time. Which TWO Google Cloud services are suitable for this task?

Accepted Answer

AutoML Video. AutoML Video supports object tracking, and Video Intelligence API provides both object detection and tracking. AutoML Vision is for images only, Natural Language for text, and Speech-to-Text for audio.

Answer

AutoML Vision

Answer

Speech-to-Text

Answer

Natural Language API

Question 12

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.

Answer

Use manual scaling with a fixed number of replicas.

Answer

Deploy all models on the same machine type for consistency.

Question 13

An ML team is optimizing an inference model for deployment on edge devices. They need to reduce the model size and improve latency while maintaining accuracy as much as possible. Which two techniques should they use? (Choose TWO.)

Accepted Answer

Post-training quantization to INT8.. Post-training quantization to INT8 reduces model size by converting 32-bit floating-point weights and activations to 8-bit integers, which also speeds up inference on edge devices with integer-optimized hardware. This technique typically maintains accuracy within 1-2% of the original model while significantly lowering memory footprint and latency.

Answer

Use a larger pre-trained model as a starting point.

Answer

Use half-precision (FP16) instead of INT8.

Answer

Increase the number of layers in the model.

Question 14

Which TWO practices are important when scaling a prototype ML model to production on Google Cloud? (Choose two.)

Accepted Answer

Set up model monitoring for data drift and concept drift. Option A is correct because production ML models degrade as input data distributions shift (data drift) and as the relationship between features and labels changes (concept drift), so Vertex AI Model Monitoring must track these and trigger retraining or alerts. Option E is correct because scaling to production requires reproducible, automated MLOps: CI/CD pipelines (e.g., Cloud Build, Vertex AI Pipelines) automate training, evaluation, and deployment, ensuring consistency and fast iteration. Option B is wrong because manual feature engineering per iteration does not scale and should be replaced by automated feature pipelines (e.g., Vertex AI Feature Store). Option C is wrong because a single high-memory Compute Engine VM is a non-scalable, non-resilient deployment; production serving should use managed, horizontally scalable endpoints like Vertex AI Prediction. Option D is wrong because proprietary, lock-in-heavy libraries hinder portability, maintainability, and integration with Google Cloud's managed ML services.

Answer

Manually engineer features for each training iteration

Answer

Run the model on a single high-memory Compute Engine VM

Answer

Use proprietary libraries to maximize performance regardless of lock-in

Question 15

A data scientist is creating a Vertex AI pipeline using the Kubeflow Pipelines SDK v2. Which TWO statements about pipeline parameters are correct? (Choose two.)

Accepted Answer

Pipeline parameters are defined as inputs to the pipeline function decorated with @dsl.pipeline.. Option A is correct because in the Kubeflow Pipelines SDK v2, pipeline parameters are declared as typed arguments of the function decorated with @dsl.pipeline, which defines the pipeline's input interface. Option D is correct because these parameters are runtime inputs: when submitting a run (e.g., via the Vertex AI Pipelines API or the SDK's create_run_from_pipeline_func), you can supply new values that override the defaults specified in the pipeline definition. Option B is wrong because KFP v2 handles parameter serialization automatically; you do not manually JSON-serialize parameters before use. Option C is wrong because KFP v2 parameters support multiple types such as int, float, bool, str, list, and dict, not only str. Option E is wrong because parameters are intended for small scalar/structured configuration values; large datasets should be passed between components as artifacts (e.g., Dataset, Model), not as parameters.

Answer

Pipeline parameters must be serialized to JSON before use.

Answer

Pipeline parameters can only be of type str.

Answer

Pipeline parameters can be used to pass large datasets between components.

Question 16

A team is troubleshooting a Vertex AI Pipelines run that keeps failing at the model evaluation step. The pipeline includes steps: data preprocessing, training, evaluation, and deployment. Which THREE actions should they take to diagnose the issue?

Accepted Answer

Verify that the training step output is correctly linked as input to evaluation.. Option A is correct because Vertex AI Pipelines passes data between steps through declared input/output artifacts, so if the training step's output artifact is not correctly wired as the evaluation step's input, the evaluation component will fail from missing or malformed input. Option B is correct because running the evaluation code locally with the same input data isolates the component logic from the pipeline orchestration, quickly revealing whether the failure is in the code or in the pipeline configuration. Option D is correct because Cloud Logging captures the evaluation step's container stdout/stderr and error messages, which is the primary way to see the actual exception raised during the pipeline run. Option C is not appropriate because increasing memory is a speculative resource change that does not diagnose the root cause and would only help if an out-of-memory error were already confirmed. Option E is not appropriate because swapping the evaluation step for Vertex AI Model Evaluation changes the pipeline rather than diagnosing the existing failure, and it may not even be compatible with the current pipeline's inputs and outputs.

Answer

Increase the memory of the evaluation step's machine.

Answer

Replace the evaluation step with a Vertex AI Model Evaluation service.

Question 17

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. Option B is correct because quantization (e.g., reducing weights from FP32 to INT8) and pruning (removing redundant parameters) shrink the model size and reduce the compute required per inference, directly lowering prediction latency on Vertex AI Endpoints. Option C is correct because deploying the model in the same region as the clients minimizes network round-trip time, which is a significant component of end-to-end serving latency for online predictions. Option A is not a best practice for latency specifically: a larger machine with more memory may help with throughput or memory-bound models, but it does not inherently reduce per-request latency and increases cost. Option D is wrong because batch prediction is an asynchronous, offline mode that does not serve real-time requests and is not a latency optimization for online endpoints. Option E is not a supported Vertex AI Endpoint feature; there is no endpoint-level 'model caching' toggle that reduces serving 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 18

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. Option A is correct because integration with Vertex AI enables managed model monitoring, which is essential for detecting training-serving skew, drift, and performance degradation in production deep learning deployments. Option B is correct because autoscaling capabilities allow the serving infrastructure to dynamically adjust compute resources based on traffic patterns, ensuring cost efficiency and consistent latency under variable load. Option C is correct because deep learning inference often requires GPU or TPU acceleration to meet latency and throughput requirements, and the chosen compute option must support the necessary hardware accelerators. Option D is not a relevant factor because the programming language used during training does not constrain the production serving compute option, as models are typically exported to framework-agnostic formats or served via standard runtimes. Option E is clearly irrelevant because the team's logo color has no bearing on compute selection for model serving.

Answer

The programming language used for training

Answer

The color of the team's logo

Question 19

An ML engineer is building a continuous training pipeline that retrains a model when new data arrives. The pipeline should also detect skew between training and serving data. Which TWO Google Cloud services should they use? (Choose two.)

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Pipelines (D) is correct because it is the managed service for orchestrating and automating ML workflows, allowing the engineer to define a pipeline that triggers retraining when new data arrives. Vertex AI Model Monitoring (B) is correct because it is specifically designed to detect training-serving skew and drift by comparing the statistical distribution of incoming prediction requests against the training data baseline. Together, these two services directly satisfy both requirements: continuous training orchestration and skew detection. Cloud Logging (A) only stores and queries log entries and does not orchestrate pipelines or compute skew. Cloud Functions (C) is a lightweight event-driven compute service that could trigger code but lacks native ML pipeline orchestration and skew detection. Cloud Monitoring (E) tracks operational metrics and alerts but does not perform training-serving skew analysis for ML models.

Answer

Cloud Logging

Answer

Cloud Functions

Answer

Cloud Monitoring

Question 20

A data scientist is training a very large neural network using Vertex AI with multiple GPUs across multiple nodes. The model does not fit on a single GPU, so they need to use both data parallelism and model parallelism (pipeline parallelism). Which THREE components or configurations are required to set up distributed training with Vertex AI?

Accepted Answer

Implementing pipeline parallelism manually in the training script using torch.distributed.pipeline.sync.Pipe. Pipeline parallelism requires explicit implementation in the training script, such as using `torch.distributed.pipeline.sync.Pipe` in PyTorch, to split the model layers across multiple GPUs. This is necessary when the model does not fit on a single GPU, and Vertex AI does not automatically handle model parallelism—it must be coded by the user.

Answer

Using Vertex AI Vizier to optimize the model parallelism strategy

Answer

Enabling Vertex AI AutoML to automatically distribute the model