PMLE Practice Test 38 — 15 Questions

Question 1

A company wants to build a model to predict housing prices using BigQuery ML. They have a dataset with features like area, number of bedrooms, and location. Which TWO model types are appropriate for this regression task?

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

Accepted Answer

BOOSTED_TREE_REGRESSOR. BOOSTED_TREE_REGRESSOR (D) is appropriate because it is a tree-based ensemble method specifically designed for regression tasks, and BigQuery ML supports it via the `CREATE MODEL` statement with `model_type='BOOSTED_TREE_REGRESSOR'`. It handles non-linear relationships and interactions between features like area, bedrooms, and location, making it suitable for predicting continuous housing prices.

Answer

LOGISTIC_REG

Answer

K_MEANS

Answer

MATRIX_FACTORIZATION

Question 2

A team deploys a real-time model using a custom container on Vertex AI Prediction. The container is large (5 GB) and cold starts are causing latency spikes. The endpoint is configured with `min_replica_count=0` to reduce cost. The team wants to keep the cost low while reducing cold starts. What is the best approach?

Accepted Answer

Set `min_replica_count=1` to keep at least one replica always warm.. Setting min_replica_count=1 keeps at least one replica always provisioned and warm, eliminating cold starts for the first request after idle while still allowing autoscaling to add replicas under load. This directly addresses the latency spikes caused by the 5 GB container's slow initialization, and the cost of one always-on replica is typically far lower than the business impact of cold-start latency.

Answer

Use a prebuilt container for the model framework to reduce image size.

Answer

Enable container memory optimization to reduce startup time.

Answer

Provision a Persistent Disk (SSD) for the container image to speed up download.

Question 3

An ML engineer needs to trigger a Vertex AI Pipeline on a recurring schedule, every 24 hours, to retrain a model with the latest data. Which approach should they use to set up this schedule?

Accepted Answer

Create a Cloud Scheduler job that calls the Vertex AI API to create a pipeline job.. Cloud Scheduler is the native Google Cloud service for cron-based job scheduling. By configuring a Cloud Scheduler job to call the Vertex AI API (e.g., via a HTTP POST to the projects.locations.pipelineJobs.create endpoint), the engineer can trigger a pipeline run every 24 hours. This approach is reliable, supports authentication via OAuth, and integrates directly with Vertex AI Pipelines without requiring additional orchestration code.

Answer

Use Cloud Tasks to queue pipeline runs daily.

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Set a cron expression in the pipeline definition file using the 'schedule' parameter.

Answer

Use the Vertex AI Pipelines UI to set a schedule directly on the pipeline.

Question 4

A team is implementing CI/CD for ML using Cloud Build. They want to trigger a training pipeline in Vertex AI whenever a new model code is pushed to the main branch of the repository. Which Cloud Build configuration should they use to achieve this?

Accepted Answer

Set up a Cloud Build trigger that runs on push to main branch, and in the build step, use gcloud to submit a Vertex AI Pipeline job.. Cloud Build triggers can be configured to fire specifically on pushes to the main branch. The build step then uses the gcloud command to submit a Vertex AI Pipeline job, which directly integrates the CI/CD pipeline with Vertex AI's orchestration. This approach is event-driven, immediate, and requires no additional services or polling.

Answer

Set up a Cloud Build trigger that runs on push to any branch, and in the build step, use gcloud to submit a Vertex AI Pipeline job.

Answer

Use a Cloud Scheduler job to periodically check for new commits on main and trigger Cloud Build.

Answer

Use Cloud Functions to watch the repository and call Cloud Build on push to main.

Question 5

You deployed a model to a Vertex AI endpoint with minReplicas=0 and maxReplicas=5. After sending prediction requests, you notice the endpoint takes about 30 seconds to respond initially, but subsequent requests are fast. What is the most likely cause?

Accepted Answer

Cold start occurs because the endpoint scaled down to zero.. Vertex AI endpoints with minReplicas=0 scale down to zero when idle. The first request after a period of inactivity triggers a cold start, where the endpoint must provision a new VM instance and load the model, causing a ~30-second delay. Subsequent requests are fast because the instance remains warm and handles them without provisioning overhead.

Answer

The model is too large for the machine type.

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The VPC Service Controls are blocking the initial request.

Answer

The endpoint's autoscaling is misconfigured.

Question 6

A company deploys a training pipeline on Vertex AI using custom containers. The pipeline includes a hyperparameter tuning job that uses Bayesian optimization. After several runs, they observe that the tuning job is not converging and the search space is large. They want to reduce the number of trials while still finding good hyperparameters. Which strategy should they use?

Accepted Answer

Reduce the search space by applying feature selection and using prior knowledge.. Reducing the search space using prior knowledge directly decreases the number of trials needed. Option A is wrong because increasing parallel trials does not reduce the total number of trials. Option B is wrong because grid search generally requires more trials than Bayesian optimization. Option C is wrong because early stopping reduces time per trial but does not reduce the number of trials.

Answer

Increase the number of parallel trials to explore more points simultaneously.

Answer

Use Grid search instead of Bayesian optimization to systematically cover the search space.

Answer

Implement early stopping by using the 'early_stopping' flag in the hyperparameter tuning job.

Question 7

Two teams independently develop two different versions of a model for the same use case. They both deploy to the same Vertex AI endpoint, causing conflicts. What is the best way to manage multiple model versions and avoid conflicts in a collaborative environment?

Accepted Answer

Use Vertex AI Model Registry with staging and production channels, and implement CI/CD to control promotions. Vertex AI Model Registry with staging and production channels provides a centralized system to manage model versions, track lineage, and control promotions via CI/CD pipelines. This prevents conflicts by enforcing a structured workflow for version updates. Option A is wrong because separate projects increase management overhead and do not address versioning within the same endpoint. Option B is wrong because custom metadata lacks enforcement of deployment order and can lead to manual errors. Option C is wrong because deploying to separate endpoints does not resolve version conflicts; it merely isolates models, increasing complexity and cost.

Answer

Have each team work on a separate Google Cloud project

Answer

Use custom metadata to tag each version and rely on team coordination

Answer

Deploy each team's model to a separate endpoint

Question 8

An organization wants to trigger a Vertex AI pipeline whenever a new commit is pushed to the main branch of their Cloud Source Repository. The pipeline should retrain and evaluate the model. Which service should they use to detect the push event and start the pipeline?

Accepted Answer

Cloud Build trigger. Cloud Build triggers are designed to automatically invoke a build pipeline in response to events from Cloud Source Repository, such as a push to a specific branch. This allows the organization to directly start a Vertex AI pipeline for retraining and evaluation without additional infrastructure. Cloud Build triggers natively integrate with Cloud Source Repository, making them the simplest and most reliable choice for this event-driven workflow.

Answer

Vertex AI Pipeline schedule

Answer

Cloud Scheduler on a short interval

Answer

Pub/Sub with push subscription to a Cloud Function

Question 9

A company uses Vertex AI Matching Engine for a product recommendation system. They need to update the index with new product embeddings every hour, but the index is used for online queries with low latency. Which index update strategy should they use?

Accepted Answer

Use streaming updates to insert new embeddings incrementally. Streaming updates in Vertex AI Matching Engine allow incremental insertion of new embeddings into an existing index without rebuilding it. This satisfies the requirement for hourly updates while maintaining low-latency online queries, as the index remains available and consistent during the update process.

Answer

Use a hybrid approach with batch for daily full rebuild and streaming for hourly

Answer

Use batch updates to replace the index every hour

Answer

Recreate the index from scratch each hour

Question 10

You need to preprocess a large dataset (terabytes) for training a TensorFlow model. The preprocessing includes scaling and bucketizing features, and the same transformations must be applied during serving. Which tool should you use?

Accepted Answer

Dataflow with Apache Beam and tf.Transform. tf.Transform is a library built on Apache Beam that allows you to define preprocessing functions (like scaling and bucketizing) once and then apply them consistently in both training and serving. Dataflow provides a scalable, fully managed runner for Apache Beam pipelines, making it ideal for processing terabyte-scale datasets. This combination ensures that the exact same transformations are used during training and serving, avoiding training-serving skew.

Answer

Dataproc with Spark ML

Answer

Vertex AI Feature Store

Answer

BigQuery ML

Question 11

A fintech company needs to deploy a TensorFlow model for real-time fraud detection with strict latency SLO (p99 < 100ms). They expect variable traffic with spikes. They also want to minimize cold-start latency. Which two configurations should they use? (Choose 2)

Accepted Answer

Use a GPU-enabled machine type (e.g., N1 with T4) to accelerate inference.. Option B is correct because a GPU-enabled machine type such as an N1 instance with an NVIDIA T4 accelerator provides the parallel compute throughput needed to keep TensorFlow inference within a p99 latency SLO under 100ms, which CPU-only serving often cannot guarantee for larger models. Option C is correct because setting min_replicas = 3 keeps a baseline of warm, already-loaded model instances, eliminating cold-start latency for the initial requests and giving the autoscaler headroom to absorb traffic spikes without waiting for new replicas to initialize. Option A is not appropriate because min_replicas = 0 enables scale-to-zero, which directly reintroduces cold-start latency and violates the strict p99 < 100ms SLO. Option D is not selected because Vertex AI Model Optimization (quantization) is a model-compression technique that may reduce latency but is not a required configuration for meeting the SLO and can degrade accuracy. Option E is not selected because batch prediction processes data offline in bulk and cannot serve real-time fraud detection requests with sub-100ms latency.

Answer

Set min_replicas = 0 to allow scale-to-zero and save costs.

Answer

Enable Vertex AI Model Optimization for automatic quantization.

Answer

Use batch prediction instead of online prediction.

Question 12

A research team is training a very large Transformer model that does not fit into the memory of a single GPU. They have access to multiple GPUs on a single machine and want to split the model layers across GPUs. Which distributed training strategy should they use?

Accepted Answer

Pipeline parallelism (model parallelism). Pipeline parallelism (model parallelism) is the correct choice because it splits the model's layers across multiple GPUs, allowing a model too large for a single GPU's memory to be trained. Each GPU holds a subset of layers and processes micro-batches in a pipelined fashion, enabling training of models that exceed single-device memory. This directly addresses the memory constraint by distributing the model parameters, not just the data.

Answer

MultiWorkerMirroredStrategy

Answer

Parameter server strategy

Answer

MirroredStrategy (data parallelism)

Question 13

A retail company wants to predict customer churn using their transaction history and customer demographics. They have limited ML expertise and want to use a managed service on Google Cloud. Which service should they use?

Accepted Answer

Vertex AI AutoML (Tables). Vertex AI AutoML (Tables) is the correct choice because it is a managed service specifically designed for tabular data, requiring no ML expertise. It automates model training, hyperparameter tuning, and deployment for classification tasks like churn prediction, directly handling transaction history and demographic features.

Answer

AI Platform Notebooks

Answer

Cloud TPU

Answer

BigQuery ML

Question 14

A machine learning team needs to ensure that the same features used for training are used for serving in production to avoid training-serving skew. They use Vertex AI Feature Store. Which THREE actions should they take?

Accepted Answer

Enable point-in-time correct retrieval when creating training datasets. Option A is correct because point-in-time correct retrieval ensures training datasets are built from the exact feature values that were available at the time of each event, preventing label leakage and keeping training data consistent with what would have been served historically. Option C is correct because using the same feature view for both training data export and online serving guarantees that the identical feature transformation and source are used in both paths, which is the core defense against training-serving skew. Option E is correct because feature monitoring detects drift in feature distributions between training and serving, surfacing skew or data quality issues so the team can remediate them. Option B is incorrect because deliberately using different feature views for training and serving introduces skew rather than preventing it. Option D is incorrect because exporting training data directly from the online store is not the recommended pattern; training datasets should come from the offline store with point-in-time correctness, while the online store serves low-latency predictions.

Answer

Use different feature views for training and serving to compare performance

Answer

Export training data from the online store directly

Question 15

A team uses Vertex AI Metadata to track pipeline runs. They need to identify all artifacts that were generated by a particular pipeline execution. Which API method should they use?

Accepted Answer

Use the lineage query API with the execution ID. The Vertex AI Metadata lineage query API accepts an execution ID and returns all artifacts, contexts, and events connected to that execution, giving the full provenance graph in one call. This is the purpose-built method for tracing which artifacts a pipeline run produced.

Answer

List executions and then list artifacts separately

Answer

Create a context and query executions

Answer

Query artifacts by filter on execution ID