PMLE Practice Test 42 — 15 Questions

Question 1

Drag and drop the steps to deploy a trained TensorFlow model to Vertex AI Prediction in the correct order.

Accepted Answer

Export the model, then upload to GCS, then register as a model, then deploy to endpoint, then test. Export the model, upload to GCS, register as a model, deploy to endpoint, then test.

Answer

Upload to GCS, then export the model, then register as a model, then deploy to endpoint, then test

Answer

Export the model, then register as a model, then upload to GCS, then deploy to endpoint, then test

Answer

Export the model, then upload to GCS, then deploy to endpoint, then register as a model, then test

Question 2

You need to deploy a model that requires a large amount of memory (over 200 GB) for inference. The model is a custom PyTorch model. Vertex AI endpoints have machine type limitations. Which TWO actions can you take to handle this memory requirement? (Choose 2 correct answers)

Accepted Answer

Use a machine type from the n1-highmem series, such as n1-highmem-32 (208 GB) or higher.. The n1-highmem-32 machine type provides 208 GB of memory, which meets the requirement of over 200 GB. Vertex AI endpoints support this machine series, allowing you to deploy a custom PyTorch model with sufficient RAM for inference without modification.

Answer

Use multiple replicas and split the model across them.

Answer

Use batch prediction instead of online prediction.

Answer

Deploy the model on Cloud Run with 32 GB memory.

Question 3

A data science team uses BigQuery to store raw data and Vertex AI for model training. They want to ensure that only authorized users can access training data, and that model artifacts are automatically versioned and tracked. Which combination of Google Cloud services should they use?

Accepted Answer

Vertex AI Feature Store with access control and Vertex AI ML Metadata for model versioning. Vertex AI Feature Store provides fine-grained access control to training data, ensuring only authorized users can access it. Vertex AI ML Metadata automatically tracks and versions model artifacts, lineage, and parameters, which aligns with the requirement for automated versioning and tracking.

Answer

Dataflow for data access control and Vertex AI Experiments for model tracking

Answer

Cloud Storage with bucket-level IAM and Cloud Build for versioning

Answer

Cloud Composer for data access control and Cloud Source Repositories for model versioning

Question 4

A team is using Vertex AI Feature Store with an online store for low-latency serving. They notice increasing latency during peak hours. The feature data is updated frequently and requires strong consistency. Which online store type should they use?

Accepted Answer

Bigtable online store. Bigtable online store is recommended for high-throughput, low-latency, and strong consistency requirements.

Answer

Optimized online store

Answer

Cloud Spanner online store

Answer

Firestore online store

Question 5

A company is deploying a machine learning model for real-time fraud detection. The model must respond to requests within 100ms. The model is a TensorFlow model and will be deployed on Google Kubernetes Engine (GKE). Which Google Cloud service should be used to serve the model to minimize latency?

Accepted Answer

Deploy TensorFlow Serving on GKE with a LoadBalancer service.. Deploying TensorFlow Serving directly on GKE with a LoadBalancer service provides the lowest-latency path for real-time inference. TensorFlow Serving is optimized for high-performance model serving with batching and gRPC support, and GKE allows fine-grained control over node placement, autoscaling, and networking to meet the 100ms SLA. In contrast, serverless options like Cloud Run or Cloud Functions add cold-start latency and lack the low-level optimization for TensorFlow models.

Answer

Deploy the model on Cloud Run with minimum instances set to 1.

Answer

Deploy the model as a Cloud Function triggered by HTTP requests.

Answer

Deploy the model on Vertex AI Prediction with a custom container.

Question 6

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.. The requirement specifies weekly retraining, which is best implemented as a scheduled training pipeline in Vertex AI using Cloud Scheduler or a recurring AI Platform Pipeline run. This automates the retraining process without manual intervention, ensuring the model stays current with new data.

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 7

A data scientist wants to automate the retraining of a model when new data arrives in Cloud Storage. Which Google Cloud service is most appropriate for orchestrating this workflow?

Accepted Answer

Cloud Composer. Cloud Composer (D) is the most appropriate service for orchestrating a retraining workflow because it is a fully managed workflow orchestration service built on Apache Airflow. It allows you to define a Directed Acyclic Graph (DAG) that triggers model retraining when new data arrives in Cloud Storage, handling dependencies, scheduling, and monitoring across multiple steps such as data validation, training, and deployment.

Answer

Cloud Run

Answer

Vertex AI Predictions

Answer

Cloud Scheduler

Answer

Cloud Functions

Question 8

A data scientist needs to retrieve training data from Vertex AI Feature Store that exactly matches the feature values as they were at a specific historical timestamp to avoid label leakage. Which feature view configuration should they use?

Accepted Answer

Enable point-in-time retrieval on the feature view.. Point-in-time retrieval is a feature of Vertex AI Feature Store that returns feature values as of a specified timestamp.

Answer

Use the offline store without point-in-time and rely on data ordering.

Answer

Use the online store with a timestamp filter.

Answer

Create a new feature view with only historical data.

Question 9

A machine learning team wants to perform A/B testing between two model versions (v1 and v2) on Vertex AI Endpoint. They need to gradually route 10% of traffic to v2 while monitoring performance. What is the most efficient way to achieve this?

Accepted Answer

Deploy both versions to the same endpoint and set traffic_split to 90% for v1 and 10% for v2.. Vertex AI Endpoint natively supports traffic splitting between model versions. Option A is wrong because creating separate endpoints adds complexity and cost. Option C is wrong because Cloud Load Balancing operates at the network level, not model level. Option D is wrong because batch prediction is not for real-time A/B testing.

Answer

Use a Cloud Load Balancer to route traffic based on a header.

Answer

Create two separate endpoints and use a weighted DNS round-robin.

Answer

Run batch predictions for v2 and log results separately.

Question 10

A team of data scientists and ML engineers is collaborating on a shared feature store in Vertex AI Feature Store. They need to ensure that feature definitions are versioned and that changes are reviewed before being used in production pipelines. Which TWO practices should they implement?

Accepted Answer

Require code reviews for all changes to feature definitions before merging to the main branch.. Requiring code reviews for all changes to feature definitions before merging to the main branch enforces a peer-review gate, ensuring that modifications are validated for correctness, consistency, and compliance before they reach production. This aligns with MLOps best practices for governance and reduces the risk of introducing errors or breaking changes into the feature store.

Answer

Allow data scientists to edit feature definitions directly in the Vertex AI Feature Store console.

Answer

Define multiple feature views in Vertex AI Feature Store for different environments and manage access via IAM.

Answer

Use scheduled batch jobs to synchronize feature definitions from a shared spreadsheet to Vertex AI Feature Store.

Question 11

A data science team deploys a PyTorch model using Vertex AI Prediction. The model requires GPU for inference, but they notice high costs and underutilized GPUs during off-peak hours. What is the most cost-effective solution?

Accepted Answer

Use a GPU instance with min replicas=0 and autoscaling. Setting min replicas to 0 allows Vertex AI Prediction to scale down to zero instances during off-peak hours, eliminating GPU costs when no requests are being served. Combined with autoscaling, the deployment will spin up GPU-backed instances on demand only when traffic arrives, directly addressing the underutilization issue while maintaining low latency for inference requests.

Answer

Move the model to Cloud Functions

Answer

Use a GPU instance with a fixed number of replicas

Answer

Switch to a CPU-only machine type

Question 12

A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 rows and 50 features, including loan amount, credit score, debt-to-income ratio, and employment length. The target variable is binary: 'default' (1) or 'no default' (0). The data is highly imbalanced, with only 2% defaults. The data scientist trains a model with AutoML Tables using default settings. The evaluation metrics show an AUC of 0.85, but the confusion matrix reveals that the model predicts 'no default' for almost all cases, missing most defaults. The data scientist needs to improve the model's ability to identify defaults without significantly increasing false positives. They have limited time and cannot write custom code. What should they do?

Accepted Answer

Enable 'Enable weighted evaluation' and set the optimization objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5.. AutoML Tables allows you to set a custom optimization objective to handle class imbalance without custom code. By enabling weighted evaluation and setting the objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5, the model will be tuned to prioritize identifying defaults (recall) while maintaining a specified precision level, directly addressing the need to catch more defaults without a massive increase in false positives.

Answer

Manually split the data into a stratified train/test set to ensure the same proportion of defaults in each.

Answer

Train multiple models with different algorithms (e.g., XGBoost, Random Forest) and blend them using a custom script.

Answer

Under-sample the majority class to create a balanced dataset and retrain.

Question 13

A logistics company uses Vertex AI AutoML Tables to predict delivery delays based on order attributes, weather data, and traffic data. The model is retrained weekly using a Vertex AI Pipeline that runs a BigQuery query to get training data, then triggers AutoML training. Recently, the pipeline fails with the error 'Dataset not found' when the AutoML training step starts. The BigQuery query runs successfully and outputs a table. Which is the most likely cause?

Accepted Answer

The BigQuery output table is not being passed as a Vertex AI Dataset resource.. The error 'Dataset not found' occurs because AutoML Tables requires a Vertex AI Dataset resource (a metadata wrapper) to reference the training data, not just a BigQuery table. The pipeline's BigQuery query produces a table, but if that table is not explicitly converted into or passed as a Vertex AI Dataset resource (via the `aiplatform.Dataset` creation step), AutoML training cannot locate it. Option D correctly identifies this missing step as the root cause.

Answer

The AutoML training step is referencing a different dataset location.

Answer

The training data has been manually deleted from Cloud Storage.

Answer

The pipeline's IAM permissions are insufficient to access BigQuery.

Question 14

An ML engineer is training a very large PyTorch model on Vertex AI using a TPU v3 pod. The training is slower than expected, and the TPU utilization is low. What is the most likely cause?

Accepted Answer

The data pipeline is a bottleneck; the TPU is waiting for data.. The most likely cause of low TPU utilization is a data pipeline bottleneck, where the TPU spends a significant amount of time idle waiting for the next batch of data to be loaded and preprocessed. TPU v3 pods are designed for high-throughput matrix operations and can process data far faster than a typical CPU-based data loader can supply it, especially if the data pipeline uses inefficient I/O, lacks prefetching, or has insufficient workers. This mismatch starves the TPU, leading to low utilization and slower training.

Answer

The learning rate schedule is too aggressive.

Answer

The model is using a single TensorFlow operation not supported by TPU.

Answer

The batch size is too large for the TPU memory.

Question 15

You need to perform batch predictions on 10 TB of data stored in BigQuery using Vertex AI. The model requires some preprocessing that cannot be expressed in SQL. What is the most scalable approach?

Accepted Answer

Use Dataflow to read from BigQuery, perform preprocessing, write results to GCS, then run Vertex AI batch prediction job with GCS source.. Dataflow (Apache Beam) provides a fully managed, auto-scaling, serverless execution environment that can read from BigQuery, apply arbitrary Python/Java preprocessing logic (e.g., feature engineering, normalization) that cannot be expressed in SQL, and write the preprocessed results to Cloud Storage (GCS). Vertex AI batch prediction can then read from GCS as input, making this the most scalable approach for 10 TB of data without requiring custom model container changes or manual data movement.

Answer

Use a Cloud Function to preprocess each row and write to a new BigQuery table, then run batch prediction.

Answer

Use Vertex AI batch prediction with BigQuery source and include preprocessing logic in the model container.

Answer

Export BigQuery data to CSV, run a local Python script for preprocessing, then upload to GCS and start a batch prediction job.