PMLE Practice Exam 53 — 20 Questions

Question 1

An ML engineer is using Vertex AI Pipelines to orchestrate a training workflow. The pipeline must run on a schedule every day at 2:00 AM UTC. The engineer wants to use a fully managed Google Cloud service to trigger the pipeline. Which service should the engineer use?

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

Accepted Answer

Cloud Scheduler. Cloud Scheduler is the fully managed service for cron-based scheduling in Google Cloud. It can directly invoke the Vertex AI Pipelines API endpoint on a schedule, such as daily at 2:00 AM UTC. Other services either lack native scheduling or require additional components to achieve the same result.

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Cloud Composer

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Cloud Functions

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Cloud Tasks

Question 2

You are deploying a scikit-learn model to a Vertex AI endpoint for real-time inference. Prediction requests arrive as JSON payloads containing a single instance per request, and the model's predict method expects a pandas DataFrame with named columns. You want to avoid writing a custom container. Which approach should you take?

Accepted Answer

Upload the model artifact with a pre-built Vertex AI scikit-learn container and supply a custom prediction routine (predictor.py) that converts the decoded JSON dict into a pandas DataFrame before calling predict.. A custom prediction routine gives you a managed pre-built runtime plus a Python package that overrides preprocessing, which is the supported way to adapt request payloads to a model's expected input format. Because the estimator requires named DataFrame columns, the override must build that DataFrame explicitly; the default Predictor performs no such conversion and other pre-built containers cannot load the estimator at all.

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Upload the model artifact with a pre-built Vertex AI XGBoost container, because that container internally wraps every scikit-learn estimator and feeds it a DataFrame.

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Upload the model artifact with a pre-built TensorFlow container and wrap the scikit-learn estimator in a tf.function so the container's serving signature handles DataFrame creation.

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Upload the model artifact with a pre-built Vertex AI scikit-learn container and rely on its default Predictor, which automatically converts JSON objects into a pandas DataFrame with matching column names.

Question 3

A retail company has a model deployed on a Vertex AI Endpoint that predicts customer lifetime value. The ML team wants to monitor the model for feature drift without setting up a full Vertex AI Model Monitoring job. They need a lightweight solution that compares live prediction requests to a reference distribution and sends alerts when drift exceeds a threshold. What should they do?

Accepted Answer

Log prediction requests to BigQuery and use a scheduled query to compute drift metrics, then alert via Cloud Monitoring.. A lightweight drift monitoring solution can be built by logging prediction requests to BigQuery, then using scheduled queries to compute drift metrics against a reference distribution. Cloud Monitoring can alert when drift exceeds a threshold. This avoids the complexity of a full Vertex AI Model Monitoring job while still providing automated alerts. Manual inspection or using audit logs does not meet the need for automated, data-based drift detection.

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Use Cloud Audit Logs to track prediction requests and set up log-based alerts.

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Enable request-response logging on the endpoint and manually inspect logs daily.

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Use Vertex AI Model Monitoring with default settings and a sampling rate of 1.0.

Question 4

Your team has deployed a model to a Vertex AI endpoint and wants to route a small percentage of live traffic to a new model version for evaluation. You need to split traffic at the endpoint level without changing the client application. What should you do?

Accepted Answer

Deploy both model versions to the same endpoint and set a traffic split percentage.. Deploying multiple models to a single endpoint and configuring a traffic split is the native Vertex AI mechanism for canary or A/B testing. The endpoint continues to expose one URL, so clients remain unchanged, and the split percentage controls how much live traffic reaches each model version. This enables safe evaluation of the new version with minimal risk.

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Create two separate endpoints and use a load balancer to distribute traffic between them.

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Deploy the new model version as a separate endpoint and update the client to call both endpoints.

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Use a Vertex AI batch prediction job to send a percentage of live traffic to the new model.

Question 5

A retail company has deployed a demand forecasting model on a Vertex AI Endpoint. The model uses 20 numeric features. The MLOps team wants Vertex AI Model Monitoring to detect training-serving skew for each feature and receive alerts when skew exceeds a threshold. They have enabled Model Monitoring for the endpoint and configured a monitoring frequency of every 24 hours. However, after several days, no skew metrics appear in the Vertex AI console. What is the most likely cause?

Accepted Answer

The training dataset used for the baseline was not provided or the baseline statistics were not generated.. Training-serving skew requires a baseline distribution from the training data. Without providing the training dataset or generating baseline statistics during monitoring configuration, Vertex AI cannot compute skew. The monitoring frequency, request volume, and prediction logging are not the cause. The team must supply the training data or enable baseline generation to see skew metrics.

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The monitoring frequency must be set to every 1 hour for skew detection to work.

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The endpoint must have at least 1000 prediction requests per day for skew metrics to appear.

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The model's predictions must be logged to BigQuery before skew can be detected.

Question 6

You have a prototype model trained on a single machine using scikit-learn. You now need to scale training to a larger dataset that does not fit in memory on one machine. You want to use Vertex AI training with minimal changes to your existing scikit-learn code. What should you do?

Accepted Answer

Use Vertex AI Training with a custom container that runs your scikit-learn script, and use a distributed framework such as Dask or Ray to partition the data across multiple workers.. The best approach is to keep the scikit-learn code and run it in a custom container on Vertex AI, using a distributed framework like Dask or Ray to partition the data across workers. This scales beyond a single machine's memory while minimizing changes to the original training script. The other options either rely on incompatible distribution strategies, assume automatic distribution that does not exist, or require a full rewrite.

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Convert the scikit-learn model to a TensorFlow model and use MirroredStrategy to train across multiple GPUs.

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Use Vertex AI Training with the pre-built scikit-learn container and set the worker count to more than one; Vertex AI will automatically distribute the scikit-learn training.

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Use Vertex AI distributed training with a ParameterServerStrategy and wrap your scikit-learn estimator in a TensorFlow estimator.

Question 7

Your organization uses Vertex AI Pipelines for training. A compliance auditor asks you to prove which dataset version and which preprocessing code commit produced a model that is currently deployed. You need to retrieve this information programmatically for a specific model version. Which approach should you use?

Accepted Answer

Use Vertex ML Metadata to traverse the lineage from the model artifact to its parent execution and input artifacts.. Vertex ML Metadata is the system of record for lineage, linking artifacts such as models and datasets through executions. Traversing lineages from the model artifact to its parent execution and input artifacts yields the dataset version and code commit programmatically, which is exactly what an auditor needs.

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List the endpoint's deployed models and read the model description field, which automatically records the dataset version and code commit.

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Inspect the model's Cloud Storage directory for a metadata.json file that lists the dataset and code commit.

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Query Vertex AI Experiments for the run that has the same display name as the model version.

Question 8

You are deploying a model to a Vertex AI endpoint that will serve predictions for a mobile application. The application sends a single request per user action and expects a response within 100 ms. The model is small and CPU-bound. You want to minimize cost while meeting the latency requirement. Which endpoint configuration should you choose?

Accepted Answer

Use a CPU-only machine type with minReplicaCount=1 and maxReplicaCount=5, and enable autoscaling based on CPU utilization.. For a small CPU-bound model with variable traffic, the optimal cost-latency trade-off is a CPU-only machine type with a low minimum replica count and autoscaling enabled. This keeps baseline cost low while allowing the endpoint to add replicas when CPU utilization increases, preserving the 100 ms latency target. GPU instances and fixed high replica counts unnecessarily increase cost without improving latency.

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Use a CPU-only machine type with minReplicaCount=5 and maxReplicaCount=5 to ensure high availability.

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Use a CPU-only machine type with minReplicaCount=1 and maxReplicaCount=1.

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Use a GPU-enabled machine type with minReplicaCount=1 and maxReplicaCount=1.

Question 9

Your team trains a model on a Vertex AI Workbench notebook and logs hyperparameters, metrics, and a confusion matrix. Your manager asks you to ensure that anyone in the organization can reproduce the exact training run and compare it with other runs without manually digging through notebook cells. Which Vertex AI component should you use to record this information?

Accepted Answer

Vertex AI Experiments. A system of record for training runs must capture parameters, metrics, and artifacts in a searchable way so teammates can reproduce and compare results. Vertex AI Experiments provides exactly this by linking each run to metadata and lineage, while visualization, deployment, and orchestration tools address different stages of the lifecycle.

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Vertex AI Pipelines

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Vertex AI Model Registry

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Vertex AI TensorBoard

Question 10

You are deploying a large language model on a Vertex AI endpoint. The model is loaded from a Cloud Storage bucket at container startup, which adds 3 minutes to each cold start. You want to reduce cold-start time and ensure predictable latency during scale-out. Which approach should you take?

Accepted Answer

Store the model artifacts in a custom container image and push it to Artifact Registry.. Embedding the model artifacts in a custom container image eliminates the separate download from Cloud Storage during container startup. The image layers are pulled by the node as part of standard container initialization, which is typically faster and more predictable than fetching a large model from a GCS bucket after the container starts.

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Use a larger machine type with more vCPUs and memory for each replica.

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Set the endpoint's minReplicaCount to a high value so that replicas are always warm.

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Enable request-response logging on the endpoint to monitor startup latency.

Question 11

A team is training a large recommendation model on Vertex AI using a custom container. They need to log training metrics and visualize them in Vertex AI TensorBoard. The training code is written in PyTorch and runs on multiple worker nodes. Which of the following is the correct way to enable TensorBoard logging?

Accepted Answer

Use the Vertex AI SDK to create a TensorBoard instance and pass its resource name to the training job using the --tensorboard flag.. To integrate with Vertex AI TensorBoard, you must create a TensorBoard instance and pass its resource name to the training job. The training code should write logs to the directory specified by the AIP_TENSORBOARD_LOG_DIR environment variable. This allows Vertex AI to automatically collect and display metrics from all workers in the managed TensorBoard service.

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Install TensorBoard in the custom container and run it as a sidecar process on each worker node.

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Write training metrics to a Cloud Storage bucket and then manually upload them to Vertex AI TensorBoard after training.

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Use the torch.utils.tensorboard.SummaryWriter to write logs to a local directory, and Vertex AI will automatically sync it to TensorBoard.

Question 12

You are collaborating on a Vertex AI Feature Store implementation. A data engineer updates a feature's values in the offline store, but the online store still serves the old values for several hours. The online store is configured with a feature value TTL of 24 hours and uses batch ingestion. What is the most likely cause of the stale online values?

Accepted Answer

The feature values are ingested into the offline store only, and the online store is not being updated because batch ingestion does not automatically sync to the online store unless a separate ingestion job is run.. Vertex AI Feature Store maintains separate offline and online stores. Batch ingestion updates the offline store, but the online store requires its own ingestion job to sync values. Without running that job, the online store continues to serve the previous values. The TTL affects validity, not propagation, so the missing sync job is the most likely cause.

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The online store is configured to read from the offline store at query time, and the delay is due to eventual consistency between the two stores.

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The online store's feature value TTL is set too high, causing it to serve cached values until the TTL expires.

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The feature's online store TTL has expired, causing the online store to fall back to the offline store for the latest values.

Question 13

A company wants to build a recommendation system that suggests products to users based on their past interactions. They have user-item interaction data in BigQuery and want a low-code solution that can generate recommendations for all users. Which approach should they use?

Accepted Answer

Use BigQuery ML to train a matrix factorization model and use ML.RECOMMEND to generate recommendations.. BigQuery ML's matrix factorization model is purpose-built for recommendations and can be trained with SQL on user-item interaction data. The ML.RECOMMEND function generates recommendations for all users in a scalable way. This low-code approach avoids custom coding and is optimized for the task, making it the best fit for the company's needs.

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Use BigQuery ML to train a k-means clustering model and assign users to clusters for recommendations.

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Use Dataflow to preprocess data and train a custom recommendation model on Vertex AI.

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Use Vertex AI AutoML Tables to train a model that predicts a rating for each user-item pair.

Question 14

A media company wants to automatically transcribe and analyze customer support calls to identify common issues. They need a low-code solution that provides both transcription and sentiment analysis. Which Google Cloud service should they use?

Accepted Answer

Contact Center AI Insights. Contact Center AI Insights is a purpose-built, low-code solution for analyzing customer calls. It automatically transcribes audio, performs sentiment analysis, and extracts topics, providing actionable insights without custom development. Other options either lack sentiment analysis or require integration with multiple services, increasing complexity and coding effort. Thus, Contact Center AI Insights is the correct choice.

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Speech-to-Text API

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Dialogflow CX

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Natural Language API

Question 15

A company runs a Vertex AI Pipeline that includes a hyperparameter tuning step followed by a training step. The tuning step outputs the best hyperparameters. The engineer wants the training step to use these hyperparameters and to ensure that the training step only runs if tuning succeeds. Which approach should the engineer take?

Accepted Answer

Pass the output of the tuning step as an input parameter to the training step and set the `after` parameter of the training step to include the tuning step.. To pass data between tasks in Vertex AI Pipelines, the output of one task must be provided as an input to another. The tuning step's output (best hyperparameters) is passed as an input to the training step, creating a data dependency. Additionally, the `after` parameter ensures the training step runs only after the tuning step succeeds. This dual approach guarantees both correct data flow and execution order, and if tuning fails, the pipeline stops before training.

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Use a conditional expression in the training step to check if the tuning step succeeded, and only then proceed.

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Configure the tuning step to write hyperparameters to a Cloud Storage bucket, and have the training step read from that bucket at runtime.

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Run the tuning and training steps in parallel to save time, and use a shared memory store to exchange hyperparameters.

Question 16

You are training a TensorFlow model on Vertex AI using a custom container. The training job uses a single node with 4 GPUs and a global batch size of 1024. You notice that the training is slower than expected and GPU utilization is low. You suspect the input pipeline is the bottleneck. Which of the following should you do to improve training throughput?

Accepted Answer

Use tf.data to prefetch data and parallelize data extraction and transformation with num_parallel_calls and prefetch.. Low GPU utilization in multi-GPU training often indicates the input pipeline cannot supply data fast enough. Using tf.data with prefetch and parallel calls overlaps data preprocessing with model training, keeping the GPUs busy. This is a standard optimization for TensorFlow input pipelines and directly targets the bottleneck without changing hardware or batch size.

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Reduce the global batch size to 512 to decrease memory pressure on the GPUs.

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Increase the number of GPUs to 8 and double the global batch size to 2048.

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Switch to using TPUs instead of GPUs for the training job.

Question 17

You are serving a model on a Vertex AI endpoint that requires a GPU. The model is used for interactive predictions with a strict latency SLO. You notice that during peak hours, some requests time out because the endpoint's autoscaler is slow to add GPU replicas. Which action should you take to meet the SLO?

Accepted Answer

Set a higher minimum replica count on the endpoint to keep more GPU replicas warm during peak hours.. GPU replicas take longer to provision and initialize than CPU replicas, so autoscaling may not react quickly enough during sudden peaks. Setting a higher minimum replica count keeps additional GPU replicas warm and ready before demand spikes. This pre-provisioned capacity absorbs peak traffic immediately, preventing request queuing and timeouts. It addresses the slow autoscaler response directly, ensuring the endpoint can meet its strict latency SLO during peak hours.

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Enable request batching on the endpoint to process multiple requests per replica.

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Reduce the model's input size by truncating features to lower per-request compute.

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Switch to a CPU-based machine type to avoid GPU provisioning delays.

Question 18

You are preparing to scale a prototype ML model to production on Vertex AI. The model is trained with a custom training job, and you want to ensure that the training is reproducible and that you can compare different runs. Which two practices should you follow? (Choose two.)

Accepted Answer

Use Vertex AI Experiments to log parameters, metrics, and artifacts for each training run.. To make training reproducible and comparable, you should control randomness by setting and recording a fixed seed, and you should use Vertex AI Experiments to log parameters, metrics, and artifacts for each run. These two practices together let you reproduce a run and compare it with others. The other options either harm reproducibility by not pinning the environment or do not directly address run comparison.

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Always use the latest pre-built training container without pinning the version so that you get the newest features.

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Store the trained model artifacts in a versioned location such as a Cloud Storage bucket with object versioning or a Vertex AI Model Registry entry.

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Train on the full dataset without any sampling to ensure that all runs see the same data.

Question 19

Your organization uses Vertex AI Model Registry to manage models. A data scientist has trained a new model version and wants to ensure that only approved models are deployed to production. You need to implement a workflow where a model must be reviewed and approved by a designated approver before it can be deployed. What should you do?

Accepted Answer

Implement a CI/CD pipeline using Cloud Build that, upon a model version being tagged as 'approved', deploys it to an endpoint, and restrict the tagging permission to the approver.. Enforcing an approval workflow requires a combination of access control and automation. By using a CI/CD pipeline that reacts to an 'approved' tag, and limiting who can apply that tag, you create a secure gate. This ensures that only reviewed models are deployed and provides an audit trail. Native Model Registry lacks built-in approval, so custom logic is necessary.

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Use Vertex AI Model Registry's built-in approval workflow by assigning the approver role to a user and setting the model version's state to 'Pending Approval'.

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Create a Cloud Function that listens for new model versions, triggers a manual review via email, and upon approval, updates the model version's labels to indicate approval.

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Restrict deployment permissions using IAM so that only the approver has the Vertex AI User role, and require the approver to manually deploy the model after review.

Question 20

A media company is serving a video recommendation model on a Vertex AI Endpoint. The model receives a mix of requests: some require only a few features, while others require many features from a feature store. The team wants to reduce average latency and cost without retraining the model. Which TWO strategies should they use? (Choose two.)

Accepted Answer

Enable request batching on the endpoint to group multiple requests into a single prediction call.. Request batching and feature caching are serving-side optimizations that reduce per-request overhead and repeated feature lookups. They lower average latency and can reduce cost by improving resource utilization, all without retraining the model. Other options either do not address the root causes or are unsuitable for real-time serving.

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Switch from online predictions to batch predictions for all requests.

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Use a smaller machine type for the endpoint to reduce cost per replica.

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Increase the endpoint's maxReplicaCount to handle peak traffic.