PMLE Practice Test 5 — 25 Questions

Question 1

A retail company wants to build a product recommendation system using BigQuery ML for their e-commerce platform. The data includes customer purchase history, product metadata, and clickstream logs. The ML engineer needs to minimize manual feature engineering and leverage pre-built solutions. Which approach should the engineer take?

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

Accepted Answer

Use BigQuery ML's matrix factorization model (CREATE MODEL with model_type='matrix_factorization') to train directly on historical interaction data.. BigQuery ML's matrix factorization model (model_type='matrix_factorization') is purpose-built for recommendation systems using implicit or explicit feedback data. It trains directly on historical interaction data (e.g., user-item purchases) without requiring manual feature engineering, aligning with the goal of minimizing low-code ML effort. This approach leverages BigQuery's native SQL interface and scales automatically, making it ideal for the described e-commerce scenario.

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Use a pre-built recommendation model from Vertex AI Model Garden and deploy it to an endpoint.

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Write a custom TensorFlow model using the Vertex AI Training service and deploy it via Vertex AI Prediction.

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Export the data to CSV and use AutoML Tables to train a recommendation model.

Question 2

You are using TensorFlow Transform (tf.Transform) to preprocess data for a model that will be deployed on Vertex AI. What is the primary benefit of using tf.Transform over Dataflow alone?

Accepted Answer

Training/serving skew prevention through a consistent transformation graph. tf.Transform computes statistics (e.g., min, max) on the full dataset, then generates a TensorFlow graph that applies the same transformation consistently at training and serving time. Dataflow alone does not ensure this consistency.

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Support for GPUs during preprocessing

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Built-in feature store integration

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Faster data processing

Question 3

An MLOps team is implementing a CI/CD pipeline for a TensorFlow model on Vertex AI. The model training job takes 2 hours and produces a SavedModel. The team wants to automatically trigger a new pipeline run whenever a change is pushed to the 'main' branch of their source repository. The pipeline should include training, evaluation, and if metrics exceed a threshold, deploy the model to a Vertex AI endpoint. Which trigger configuration should they use?

Accepted Answer

Configure a Cloud Build trigger that watches the 'main' branch of Cloud Source Repositories; in the build config, use steps to run the pipeline via the Vertex AI API.. Cloud Build triggers can be configured to watch a specific branch (e.g., 'main') in Cloud Source Repositories and automatically execute a build configuration. Within that build config, you can use the `gcloud` or `curl` steps to invoke the Vertex AI Pipeline API, which starts the training, evaluation, and conditional deployment workflow. This directly matches the requirement for a branch-based push trigger that orchestrates the full ML pipeline.

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Use Eventarc to listen for Cloud Source Repository push events and invoke a Cloud Run service that starts the pipeline.

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Use an Artifact Registry trigger to detect new model images and then start the pipeline.

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Set up a Cloud Scheduler job that runs every 2 hours and triggers a Vertex AI Pipeline run.

Question 4

A financial services company deploys a fraud detection model on Vertex AI. The model must make predictions in under 100ms. After deployment, latency spikes to 300ms during peak hours. The model is a large ensemble with 500MB size. Which action is most likely to reduce latency?

Accepted Answer

Optimize the model using TensorFlow Lite and convert to a smaller format.. The primary cause of latency is the large model size (500MB) combined with real-time inference constraints. Optimizing the model with TensorFlow Lite reduces the model size and computational overhead, directly decreasing inference time. This addresses the root cause—model complexity—rather than scaling infrastructure around an inefficient model.

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Switch to batch prediction to process requests asynchronously.

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Reduce the machine type to a smaller instance.

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Increase the number of replicas on the endpoint.

Question 5

A machine learning team wants to implement a continuous delivery pipeline for their ML models using Vertex AI Pipelines. The pipeline should automatically deploy a model to a staging endpoint after evaluation passes, and then after manual approval, promote it to production. Which strategy should they use to manage model versions in the Vertex AI Model Registry?

Accepted Answer

Use Vertex AI Model Registry aliases: assign the model version an alias 'staging' initially, and after approval, change the alias to 'production' via the API.. Vertex AI Model Registry supports versioning and alias management. By uploading models with an alias like 'staging' and later changing the alias to 'production', the team can control promotion. The other options either lack the required functionality or add unnecessary complexity.

Answer

Store model artifacts in Cloud Storage with versioned paths and deploy directly from Storage without using the Model Registry.

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Create two separate models in the registry: one for staging and one for production, and copy artifacts between them after approval.

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Upload each model version with the same model ID and use labels to differentiate staging vs production.

Question 6

A data science team is using Vertex AI Feature Store for online serving. They notice that the online serving latency is high. What is the most likely cause?

Accepted Answer

The feature values are stored in Cloud Storage.. Vertex AI Feature Store requires feature values to be stored in a low-latency online store (such as a Bigtable or Redis cluster) for serving. When features are stored in Cloud Storage, each online serving request must read from object storage, which introduces significant latency due to network overhead and lack of indexing. This design violates the fundamental architecture of Feature Store, which expects precomputed features in a key-value store optimized for sub-millisecond lookups.

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The features are being computed on the fly instead of being precomputed.

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The feature table has too many rows.

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The online store is not configured for high throughput.

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The serving endpoint is in a different region than the client.

Question 7

A team monitors features in Vertex AI Feature Store for drift. They want to set up automated alerts when a feature's distribution deviates significantly from the baseline. Which feature monitoring configuration should they use?

Accepted Answer

Enable feature monitoring on the feature group with drift threshold and notification channel.. Feature monitoring in Vertex AI Feature Store allows defining drift thresholds and alerting via Cloud Monitoring.

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Use Cloud Monitoring custom metrics and log-based alerts manually.

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Use Vertex AI Experiments to compare distributions.

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Export features to BigQuery and set up scheduled queries with alerts.

Question 8

A company needs to detect objects in real-time from a live video feed. They want to use a pre-trained model with minimal setup. Which Google Cloud service should they use?

Accepted Answer

Video Intelligence API. The Video Intelligence API is the correct choice because it provides pre-trained models for real-time video analysis, including object detection, with minimal setup. It is designed to extract metadata from video streams without requiring custom training, making it ideal for real-time live video feeds. Vertex AI AutoML Video, in contrast, requires custom training and is not pre-trained for immediate use.

Answer

Vertex AI AutoML Video

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Document AI

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Cloud Vision API

Question 9

You are deploying a model on Vertex AI and need to ensure high availability and low latency. Which THREE configurations should you implement?

Accepted Answer

Choose a machine type with GPUs for compute-intensive models.. GPUs are essential for compute-intensive models, such as deep neural networks, as they provide parallel processing capabilities that significantly reduce inference latency compared to CPUs. On Vertex AI, selecting a machine type with GPUs (e.g., n1-standard-4 with NVIDIA T4) ensures that the model can handle high-throughput requests with low latency, which is critical for real-time serving.

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Use a custom endpoint with a static IP address.

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Deploy to a single zone to reduce network latency.

Question 10

A machine learning engineer wants to define a lightweight pipeline component that runs custom Python code without building a container image. Which KFP SDK feature should they use?

Accepted Answer

Python function component with @dsl.component. The `@dsl.component` decorator in KFP SDK allows you to define a lightweight Python function component that runs custom code without requiring a container image. It automatically generates a container specification from the function's dependencies, making it ideal for simple, non-containerized pipeline steps.

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Importer component

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Container component

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Vertex AI Training job

Question 11

A machine learning engineer notices that the online prediction latency for a custom TensorFlow model deployed on Vertex AI has increased significantly over the past week. Cloud Monitoring shows that the CPU utilization of the endpoints remains below 40%, but the number of concurrent requests has doubled. What is the most likely cause of the latency increase?

Accepted Answer

Insufficient number of replicas for autoscaling. The CPU utilization remains below 40% while concurrent requests have doubled, indicating that the existing replicas are not saturated on CPU but are bottlenecked by request queuing or thread contention. Vertex AI autoscaling scales based on CPU utilization by default; if the threshold is not crossed, new replicas are not provisioned, causing requests to queue and latency to spike. The engineer should verify the autoscaling configuration and consider scaling on request count or reducing the CPU utilization target.

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Data skew causing longer inference time

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Memory leak in the serving container

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Model overfitting

Question 12

An ML team uses Delta Lake on Dataproc for data versioning. Which THREE benefits does Delta Lake provide?

Accepted Answer

Time travel for accessing previous versions. Delta Lake provides ACID transactions, schema enforcement, and time travel.

Answer

Automatic data encryption at rest

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Built-in real-time streaming

Question 13

You are the ML engineer for a financial services company. You have deployed a fraud detection model on Vertex AI Endpoints using a custom container. The model is a gradient boosting model trained on transactional data. Over the past week, the model's precision has dropped from 95% to 80%, while recall has remained stable. The input data volume and distribution have not changed significantly. The model is served on a single endpoint with autoscaling enabled (min replicas=2, max replicas=10). You notice that the average CPU utilization of the serving containers has increased from 40% to 90%, and the p99 latency has increased from 50ms to 200ms. The model is retrained weekly using the latest data, and the last retraining was 3 days ago. The logs show no errors, and the model version is unchanged. Given these symptoms, what is the most likely cause of the precision drop?

Accepted Answer

A recent change in the preprocessing code in the container transformed features differently than what the model expects, causing incorrect predictions.. The precision drop without a change in input distribution or recall strongly indicates a systematic error in predictions, not a data shift. A preprocessing code change in the custom container would cause the model to receive features transformed differently than during training, leading to incorrect probability estimates. The increased CPU utilization and latency are consistent with the container performing additional or different preprocessing steps, not with autoscaling issues or model version changes.

Answer

The autoscaling policy is not scaling up fast enough, causing increased latency and prediction errors.

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The model is overfitting to recent transaction patterns due to weekly retraining.

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The model was replaced with a different version without updating the endpoint.

Question 14

A company uses BigQuery to store feature data for ML training. A data engineer notices that a Vertex AI Training job is failing with 'Access Denied' errors when reading from a BigQuery table. The training job uses a custom service account that has been granted the 'bigquery.dataViewer' role on the dataset. What is the most likely cause of the failure?

Accepted Answer

The service account lacks the 'bigquery.jobs.create' permission in the project.. The 'bigquery.dataViewer' role grants permissions to read BigQuery data (e.g., bigquery.tables.getData), but it does not include the 'bigquery.jobs.create' permission. When a Vertex AI training job reads from BigQuery, it must first create a BigQuery job (a query job) to retrieve the data. Without 'bigquery.jobs.create' at the project level, the service account cannot initiate the read operation, resulting in an 'Access Denied' error even though it has data-level access.

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The service account is not in the same project as the BigQuery dataset.

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The BigQuery table is partitioned and requires row-level access.

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The training job does not have the required network access to BigQuery.

Question 15

You have a TensorFlow training script that runs on a single machine. To speed up training on Vertex AI with 8 GPUs on a single machine, which strategy should you use?

Accepted Answer

tf.distribute.MirroredStrategy. MirroredStrategy performs synchronous data parallelism across multiple GPUs on a single machine. MultiWorkerMirroredStrategy is for multiple machines, not needed here. ParameterServerStrategy is for distributed asynchronous training. TPUStrategy is for TPUs.

Answer

tf.distribute.ParameterServerStrategy

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tf.distribute.TPUStrategy

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tf.distribute.MultiWorkerMirroredStrategy

Question 16

Your company deploys batch prediction jobs using Vertex AI Batch Prediction. You need to monitor the jobs for failures and performance. What is the recommended approach?

Accepted Answer

Use Cloud Monitoring to create custom dashboards and alerts based on Vertex AI batch prediction metrics.. Cloud Monitoring (formerly Stackdriver) is the native Google Cloud service for collecting, visualizing, and alerting on metrics from Vertex AI, including batch prediction job success rates, latency, and resource utilization. It provides pre-built dashboards and the ability to create custom alerts, making it the recommended approach for monitoring failures and performance in a centralized, scalable way.

Answer

Use Cloud Logging to export batch prediction logs and create log-based metrics.

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Set up email alerts in the Vertex AI console for failed jobs.

Answer

Enable the Recommender to get optimization suggestions for batch jobs.

Question 17

In a Vertex AI Pipeline, a component produces a Metrics artifact that includes an evaluation metric. The engineer wants to use this metric value as a condition to decide whether to deploy the model. However, the metric value is stored in the artifact's metadata and not directly as a pipeline parameter. How can the engineer pass the metric value to a downstream conditional task?

Accepted Answer

Add a component that reads the artifact's metadata and outputs the metric as a parameter, then use that parameter in the condition.. Vertex AI Pipeline conditions require pipeline parameters (typed values) to evaluate expressions like `dsl.If`. A Metrics artifact's metadata is stored as an artifact property, not a pipeline parameter, so a custom component must read that metadata and output the metric as a parameter. This parameter can then be used in the `dsl.If` condition to control downstream deployment.

Answer

Configure the component that produces the Metrics artifact to also output the metric as a pipeline parameter.

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Use the importer component to convert the artifact into a parameter.

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Use the artifact directly in the dsl.If condition, as artifacts are comparable.

Question 18

An engineer is configuring Vertex AI Model Monitoring for a model deployed on an endpoint. They want to monitor feature skew using the training dataset as a baseline. The training dataset is large (10 TB). What is the most efficient way to provide the baseline distribution?

Accepted Answer

Use the BigQuery table containing the training data as the baseline source in the monitoring configuration. Vertex AI Model Monitoring can automatically compute the baseline distribution from the training dataset stored in BigQuery. This avoids manual computation or exporting large datasets.

Answer

Compute the baseline distribution offline and upload a JSON file with feature statistics

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Upload the entire training dataset as a CSV file to Cloud Storage and reference it in the monitoring config

Answer

Sample 1% of the training data and use that as baseline

Question 19

A data science team uses Vertex AI Workbench and wants to share notebooks with version history. Which service should they use?

Accepted Answer

Cloud Source Repositories. Cloud Source Repositories (CSR) is the correct choice because it provides Git-based version control for notebooks, enabling teams to track changes, collaborate, and maintain a full version history. Vertex AI Workbench integrates natively with CSR, allowing users to clone, commit, and push notebook files directly from the JupyterLab interface, which is essential for collaborative development with revision tracking.

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Artifact Registry

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Cloud Storage

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Data Catalog

Question 20

A financial services firm deploys a binary classification model for fraud detection. The model's precision is 0.95 and recall is 0.60 on the test set. After deployment, the fraud rate in production is 0.5% compared to 5% in the test set. The model shows good calibration on the test set (Brier score 0.02) but poor calibration in production (Brier score 0.15). What is the most likely explanation for the calibration degradation?

Accepted Answer

The relationship between features and the target has changed (concept drift), causing the model's probability estimates to be misaligned with the true probabilities.. The model's calibration degrades in production despite being well-calibrated on the test set, which had a 5% fraud rate, while production has a 0.5% fraud rate. This shift in class imbalance (prior probability shift) directly affects the model's probability estimates because the model's predicted probabilities are conditional on the training distribution. Option D is correct because concept drift—specifically a change in the base rate of fraud—causes the model's probability estimates to no longer reflect the true posterior probabilities in production, leading to a higher Brier score.

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The distribution of input features has shifted significantly, causing the model to produce incorrect probabilities.

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The model overfits to noise in the training data, leading to poor generalization.

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The production data has a different class imbalance than the training data, causing the model to be biased toward the majority class.