PDE Practice Test 29 — 15 Questions

Question 1

A company needs to stream data from a fleet of IoT devices to BigQuery for near-real-time analytics. The data volume is unpredictable and can spike during certain events. Which Google Cloud service should be used as the ingestion point to handle variable throughput with minimal operational overhead?

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

Accepted Answer

Cloud Pub/Sub. Cloud Pub/Sub is the correct choice because it is a fully managed, scalable messaging service designed to decouple data producers from consumers, handling unpredictable and spiky throughput without requiring manual scaling. It can ingest millions of messages per second and buffer them until BigQuery is ready to consume, ensuring near-real-time analytics with minimal operational overhead.

Answer

Cloud Datastore

Answer

Cloud Functions

Answer

Cloud Storage

Question 2

A company stores IoT sensor data in BigQuery. Queries that filter on a timestamp column and a device_id column are slow even though the table is partitioned by day. What should the data engineer do to improve query performance?

Accepted Answer

Cluster the table on device_id. Clustering on device_id organizes the data within each day partition by device_id, allowing BigQuery to prune blocks during queries that filter on that column. This reduces the amount of data scanned and improves query performance without changing the partitioning scheme. Partitioning alone only limits scans by time range; clustering adds intra-partition sorting for non-time-based filters.

Answer

Increase the partition size to monthly

Answer

Switch to ingestion-time partitioning instead of column-based

Answer

Enable automatic query rewriting with BI Engine

Question 3

The push endpoint is returning 500 errors. What is the most likely cause?

Accepted Answer

The push endpoint requires authentication but none is set. The push endpoint likely requires authentication, but none is configured, causing the 500 errors.

Answer

The topic has no messages

Answer

The push endpoint is not a valid HTTPS URL

Answer

The ack deadline is too short

Question 4

A team trained a model on a Vertex AI custom training job and wants to deploy it to an endpoint for online predictions. They have the model artifacts stored in Cloud Storage. What steps are required?

Accepted Answer

Upload model to Model Registry, create endpoint, deploy model. To deploy a model for online predictions on Vertex AI, you must first upload the model artifacts from Cloud Storage to the Model Registry, which creates a versioned model resource. Then you create an endpoint (or use an existing one) and deploy the model to that endpoint, specifying machine type, traffic split, and other settings. This three-step process (upload → create endpoint → deploy) is the required workflow for online serving.

Answer

Directly deploy from Cloud Storage without Model Registry

Answer

Create endpoint, then upload model

Answer

Use Vertex AI Batch Prediction only

Question 5

Refer to the exhibit. What is the most likely cause of the error?

Accepted Answer

The model artifact was not uploaded to Cloud Storage. The error occurs because the model artifact must be uploaded to Cloud Storage before it can be deployed to an endpoint. Vertex AI requires the model to be stored in a Cloud Storage bucket, and the deployment process references that artifact. Without the artifact in Cloud Storage, the endpoint creation or model deployment fails with an error indicating the resource is missing.

Answer

The endpoint does not exist

Answer

The service account lacks permissions

Answer

The model ID is invalid

Question 6

Your company runs batch predictions using Vertex AI Batch Prediction on a monthly basis. The predictions are used to generate customer segments for marketing campaigns. This month, the batch prediction job failed with an error: 'The number of rows in the input table does not match the number of rows in the output table.' The input table in BigQuery has 5 million rows, but the output table has only 4.5 million rows. You need to identify and handle the missing predictions. What is the most efficient course of action?

Accepted Answer

Enable the 'write_prediction_errors' flag in the batch prediction configuration to capture failed predictions in a separate table.

Answer

Manually inspect the input table to find which rows are missing and rerun the batch prediction for those rows.

Answer

Run the batch prediction job with the 'generate_explanation' parameter enabled to get additional output for debugging.

Answer

Use a Cloud Dataflow pipeline to process the input data and call the model for each row, handling errors programmatically.

Question 7

After deploying a model to Vertex AI Endpoints, the prediction responses include unexpected data. The model returns logits instead of probabilities. What is the most likely cause?

Accepted Answer

The model output is not post-processed. The most likely cause is that the model output is not post-processed. In Vertex AI Endpoints, models often output raw logits (unnormalized scores) from the final layer, and a softmax or sigmoid activation must be applied as a post-processing step to convert these logits into probabilities. Without this post-processing, the endpoint returns the raw logits, which is why the prediction responses contain unexpected data.

Answer

The model was trained with different loss

Answer

The input data is scaled incorrectly

Answer

The endpoint is not properly configured

Question 8

Refer to the exhibit. A developer sees this log entry when trying to get a prediction. What is the most likely cause?

Accepted Answer

The model version is not deployed. The log entry indicates that the model version specified in the request is not currently deployed to the serving infrastructure. In Google Cloud's Vertex AI, a model version must be explicitly deployed to an endpoint before it can serve predictions; attempting to predict against a non-deployed version returns an error. This is the most likely cause because the error message directly references the model version's deployment status.

Answer

The model ID is incorrect

Answer

The endpoint does not exist

Answer

The project ID is wrong

Question 9

After deploying a model, the team notices that predictions are significantly different from training data distribution. What should they do?

Accepted Answer

Set up Vertex AI Model Monitoring for skew detection. Vertex AI Model Monitoring is specifically designed to detect skew between training data and serving data, including prediction drift. When predictions differ significantly from the training distribution, this indicates a skew or drift issue that Model Monitoring can alert on, enabling proactive investigation. Updating the endpoint or retraining without diagnosis would not address the root cause, and reviewing the pipeline alone does not provide ongoing detection.

Answer

Update the model endpoint

Answer

Review the training data pipeline

Answer

Retrain the model with new data

Question 10

A company deploys a scikit-learn model on Vertex AI for online predictions. The model is packaged in a custom container with all dependencies. Users report high latency (over 5 seconds) for predictions. The model size is 2 GB. What is the most likely cause of the high latency?

Accepted Answer

Using a custom container with a large unoptimized model. A 2 GB model loaded into a custom container without optimization (e.g., quantization, pruning, or ONNX conversion) will cause significant cold-start latency and per-request loading overhead. Vertex AI online predictions require the model to be loaded into memory for each request or container instance; a large, unoptimized model increases both loading time and inference time, easily exceeding 5 seconds.

Answer

Using online predictions instead of batch prediction

Answer

Not enabling GPU acceleration

Answer

Using a small machine type (e.g., n1-standard-2)

Question 11

Refer to the exhibit. A Cloud Build step fails when pushing a Docker image to Artifact Registry. What is the missing IAM role for the Cloud Build service account?

Accepted Answer

roles/artifactregistry.writer. The Cloud Build service account needs the `roles/artifactregistry.writer` role to push Docker images to Artifact Registry. This role grants the necessary permissions to upload artifacts, including images, to the registry. Without it, the build step fails with an authorization error.

Answer

roles/containerregistry.admin

Answer

roles/storage.objectCreator

Answer

roles/cloudbuild.builds.editor

Question 12

A retail company uses a machine learning model to predict inventory demand. The model is retrained weekly using Vertex AI Pipelines. Recently, the model's accuracy has degraded because the data distribution has shifted. Which action should you take to monitor and detect this drift automatically?

Accepted Answer

Enable Vertex AI Model Monitoring for the endpoint and configure alerting on feature drift. Vertex AI Model Monitoring is purpose-built to automatically detect feature drift and prediction drift on deployed endpoints. By enabling it and configuring alerting on feature drift, you can proactively identify when the distribution of incoming features deviates from the training data, which directly addresses the root cause of accuracy degradation without manual intervention.

Answer

Set up alerts for when the model's mean absolute error exceeds a threshold on the evaluation dataset

Answer

Enable Cloud Logging for the prediction endpoint and search for error logs

Answer

Schedule a job to compare the distribution of incoming features with the training data using Cloud Dataflow

Question 13

A startup is deploying a machine learning model for real-time fraud detection. They need low latency and automatic scaling during peak hours. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Endpoints. Vertex AI Endpoints provide managed, autoscaling infrastructure designed for low-latency online predictions, making them ideal for real-time fraud detection. They automatically scale the number of compute nodes based on incoming traffic, ensuring peak-hour demand is met without manual intervention.

Answer

Cloud Functions

Answer

Batch Prediction on Vertex AI

Answer

Cloud AI Platform Prediction with custom containers

Question 14

Your organization uses Vertex AI Feature Store to serve features for a real-time fraud detection model. The model is deployed on a Vertex AI endpoint. After a data pipeline update, the model's online predictions became inconsistent. What is the most likely cause?

Accepted Answer

The feature store's online serving values are not synchronized with the batch feature values used during training.. In Vertex AI Feature Store, batch feature values used during model training and online serving values are stored separately. If a data pipeline update changes the batch feature values but the online serving values are not updated or synchronized, the model will receive different feature values at inference time than it was trained on, leading to inconsistent predictions. This is the most common cause of prediction drift after a pipeline change.

Answer

The model's prediction server is running out of memory.

Answer

The model was retrained with a different training dataset.

Answer

The online serving endpoint's model version was accidentally rolled back.

Question 15

A team is using Kubeflow Pipelines on Google Kubernetes Engine to orchestrate ML workflows. They need to track parameters, metrics, and artifacts for each run. Which tool should they integrate?

Accepted Answer

Vertex ML Metadata. Vertex ML Metadata is the correct choice because it is purpose-built for tracking parameters, metrics, and artifacts in ML workflows, and it integrates natively with Kubeflow Pipelines on Google Kubernetes Engine. It stores metadata for each pipeline run, enabling lineage tracking, comparison, and reproducibility of experiments.

Answer

Cloud Monitoring

Answer

Cloud Logging

Answer

BigQuery