PDE Practice Test 29 — 15 Questions

Question 1

A startup is building a mobile app that needs to sync user data across devices in real time. They expect millions of concurrent users and need a NoSQL database with offline support and automatic multi-region replication. Which Google Cloud service meets these requirements?

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

Accepted Answer

Firestore. Firestore is a NoSQL, serverless document database that provides real-time synchronization, offline support via local persistence, and automatic multi-region replication. It is designed for mobile and web apps with millions of concurrent users, making it the ideal choice for this use case.

Answer

Cloud Bigtable

Answer

Cloud Spanner

Answer

Cloud SQL

Question 2

You need to schedule a Dataproc Spark job to run at 2 AM every day, and upon completion, trigger a BigQuery load job. Which Cloud Composer operator should you use to run the Spark job?

Accepted Answer

DataprocSubmitJobOperator. The DataprocSubmitJobOperator is specifically designed to submit a job (e.g., a Spark job) to an existing Dataproc cluster. In this scenario, you need to run a Spark job on a scheduled basis, and Cloud Composer (Airflow) provides this operator to submit the job to Dataproc. After the Spark job completes, you can chain a BigQuery load operator to trigger the load, matching the requirement exactly.

Answer

DataflowPythonOperator

Answer

BigQueryOperator

Answer

DataprocClusterCreateOperator

Question 3

A data scientist wants to use Vertex AI Workbench for exploratory data analysis. Which TWO statements are true about Vertex AI Workbench?

Accepted Answer

It supports custom container images for the notebook environment.. Option B is correct because Vertex AI Workbench instances let you specify a custom container image for the notebook environment, so you can bring your own dependencies and tooling rather than being limited to the default image. Option D is correct because Vertex AI Workbench provides a managed JupyterLab environment with pre-installed ML libraries (such as TensorFlow, PyTorch, and scikit-learn), which is exactly what a data scientist needs for exploratory data analysis. Option A is incorrect because Workbench instances are user-managed VMs that do not scale to zero; they keep running (and incurring cost) until stopped. Option C is incorrect because Workbench supports multiple frameworks and languages, not only TensorFlow. Option E is incorrect because a built-in SQL query editor for BigQuery is a feature of BigQuery and other tools, not a defining capability of Vertex AI Workbench.

Answer

It is a serverless service that scales to zero when not in use.

Answer

It can only be used with TensorFlow.

Answer

It includes a built-in SQL query editor for BigQuery.

Question 4

A data team is building a near-real-time dashboard that displays aggregated metrics from Kafka topics. They want to use Pub/Sub as a managed messaging service and Dataflow for stream processing. They need to ingest data from Kafka into Pub/Sub with minimal custom code. Which THREE Google Cloud services should they use together? (Choose three.)

Accepted Answer

Dataflow. Option A, Dataflow, is correct because it is Google Cloud's managed Apache Beam service for stream processing, and it can run a streaming pipeline that reads from Pub/Sub, performs windowed aggregations, and writes results for the near-real-time dashboard. Option B, Pub/Sub, is correct because it serves as the managed messaging service that decouples the Kafka ingestion layer from the Dataflow processing layer, buffering messages and enabling reliable, scalable delivery. Option C, Kafka Connect (with Pub/Sub connector), is correct because Kafka Connect provides a configuration-driven, low-code way to move data from Kafka topics into Pub/Sub using a Pub/Sub sink connector, satisfying the requirement for minimal custom code. Option D, Cloud NAT, is not relevant because it provides outbound internet address translation for private VMs and does not ingest Kafka data into Pub/Sub. Option E, Cloud Functions, is not appropriate here because it is an event-driven serverless compute service for lightweight functions, not the managed stream-processing engine needed for aggregated Kafka metrics.

Answer

Cloud NAT

Answer

Cloud Functions

Question 5

An organization is implementing a data lake on Google Cloud using Cloud Storage. They need to process both batch and streaming data with a unified pipeline. The team has experience with Apache Beam. Which architecture should they use to minimize operational overhead?

Accepted Answer

Kappa architecture with Cloud Dataflow using the same pipeline for batch and streaming. Kappa architecture uses a single streaming pipeline for both batch and streaming, simplifying operations. Dataflow implements Beam and supports both modes.

Answer

Use Cloud Dataproc for batch and Cloud Dataflow for streaming

Answer

Lambda architecture with Cloud Dataflow for batch and Cloud Pub/Sub for streaming

Answer

Use Cloud Data Fusion for both batch and streaming

Question 6

A retail company uses a Vertex AI endpoint to serve product recommendations. The model is a TensorFlow model deployed with a custom container. Recently, users have reported that recommendations are stale. The model is retrained daily using Vertex AI Pipelines. The pipeline completes successfully, but the endpoint continues to serve the old model. The team checks the pipeline logs and sees that the new model is uploaded to the Vertex AI Model Registry. The endpoint has traffic split set to 100% for the old model. The team needs to update the endpoint to serve the new model version. What should they do?

Accepted Answer

Update the endpoint to deploy the new model version from the registry and adjust traffic split. The pipeline successfully uploaded the new model to the Vertex AI Model Registry, but the endpoint still has its traffic split configured to 100% for the old model. To serve the new model, the team must explicitly update the endpoint to deploy the new model version from the registry and adjust the traffic split to route 100% of traffic to it. This is a standard operational step in Vertex AI: uploading a model does not automatically update the endpoint's deployment or traffic allocation.

Answer

Check the pipeline for errors in the deployment step

Answer

Re-upload the model with a different version ID

Answer

Redeploy the same model to the endpoint

Question 7

A data engineer is using Apache Spark on Dataproc to process a large dataset. They need to perform complex aggregation and transformation with high performance. The dataset has a known schema and they want to take advantage of Catalyst optimizer. Which Spark API should they use?

Accepted Answer

DataFrames. DataFrames are the correct choice because they combine a declarative, structured API with the Catalyst optimizer, which builds and optimizes a logical plan before execution. With a known schema, DataFrames let Spark apply rule-based and cost-based optimizations such as predicate pushdown, column pruning, and join reordering. This delivers high performance for complex aggregations and transformations without sacrificing the structured API benefits.

Answer

Spark SQL only

Answer

Datasets

Answer

RDDs

Question 8

A mobile app needs an offline-first NoSQL database that syncs data across devices when connectivity is available. Which Google Cloud database meets these requirements?

Accepted Answer

Firestore. Firestore is a NoSQL, serverless, offline-first database that automatically syncs data across devices when connectivity is restored. It provides built-in offline persistence and real-time synchronization, making it ideal for mobile apps that need to work offline and sync later.

Answer

Memorystore

Answer

Cloud SQL

Answer

Bigtable

Question 9

A company uses Vertex AI to serve a model. They notice that some predictions are incorrect due to data drift. What is the best way to detect and retrain the model automatically?

Accepted Answer

Use Vertex AI Model Monitoring with alerts and retraining pipeline. Vertex AI Model Monitoring is specifically designed to detect data drift and feature skew in production models. It can be configured to send alerts and trigger an automated retraining pipeline via Cloud Functions or Vertex AI Pipelines, enabling continuous model improvement without manual intervention. This directly addresses the need for automatic detection and retraining in response to data drift.

Answer

Store predictions in BigQuery and run scheduled queries

Answer

Create a Cloud Monitoring dashboard

Answer

Set up Cloud Logging metrics to monitor predictions

Question 10

Your Looker dashboard uses a BigQuery connection. You notice that some queries take over a minute. Which service can you enable to cache results in memory for sub-second Looker queries?

Accepted Answer

BigQuery BI Engine. BigQuery BI Engine is an in-memory analysis service that caches frequently accessed BigQuery data and accelerates queries, delivering sub-second response times for Looker dashboards. It integrates natively with BigQuery and Looker, so enabling BI Engine on the relevant project or dataset is the correct fix. Other options are separate database services that do not accelerate BigQuery queries.

Answer

Cloud SQL

Answer

Cloud Bigtable

Answer

Cloud Memorystore

Question 11

A data engineer needs to migrate 200 TB of on-premises Oracle data to BigQuery. The network bandwidth is limited to 100 Mbps, and the data must be loaded within 2 weeks. Which Google Cloud service is most appropriate for the initial data transfer?

Accepted Answer

Transfer Appliance. Transfer Appliance is correct because it is a physical device designed for large-scale offline data transfers when network bandwidth is insufficient. With 200 TB at 100 Mbps, the theoretical transfer time exceeds 190 days, far beyond the 2-week window. Transfer Appliance allows shipping the data directly to Google, bypassing network constraints entirely.

Answer

BigQuery Data Transfer Service for Oracle

Answer

Datastream

Answer

Storage Transfer Service

Question 12

A company uses dbt on BigQuery to transform data. They want to run dbt models on a schedule and manage environments (dev, prod). Which GCP service should they use to run dbt jobs?

Accepted Answer

Cloud Composer. Cloud Composer is an Apache Airflow managed service that can schedule dbt runs.

Answer

Dataflow

Answer

Cloud Scheduler

Answer

Cloud Build

Question 13

A data pipeline using Dataflow processes streaming data. Late-arriving events are currently being dropped. How should the team modify the pipeline to ensure late data is processed correctly?

Accepted Answer

Configure triggers with allowed lateness and accumulation of late firings.. Dataflow's triggers allow you to configure allowed lateness and accumulation of late firings, which ensures that late-arriving events are still processed within the correct window. By setting `withAllowedLateness` and specifying accumulation mode (e.g., `ACCUMULATING`), the pipeline will emit additional panes for late data and merge them with the existing window results, preventing data loss.

Answer

Use side inputs to join late data with the main stream.

Answer

Use streaming inserts into BigQuery and ignore late data.

Answer

Increase the window duration to cover late data.

Question 14

You are building a BigQuery table that contains nested and repeated fields (e.g., order with line items). You need to write a query that counts the number of line items per order. Which TWO SQL functions/techniques can you use?

Accepted Answer

UNNEST with COUNT. UNNEST flattens the repeated line items array into individual rows, allowing COUNT to aggregate the number of line items per order. Option D is correct because ARRAY_LENGTH directly returns the number of elements in the repeated field array, which corresponds to the line item count.

Answer

Window function ROW_NUMBER

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STRUCT with aggregation

Answer

SELECT * EXCEPT

Question 15

Refer to the exhibit. A team uses this Cloud Build configuration to deploy a service to Cloud Run. The deployment step fails with a 'Permission denied' error. What is the most likely cause?

Accepted Answer

The Cloud Build service account does not have the Cloud Run Admin role.. The Cloud Build service account (typically the default compute engine service account or a user-specified service account) must have the Cloud Run Admin role (roles/run.admin) to deploy services to Cloud Run. Without this IAM permission, the deployment step fails with a 'Permission denied' error, even if the build itself succeeds. The error occurs because Cloud Build attempts to call the Cloud Run Admin API (run.googleapis.com) to create or update the service, and the service account lacks the required authorization.

Answer

The Dockerfile is missing from the repository.

Answer

The Docker image tag is missing or malformed.

Answer

The region 'us-central1' is incorrect for Cloud Run.