PDE Practice Test 4 — 10 Questions

Question 1

A company wants to trigger a Cloud Run service whenever a new file is uploaded to a specific Cloud Storage bucket. Which event-driven solution should they use?

Accepted Answer

Eventarc with Cloud Storage trigger and Cloud Run destination. Eventarc is the recommended service for routing events from Cloud Storage to Cloud Run because it provides a fully managed, event-driven architecture with built-in filtering and retry logic. When a new file is uploaded, Cloud Storage emits a notification that Eventarc captures and delivers directly to the Cloud Run service as an HTTP request, enabling serverless processing without polling or additional infrastructure.

Answer

Cloud Scheduler to periodically poll the bucket

Answer

Cloud Functions triggered by Cloud Storage

Answer

Pub/Sub with a push subscription to Cloud Run

Question 2

A data engineer needs to transfer 5 PB of historical data from an on-premises Hadoop cluster to Cloud Storage. The network bandwidth is limited to 1 Gbps, and the transfer must complete within 30 days. Which transfer method should they use?

Accepted Answer

Transfer Appliance. The Transfer Appliance is a physical device designed for large-scale data transfers when network bandwidth is insufficient. With 5 PB of data and a 1 Gbps link, the theoretical maximum transfer time is over 500 days (5 PB × 8 bits/byte / 1 Gbps / 86400 seconds/day), far exceeding the 30-day window. The Transfer Appliance bypasses network constraints by shipping data physically to Google Cloud. Option A (gsutil rsync over the internet) is incorrect because it relies on the same 1 Gbps bandwidth, making it impossible to transfer 5 PB within 30 days. Option B (BigQuery Data Transfer Service) is designed for transferring data into BigQuery from cloud sources, not for large on-premises data transfers. Option C (Storage Transfer Service for on-premises) is intended for smaller or incremental transfers over a network, not for a 5 PB initial load within 30 days.

Answer

gsutil rsync over the internet

Answer

BigQuery Data Transfer Service

Answer

Storage Transfer Service for on-premises

Question 3

A data engineer is designing a batch processing system using Cloud Dataproc. Which TWO practices improve performance and reduce costs? (Choose TWO.)

Accepted Answer

Set autoscaling policies based on YARN memory.. Autoscaling policies based on YARN memory allow the cluster to dynamically add or remove worker nodes in response to actual resource demand from running jobs. This prevents over-provisioning (reducing costs) and ensures sufficient resources for job completion (improving performance), as Cloud Dataproc directly monitors YARN memory metrics to trigger scaling actions.

Answer

Always use persistent disks for all nodes.

Answer

Store intermediate data in HDFS.

Answer

Use the largest machine types for master nodes.

Question 4

A company uses Google Ads and wants to automatically load their advertising data into BigQuery daily. They also need to transform the data with SQL and schedule a recurring query. Which combination of services meets these requirements with minimal operational overhead?

Accepted Answer

BigQuery Data Transfer Service for Google Ads and scheduled queries. BigQuery Data Transfer Service has a built-in Google Ads connector that automatically loads advertising data into BigQuery on a schedule, and BigQuery scheduled queries let you transform that data with SQL on a recurring basis — both fully managed with no infrastructure. This combination meets the daily load and SQL transformation requirements with minimal operational overhead.

Answer

Cloud Functions triggered by Cloud Scheduler to call Google Ads API and load into BigQuery

Answer

Cloud Composer to extract Google Ads API and Dataflow to transform

Answer

Storage Transfer Service to move CSV files to GCS, then load into BigQuery

Question 5

An organization wants to automate their batch data processing pipeline using Cloud Composer. The pipeline consists of multiple tasks: extract from Cloud Storage, transform with Dataflow, and load into BigQuery. Which Airflow operator should be used to run Dataflow jobs?

Accepted Answer

DataflowCreatePythonJobOperator. B is correct because the DataflowCreatePythonJobOperator is specifically designed to submit and manage Apache Beam pipelines written in Python as Dataflow jobs in Google Cloud. This operator handles the creation of a Dataflow job from a Python file, which aligns with the requirement to run Dataflow transformations within a Cloud Composer DAG.

Answer

BigQueryInsertJobOperator

Answer

GCSToBigQueryOperator

Answer

DataprocSubmitJobOperator

Question 6

A company uses Workflows to orchestrate a multi-step data pipeline. One step calls an HTTP endpoint that may take up to 10 minutes, but the default Workflows timeout is too short. They also need to handle transient errors with retries. Which TWO configurations should they apply? (Choose 2)

Accepted Answer

Set a step timeout of 600 seconds for the HTTP call step. Option A is correct because Workflows steps support a per-step timeout, and setting it to 600 seconds (10 minutes) ensures the HTTP call step is allowed to run for the full duration instead of being cut off by the shorter default step timeout. Option E is correct because transient errors (such as 5xx responses or connection resets) should be handled by attaching a retry policy to the step with conditions that match those transient failures, so the step is retried automatically. Option B is not appropriate because Workflows does not use dead letter queues for failed steps; failures are surfaced through execution errors and logs. Option C is wrong because relying on the default retry policy does not let them target transient errors with appropriate conditions, and the default may not retry at all. Option D is wrong because setting the workflow execution timeout to 600 seconds would cap the entire workflow at 10 minutes, which is not the same as giving the HTTP step enough time and could still cause premature termination.

Answer

Configure a dead letter queue for failed steps

Answer

Use the default retry policy on the step

Answer

Set the workflow execution timeout to 600 seconds

Question 7

A data team uses Looker Studio to create a report that combines data from two different BigQuery tables: one with sales transactions and another with customer demographics. They need to join these tables in the report without writing SQL. Which feature should they use?

Accepted Answer

Data blending. Looker Studio's data blending feature allows combining data from multiple sources (including BigQuery tables) using a common key, without writing SQL. It provides a graphical interface to define joins. Creating a custom query requires SQL. Looker Studio reports support multiple charts, but blending is the specific feature for joining data. Calculated fields transform data within a single source.

Answer

Creating a report with multiple charts

Answer

Custom query in BigQuery connector

Answer

Calculated fields

Question 8

A data engineer needs to alert when Pub/Sub subscription has messages older than 1 hour. Which Cloud Monitoring metric and filter should they use?

Accepted Answer

Metric: subscription/oldest_unacked_message_age; filter: subscription_id. The metric subscription/oldest_unacked_message_age directly reports the age of the oldest unacknowledged message in a subscription, which is exactly what's needed to alert when messages are older than 1 hour. The filter subscription_id scopes the metric to the specific subscription. This metric is designed for monitoring message backlog age and is the correct choice for this requirement.

Answer

Metric: topic/send_message_operation_count; filter: topic_id

Answer

Metric: subscription/ack_message_count; filter: subscription_id

Answer

Metric: subscription/num_undelivered_messages; filter: subscription_id

Question 9

Which TWO are benefits of using Vertex AI Endpoints for model serving?

Accepted Answer

Integrated monitoring for prediction latency and error rates.. Option B is correct because Vertex AI Endpoints provide integrated monitoring that surfaces prediction latency and error rates, letting you track online serving health and performance directly in the console. Option C is correct because Vertex AI Endpoints support automatic scaling (autoscaling) based on traffic, adjusting the number of deployed model replicas to match request load. Options A, D, and E are not benefits of Endpoints: batch prediction is a separate Vertex AI feature (Batch Prediction jobs) rather than an Endpoint capability, automatic retraining on drift is not a built-in Endpoint function, and A/B testing requires explicit traffic-split configuration rather than working with no additional setup.

Answer

Batch prediction support out of the box.

Answer

Automatic model retraining when drift is detected.

Answer

Built-in support for A/B testing without any additional configuration.

Question 10

Match each machine learning term to its description.

Accepted Answer

Supervised Learning: Model trained on labeled data to predict output. In machine learning, supervised learning uses labeled data (inputs mapped to known outputs), unsupervised learning finds patterns in unlabeled data, features are input variables, and labels are the target outputs. Common confusions include mixing supervised/unsupervised definitions or swapping features and labels.

Answer

Supervised Learning: Model finds patterns without labeled data

Answer

Feature: The output variable the model predicts