PDE Practice Test 10 — 10 Questions

Question 1

A company uses Workflows to orchestrate a series of Google Cloud services for data processing. They need to call an external HTTP API as part of the workflow and handle potential failures with retries. Which Workflows feature should they use?

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

Accepted Answer

Retry policy on the step. Workflows provides a built-in retry policy that can be configured on individual steps to automatically retry an HTTP call upon transient failures (e.g., 5xx server errors or network timeouts). This allows the workflow to handle external API failures without custom code, using exponential backoff and a maximum retry count.

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Subworkflows

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Parallel steps

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Conditional steps

Question 2

You need to track the lineage of data in BigQuery, showing how tables are derived from other tables via queries. Which service provides this capability?

Accepted Answer

BigQuery Lineage API. The BigQuery Lineage API provides programmatic access to lineage information, showing how tables are derived from other tables through queries, including column-level lineage. It captures dependencies from SQL queries, scheduled queries, and other BigQuery operations.

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Cloud Composer

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Cloud Data Catalog

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Dataflow

Question 3

A company is using Cloud Storage to store raw logs. They want to use Cloud Data Fusion to transform and load the data into BigQuery on a daily schedule. The transformations are complex and involve joining multiple datasets. What is the most efficient way to run these pipelines?

Accepted Answer

Use Cloud Data Fusion to design the pipeline and schedule it to run on a Dataproc cluster. Cloud Data Fusion is a fully managed, code-free ETL/ELT service built on CDAP that provides a visual pipeline designer with a rich set of plugins (including BigQuery sinks) and can execute pipelines on ephemeral Dataproc clusters. For complex transformations involving joins across multiple datasets, Data Fusion's Wrangler and Joiner transforms plus its scheduling capability make it the most efficient native choice. Scheduling the pipeline to run on a Dataproc cluster gives the compute needed for joins while keeping orchestration managed.

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Use Cloud Composer to orchestrate Dataproc jobs that run the transformations

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Use Cloud Functions to trigger a Dataflow job that does the transformations

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Use Cloud Dataprep to design the transformation and export to BigQuery

Question 4

You need to create a BigQuery table that stores customer transaction data. The table will be queried frequently by a customer_id column to retrieve recent transactions (last 30 days). Which table design optimizes query performance and cost?

Accepted Answer

Partition by transaction_date and cluster by customer_id. Partitioning by transaction_date aligns with the query filter (last 30 days), so BigQuery prunes all partitions outside that window, scanning only the relevant date range. Clustering by customer_id then sorts data within each partition, so the customer_id filter benefits from block-level pruning. This combination minimizes both bytes scanned (cost) and query latency.

Answer

Partition by customer_id and cluster by transaction_date

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Partition by ingestion_time and cluster by customer_id

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Cluster by transaction_date and customer_id without partitioning

Question 5

You are migrating an on-premises PostgreSQL database to Cloud SQL. You need to continuously replicate changes to BigQuery for real-time analytics with minimal latency. Which service should you use?

Accepted Answer

Datastream. Datastream is Google Cloud's serverless change data capture (CDC) service that continuously replicates changes from sources like PostgreSQL, MySQL, Oracle, and SQL Server into BigQuery or Cloud Storage with low latency. It reads the source's replication log and streams changes without impacting production workloads significantly. This directly matches the requirement for continuous, near-real-time replication into BigQuery.

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Dataflow with JDBC source

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Pub/Sub with a Cloud Function that writes to BigQuery

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Storage Transfer Service

Question 6

A team uses dbt on BigQuery to transform data in their data warehouse. They have a large table with nested and repeated fields (arrays and structs). The transformation needs to normalize this data into a star schema. Which dbt feature and BigQuery SQL feature should they use together?

Accepted Answer

dbt models with BigQuery UNNEST and CROSS JOIN. To normalize nested and repeated fields (arrays and structs) into a star schema, you need to flatten the arrays into separate rows. BigQuery's UNNEST operator, when used with CROSS JOIN, expands each array element into its own row, effectively denormalizing the nested structure. dbt models (SQL SELECT statements) are the correct dbt feature to define these transformations as version-controlled, reusable SQL files. Together, they allow you to write a dbt model that uses CROSS JOIN UNNEST to produce dimension and fact tables from a single nested table.

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dbt hooks with BigQuery STRUCT access

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dbt snapshots with BigQuery JSON functions

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dbt seeds with BigQuery ARRAY_AGG

Question 7

You need to schedule a simple workflow that fetches data from an API every hour, transforms it using Cloud Functions, and writes the result to Cloud Storage. The workflow has no complex branching or retry logic beyond basic retries. Which orchestration service is the MOST cost-effective and simplest to implement?

Accepted Answer

Workflows. Workflows is a serverless, fully managed orchestration service that charges only per step executed, making it ideal for simple linear workflows with basic retries. It natively integrates with Cloud Functions and Cloud Storage via connectors, so the hourly fetch-transform-write pipeline can be defined in YAML/JSON without managing infrastructure. Cloud Scheduler alone can trigger jobs but cannot orchestrate multi-step logic or handle the transform step.

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Cloud Scheduler

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Cloud Composer

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Dataflow

Question 8

You need to choose a messaging service for a real-time streaming application that requires low cost and can tolerate occasional message loss. Which service is MOST suitable?

Accepted Answer

Pub/Sub Lite. Pub/Sub Lite is designed for high-volume, low-cost streaming where occasional message loss is acceptable. Unlike standard Pub/Sub, it uses zonal or regional storage with pre-provisioned capacity, which dramatically lowers cost but does not guarantee the same durability or at-least-once delivery semantics under all failure conditions. This makes it the right fit when cost is prioritized over guaranteed delivery.

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Cloud Scheduler

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Pub/Sub

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Cloud Tasks

Question 9

Drag and drop the steps to create a Cloud Function triggered by Cloud Storage events into the correct order.

Accepted Answer

Step 1: Create a Cloud Storage bucket. Step 2: Write the Cloud Function code. Step 3: Deploy the Cloud Function with the appropriate event trigger. Step 4: Test the function by uploading a file to the bucket.. The correct sequence for creating a Cloud Function triggered by Cloud Storage events is: first ensure the bucket exists (create if needed), then write the function code, deploy the function with the appropriate event trigger (e.g., google.storage.object.finalize), and finally test by uploading a file. Common mistakes include deploying before creating the bucket, testing before deployment, or deploying without code.

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Step 1: Write the Cloud Function code. Step 2: Create a Cloud Storage bucket. Step 3: Deploy the Cloud Function with the appropriate event trigger. Step 4: Test the function by uploading a file to the bucket.

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Step 1: Create a Cloud Storage bucket. Step 2: Write the Cloud Function code. Step 3: Test the function by uploading a file to the bucket. Step 4: Deploy the Cloud Function with the appropriate event trigger.

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Step 1: Deploy the Cloud Function with the appropriate event trigger. Step 2: Create a Cloud Storage bucket. Step 3: Write the Cloud Function code. Step 4: Test the function by uploading a file to the bucket.

Question 10

A data science team has trained a TensorFlow model for image classification and wants to deploy it to production with minimal latency. They have already exported the model as a SavedModel directory. Which service should they use to create an online prediction endpoint?

Accepted Answer

Vertex AI Endpoints. Vertex AI Endpoints is the correct service for deploying a TensorFlow SavedModel to an online prediction endpoint with minimal latency. It provides managed, autoscaling infrastructure optimized for real-time inference, including GPU/TPU support, request batching, and automatic health checking, which are essential for production deployment.

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Cloud Functions

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AI Platform Prediction (legacy)

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Cloud Dataflow