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Read each scenario carefully and choose your answer before revealing the explanation. Then check why your choice was right or wrong. Repeat until the reasoning feels automatic.

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Designing Data Processing Systems practice questions

Practise Google Professional Data Engineer Designing Data Processing Systems practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security

20 questionsDomain: Designing Data Processing Systems

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What the exam tests

What to know about Designing Data Processing Systems

Designing Data Processing Systems questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Designing Data Processing Systems exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Designing Data Processing Systems questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice

Read the full Designing Data Processing Systems explanation →

A data engineer needs to design a stream processing pipeline that reads events from Pub/Sub, enriches them with data from a Cloud Storage file, and writes aggregated results to BigQuery. The pipeline must handle late-arriving events up to 1 hour. Which Dataflow feature should be used to manage late data?

Trap 1: Triggers

Triggers control when aggregate results are emitted, but watermarks signal late data.

Trap 2: Side inputs

Side inputs are for enriching streams with static or slowly changing data, not for managing late data.

Trap 3: Windowing

Windowing groups elements by time, but watermarks handle late arrivals.

Study all Designing Data Processing Systems common traps →

A
Triggers
Why wrong: Triggers control when aggregate results are emitted, but watermarks signal late data.
B
Watermarks
Watermarks track the event time progress and allowed lateness; Dataflow drops elements beyond the watermark.
C
Side inputs
Why wrong: Side inputs are for enriching streams with static or slowly changing data, not for managing late data.
D
Windowing
Why wrong: Windowing groups elements by time, but watermarks handle late arrivals.

Designing Data Processing Systems practice questions

What to know about Designing Data Processing Systems

Common Designing Data Processing Systems exam traps

Designing Data Processing Systems questions

A company uses Dataproc to run daily Spark ML jobs. The jobs run for 2 hours each day. The team wants to reduce costs without changing job characteristics. Which strategy is MOST cost-effective?

A data engineer needs to create a BigQuery table that is partitioned by ingestion time and clustered by customer_id and transaction_date. They also want to limit access so that only users from a specific domain can query the table. Which approach should they use?

A startup needs a fully managed, serverless Spark service to run occasional data processing jobs without managing clusters. They want to pay only for the resources used during job execution. Which Google Cloud service should they use?

A company wants to use Cloud Data Fusion to build ETL pipelines. They need to connect to a legacy on-premises database using JDBC and also want to use prebuilt transforms from the Hub. Which two features should they use?

A company uses Pub/Sub with push subscriptions to deliver events to a Cloud Run service. Recently, the service has been returning HTTP 429 (Too Many Requests), causing messages to be retried and eventually sent to the dead letter topic. What is the MOST likely cause?

A data engineer needs to process data in a Dataflow pipeline that reads from a Pub/Sub topic. The pipeline must group events into 5-minute windows and compute the average value per key. Which Beam transform should they use after windowing?

A data engineer is designing a real-time fraud detection system using Dataflow. The system must detect patterns across events from multiple users within a sliding window of 10 minutes. Events arrive on Pub/Sub topics per user. Which approach should they use to join the streams?

A company wants to use Dataprep to clean and transform raw CSV files stored in Cloud Storage before loading into BigQuery. The data quality checks show missing values and inconsistent date formats. Which Dataprep feature should they use to handle these issues?

A company needs a messaging service for event-driven applications that require low cost for high-throughput, but can tolerate occasional message loss. Which Pub/Sub product should they choose?

A retail company uses Dataflow to process real-time clickstream data. They need to enrich each event with customer profile data from Cloud Bigtable and session metadata from Cloud Spanner. Which two Dataflow features should they use?

A company is migrating on-premises Hadoop Hive workloads to Google Cloud. They want to use Dataproc for Spark processing and require a managed Hive metastore that can be shared across multiple Dataproc clusters. Which TWO components should they use?

A data engineer needs to design a BigQuery dataset for a multi-team environment. Each team should have read access only to specific tables, and the data must be protected from accidental deletion. Which THREE steps should they take?

A company wants to design a data pipeline for real-time fraud detection. The system must process streaming financial transactions, enrich them with user profiles from a lookup table, and flag suspicious activities within seconds. Which architecture pattern would be MOST suitable?

You are designing a BigQuery data warehouse for a multi-tenant SaaS application. Each tenant's data must be isolated and queried only by that tenant. You need to minimise management overhead and allow tenants to be added dynamically. Which approach should you use?

You need to process large-scale log files (hundreds of terabytes) using Apache Spark on Google Cloud. The job runs nightly and you want to minimise costs. Which Dataproc cluster configuration is MOST cost-effective?

A data pipeline ingests streaming events into Pub/Sub. You need to guarantee that each event is processed exactly once downstream in Dataflow. Which combination of Pub/Sub and Dataflow configurations should you use?

You are designing a Dataflow pipeline that reads from Pub/Sub and writes to BigQuery. The pipeline must handle late-arriving data (up to 1 hour) and group events into 10-minute windows. Which configuration is correct?

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