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PDE Data Processing Question Bank (22 Questions)

Browse all 22 practice questions covering Designing Data Processing Systems for the PDE certification exam. Answers are intentionally hidden on this page so you can self-test first before checking results in quiz mode.

Question 1Ingesting and Processing Data
How do you handle late-arriving data in a Pub/Sub-to-BigQuery streaming pipeline?
ADiscard late data
BConfigure Dataflow windowing with allowed lateness — late data triggers recomputation of window results, and a dead-letter topic captures data that arrives after the allowed lateness period
CBuffer all data indefinitely
DProcess everything in batch
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Question 2Designing Data Processing Systems
When should you choose batch processing over stream processing for a data pipeline?
AAlways use streaming
BWhen data freshness requirements are hours/daily (not real-time), datasets are bounded and complete, and processing logic requires global aggregations across the full dataset
CNever use batch
DBatch is always cheaper
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Question 3Designing Data Processing Systems
You need to process streaming data from IoT devices with exactly-once semantics and load it into BigQuery. What pipeline architecture should you use?
ACloud Functions reading from Pub/Sub
BPub/Sub → Dataflow (Apache Beam streaming pipeline) → BigQuery — Dataflow provides exactly-once processing with windowing and watermarks
CPub/Sub → Cloud SQL
DDirect insert into BigQuery from devices
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Question 4Designing Data Processing Systems
Your team needs near-real-time analytics with less than 1-minute latency from Pub/Sub to BigQuery. What approach is most cost-effective?
ADataflow streaming with 1-second windows
BPub/Sub subscription writing directly to BigQuery (BigQuery Subscription) — a zero-code, auto-managed pipeline with sub-minute latency at lower cost than running Dataflow
CBatch load every minute
DCloud Functions processing each message
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Question 5Maintaining and Automating Data Workloads
How do you monitor a streaming Dataflow pipeline for performance issues?
ACheck logs once a day
BMonitor system lag, data freshness, watermark age, worker CPU/memory, and throughput in Cloud Monitoring — set alerts on watermark age exceeding thresholds
COnly check if the job is running
DUse BigQuery to check output
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Question 6Designing Data Processing Systems
How does Dataflow achieve exactly-once processing in streaming pipelines?
AIt processes each record only once naturally
BThrough checkpointing, record deduplication based on message IDs, and transactional commits to sinks — ensuring each record is processed and written exactly once even on retries
CIt uses Pub/Sub's exactly-once guarantee
DThrough idempotent writes only
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Question 7Maintaining and Automating Data Workloads
How do you backfill historical data through a Dataflow streaming pipeline?
AReplay all Pub/Sub messages
BRun the same Beam pipeline in batch mode reading from Cloud Storage (historical data) — Apache Beam's unified model allows the same transforms to work on both bounded and unbounded data
CWrite a separate batch pipeline
DBackfilling is impossible
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Question 8Maintaining and Automating Data Workloads
How should you test a Dataflow pipeline before deploying to production?
ATest only in production
BUnit test transforms with DirectRunner (local), integration test with sample data on TestRunner, validate output schema/content, and run the pipeline on a test dataset in a staging project
CSkip testing for data pipelines
DManual data inspection
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Question 9Ingesting and Processing Data
How do you reuse Dataflow pipelines across teams without sharing source code?
ACopy the code to each team
BCreate Dataflow templates (Classic or Flex) — pre-packaged pipelines that can be executed with runtime parameters without access to source code
CShare a VM with the code
DUse Cloud Functions instead
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Question 10Ingesting and Processing Data
How do you design Pub/Sub topics and subscriptions for a multi-consumer data pipeline?
AOne topic with one subscription
BOne topic per data type, multiple subscriptions per topic — each subscription delivers messages to a different consumer independently (fan-out pattern)
COne topic for all data
DSeparate topics per consumer
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Question 11Ingesting and Processing Data
When should you use Dataflow SQL instead of writing Java/Python Beam pipelines?
AAlways use SQL
BDataflow SQL for SQL-familiar analysts to build streaming pipelines using ZetaSQL — useful for simple filtering, aggregation, and joins on Pub/Sub and BigQuery data without coding
CDataflow SQL is deprecated
DOnly for batch processing
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Question 12Ingesting and Processing Data
What are the differences between BigQuery's streaming insert (legacy) and Storage Write API?
AThey are the same
BStorage Write API: exactly-once, higher throughput, lower cost, supports transactions. Legacy streaming: at-least-once, simpler API, per-row pricing. Prefer Storage Write API for new development
CLegacy is better
DStorage Write API is for batch only
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Question 13Ingesting and Processing Data
What is the most efficient way to load large volumes of data into BigQuery?
AINSERT statements
BBigQuery load jobs (batch) for bulk data from Cloud Storage — free, supports Parquet/Avro/CSV/JSON, and handles schema auto-detection. Use Storage Write API for high-throughput streaming
CStreaming inserts for everything
Dbq query with INSERT INTO
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Question 14Designing Data Processing Systems
You need to calculate the average temperature per sensor every 5 minutes from streaming data. What Dataflow windowing strategy should you use?
AGlobal window
BFixed windows of 5 minutes — each window collects all events in a 5-minute interval, then computes the aggregate when the window closes
CSliding windows
DSession windows
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Question 15Ingesting and Processing Data
When should you use Cloud Data Fusion instead of writing custom Dataflow pipelines?
AAlways use custom Dataflow
BWhen citizen data engineers need a visual, code-free ETL/ELT tool — Data Fusion provides a drag-and-drop UI with pre-built connectors for common sources and transformations
CNever use Data Fusion
DData Fusion replaces Dataflow entirely
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Question 16Maintaining and Automating Data Workloads
How should you handle errors and failed records in a Dataflow pipeline?
ALet the pipeline crash
BImplement dead-letter queues — catch and route failed records to a separate Pub/Sub topic or BigQuery table for later investigation, while the pipeline continues processing valid records
CRetry infinitely
DSkip all errors silently
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Question 17Ingesting and Processing Data
How do you handle schema evolution in a streaming pipeline when the source schema changes?
AStop the pipeline and rebuild
BUse Avro or Protocol Buffers (support schema evolution), configure BigQuery to auto-detect schema changes, and implement forward-compatible schemas in Dataflow with dead-letter for incompatible records
CReject all schema changes
DUse schema-less formats only
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Question 18Designing Data Processing Systems
How do you estimate costs for a BigQuery + Dataflow data platform?
AGuess based on similar projects
BGoogle Cloud Pricing Calculator — estimate BigQuery (storage + queries/slots), Dataflow (worker hours + Streaming Engine), Pub/Sub (throughput), and Cloud Storage. Use INFORMATION_SCHEMA for actual usage data
CJust pay as you go
DCosts are fixed
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Question 19Maintaining and Automating Data Workloads
A Dataflow streaming job's system lag is increasing over time. How do you troubleshoot?
ARestart the job
BCheck: 1) Worker CPU/memory (under-provisioned?), 2) Hot keys (uneven data distribution), 3) Slow external calls (API/DB bottleneck), 4) Data skew in GroupByKey. Scale up workers or add key rebalancing
CIncrease Pub/Sub throughput
DWait for it to recover
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Question 20Designing Data Processing Systems
When should you use Dataproc instead of Dataflow?
AAlways
BFor existing Hadoop/Spark workloads, when you need Spark-specific libraries, or when the team has Spark expertise
CFor streaming only
DFor SQL queries only
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Question 21Maintaining and Automating Data Workloads
How should you handle late-arriving data in streaming pipelines?
ADiscard late data
BUse Dataflow watermarks, allowed lateness, and triggers to handle late data by updating windows when late elements arrive
CProcess everything immediately
DBuffer all data indefinitely
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Question 22Designing Data Processing Systems
A data lake needs to support both batch and streaming data processing with unified code. Which Google Cloud service provides this?
ADataproc
BDataflow (Apache Beam)
CBigQuery
DCloud Composer
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Key Data Processing Concepts for PDE

bigquerydataflowdataprocpub/subcomposerpipelinebatchstreaming

PDE Data Processing Exam Tips

Designing Data Processing Systems questions in PDE are typically scenario-based. Focus on service-level decision making aligned to official exam objectives. Priority concepts: bigquery, dataflow, dataproc, pub/sub, composer, pipeline.

What PDE Expects

Anchor your answer in select the most practical, secure, and scalable answer for the stated scenario.
Data Processing scenarios for PDE are frequently mapped to Domain 1 (~23%), so read the objective carefully before picking controls or architecture.
Expect multi-topic scenarios where Data Processing interacts with IAM, networking, data, or operations patterns rather than appearing as an isolated question.
When two options are both technically valid, prefer the choice that best aligns with the exam's operational scope (Professional) and vendor best practices.

High-Value Data Processing Concepts

Know the core Data Processing building blocks cold: bigquery, dataflow, dataproc, pub/sub.
Review the edge-case features and limits for composer, pipeline; these details are commonly used to differentiate answer choices.
Practice service-integration reasoning: how Data Processing pairs with Ingesting & Processing, Storing & Managing in real deployment patterns.
For PDE, explain why the chosen Data Processing design meets reliability, security, and cost expectations better than the alternatives.

Common PDE Traps

Watch for answers that partially solve the requirement but miss operational constraints.
Questions in Designing Data Processing Systems often include distractors that look correct for Data Processing but violate least-privilege, reliability, or scalability requirements.
Avoid picking options purely by feature name; validate data path, failure handling, and governance impact before answering.
If the prompt hints at automation or repeatability, eliminate manual-only operational answers first.

Fast Review Checklist

Can you compare at least two Data Processing implementation paths and justify which one best fits the scenario?
Can you map the chosen answer back to Designing Data Processing Systems (~23%) outcomes for PDE?
Can you explain security and access boundaries for Data Processing without relying on default-open assumptions?
Can you describe how Data Processing integrates with Ingesting & Processing and Storing & Managing during failure, scaling, and monitoring events?

Exam Domains Covering Data Processing

Domain 1Designing Data Processing Systems~23%

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