Searching for GCP Professional Data Engineer exam questions and answers usually lands you in one of two places: dump sites selling stale memorized questions (risky and against the rules), or question banks that give you a letter answer with no reasoning. Neither teaches you anything. So let's do it properly — here are five original, exam-style questions with fully worked answers. Each one encodes a pattern that shows up on the real PDE again and again.
Want more of these with instant explanations? Start with the free GCP Professional Data Engineer practice test — it's the same style, and genuinely free.
Question 1: The streaming deduplication classic
Your pipeline ingests IoT events through Pub/Sub into Dataflow and writes to BigQuery. Duplicate events are appearing in BigQuery. Delivery must remain at-least-once at the ingestion layer. What should you do?
A. Switch Pub/Sub to exactly-once delivery and remove Dataflow
B. Attach a unique ID to each event at the publisher and deduplicate in Dataflow using that ID
C. Run a scheduled query that deletes duplicates from BigQuery hourly
D. Enable BigQuery streaming insert best-effort deduplication and rely on it
Answer: B. Pub/Sub is at-least-once by design (the stem even pins that), so duplicates are expected upstream. The idiomatic fix is an ID at the source plus withIdAttribute-style dedup in Dataflow. C treats the symptom with cost and latency; D's best-effort dedup is explicitly not a guarantee. The pattern: fix data problems as early in the pipeline as possible, with the mechanism Google built for it.
Question 2: The storage-picker
You need to store 10 PB of time-series sensor data with sub-10ms row lookups by device ID and timestamp, and you'll run occasional analytical scans. Which storage service?
A. Cloud SQL B. BigQuery C. Bigtable D. Firestore
Answer: C. Sub-10ms single-row lookups at petabyte scale is the textbook Bigtable use case, with a row key like device_id#reversed_timestamp to avoid hotspotting. BigQuery is the analytics complement (and can query Bigtable externally), not a low-latency row store. Cloud SQL dies at that scale; Firestore is for app documents, not wide time-series. The pattern: latency + scale + access pattern picks the store, and every PDE sitting has two or three of these.
Question 3: The Dataflow window puzzle
You compute per-minute averages from a stream, but late events arriving up to 30 minutes after their event time must still be counted. What do you configure?
A. Fixed 1-minute windows with allowed lateness of 30 minutes and accumulating triggers
B. Session windows with a 30-minute gap
C. A 30-minute sliding window emitting every minute
D. Drop late data and increase the watermark
Answer: A. The requirement decomposes cleanly: per-minute aggregation → fixed 1-minute windows; late data up to 30 minutes → allowed lateness; updated results when stragglers arrive → accumulating fired panes. Session windows model user activity gaps, not fixed reporting periods. The pattern: translate each business clause into exactly one windowing concept — the exam writes questions so this mapping is one-to-one.
Question 4: The IAM least-privilege trap
Data analysts need to run queries against datasets in a shared project but must not be able to create or delete tables. Which role?
A. BigQuery Admin on the project
B. BigQuery Data Editor on the datasets
C. BigQuery Data Viewer on the datasets plus BigQuery Job User on the project
D. BigQuery Job User on the datasets only
Answer: C. Two distinct permissions are in play: reading data (Data Viewer, granted at dataset level) and running jobs (Job User, a project-level capability — jobs run in a project). Data Editor grants table mutation, violating the requirement; Job User alone can run jobs but read nothing. The pattern: BigQuery access = data-side role + job-side role, and the exam loves splitting them.
Question 5: The migration judgment call
A 40-node on-prem Hadoop cluster runs Spark ETL. The company wants to minimize refactoring now but reduce ops burden. What do you recommend?
A. Rewrite everything as BigQuery SQL immediately
B. Lift the workloads to Dataproc with ephemeral, job-scoped clusters and data in Cloud Storage
C. Rebuild every job in Dataflow
D. Run Hadoop on GCE VMs exactly as-is
Answer: B. "Minimize refactoring" keeps Spark → Dataproc; "reduce ops" means ephemeral clusters with Cloud Storage replacing HDFS, so compute is disposable. A and C are the eventual modernization answers, wrong for the stated constraint; D reduces nothing. The pattern: the constraint sentence, not the technology's coolness, selects the answer. When the stem says "minimal changes," respect it.
What these five just taught you
Notice that we covered streaming semantics, storage selection, windowing, IAM, and migration strategy — that's a large slice of the actual blueprint, and each answer followed a transferable rule rather than a memorized fact. That's the difference between studying questions-with-answers and studying dumps: one generalizes to the 60 questions you'll actually face, the other bets your certification (and integrity) on seeing an exact repeat.
Work through a full domain-weighted set free at examcert.app. And when a miss doesn't make sense, the AI simulator at ai.examcert.app explains every option on the spot — the fastest way I know to turn a wrong answer into one of these patterns instead of a shrug.
Top comments (0)