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GCP · Professional

Google Professional Data Engineer

Prepare for this GCP Professional exam. Try an exam-style question first, then review the exam domains and practice-test format.

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Exam preparation details

300+

Practice Questions

5

Exam Domains

2

Practice Tests

120

Minutes

Practice-test format

50 Questions · 120 Minutes · Passing Score 700/1000

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Practice Questions 1

A ride-sharing platform has seen its daily telemetry and trip logs grow from 1.5 TB/day to 5 TB/day over the last six months. You manage nightly batch analytics written in classic Hadoop MapReduce running on a fixed 24-node Hadoop cluster (about 120 vCPUs total), and jobs are now missing the 6:00 a.m. SLA by 2–3 hours. Leadership wants the analytics to be more responsive (faster completion/latency) without increasing infrastructure costs. What should you recommend the development team do?

Apache Pig is a higher-level scripting layer that typically compiles into MapReduce jobs in classic Hadoop deployments. While it can improve developer productivity, it usually does not materially reduce runtime versus a well-written MapReduce job on the same engine. Without changing the execution engine (e.g., Tez or Spark), Pig won’t reliably recover a 2–3 hour SLA miss under increased data volume.
Apache Spark replaces MapReduce’s stage-by-stage disk materialization with a DAG execution model and can cache intermediate results in memory, often yielding large speedups on the same hardware. For growing batch workloads missing SLAs, Spark is a common recommendation to improve latency without adding nodes. It aligns with optimizing performance and cost by improving efficiency rather than scaling infrastructure.
Increasing the Hadoop cluster size (more nodes/vCPUs) is a straightforward way to reduce runtime, but it directly increases infrastructure costs, violating the stated constraint. On the exam, when leadership explicitly says “without increasing costs,” scaling out is typically disqualified unless paired with cost offsets (e.g., preemptibles/spot), which is not offered here.
Decreasing cluster size would further reduce available compute and likely worsen SLA performance. Hive can improve ease of querying, but on a classic Hadoop stack it often runs on MapReduce unless configured for alternative engines. Even if Hive improves productivity, combining it with fewer resources is unlikely to make jobs faster, making this option the least plausible.

Exam Domains

Use the exam weights to decide which domains to study first.

Designing Data Processing SystemsWeight 22%
Ingesting and Processing the DataWeight 25%
Storing the DataWeight 20%
Preparing and Using Data for AnalysisWeight 15%
Maintaining and Automating Data WorkloadsWeight 18%

Success Stories(9)

M
M*********Nov 25, 2025

Study period: 1 month

I tend to get overwhelmed with large exams, but doing a few questions every day kept me on track. The explanations and domain coverage felt balanced and practical. Happy to say I passed on the first try.

L
L*************Nov 25, 2025

Study period: 2 months

Thank you ! These practice questions helped me pass the GCP PDE exam at the first try.

S
S***********Nov 21, 2025

Study period: 1 month

The layout and pacing make it comfortable to study on the bus or during breaks. I solved around 20–30 questions a day, and after a few days I could feel my confidence improving.

정
정**Nov 19, 2025

Study period: 1 month

The explanations were primarily in English, but they still helped. The questions were similar to the actual exam too.

E
E********Nov 16, 2025

Study period: 2 months

I combined this app with some hands-on practice in GCP, and the mix worked really well. The questions pointed out gaps I didn’t notice during practice labs. Good companion for PDE prep.

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