- A
Cloud Storage for transaction logs
Why wrong: Cloud Storage is batch-oriented and does not support low-latency access needed for online fraud detection.
- B
Bigtable to store user profiles and transaction history for fast lookups
Bigtable offers sub-millisecond latency for point lookups, essential for real-time fraud scoring.
- C
Dataflow for stream processing with sliding windows
Dataflow can aggregate events in sliding windows and call a machine learning model for each window.
- D
Cloud SQL to store reference data
Why wrong: Cloud SQL cannot handle the throughput and latency requirements for real-time fraud detection with high transaction volumes.
- E
Cloud Functions for long-running batch model training
Why wrong: Cloud Functions has a timeout limit and is not suitable for training models; it is event-driven and short-lived.
Quick Answer
The answer is Bigtable and Dataflow. Bigtable provides the sub-10ms latency needed for real-time lookups of user profiles and transaction history, directly supporting the sub-second requirement for high-value transactions, while Dataflow’s stream processing with sliding windows enables the continuous aggregation and pattern detection essential for real-time fraud detection architecture. On the Google Professional Data Engineer exam, this pairing tests your understanding of combining a low-latency NoSQL store with a unified stream and batch processing engine to meet strict SLAs. A common trap is choosing Cloud SQL or Spanner for the database, but those lack Bigtable’s consistent single-digit millisecond latency at high throughput. Memory tip: think “Bigtable for fast reads, Dataflow for sliding windows” — the two work together like a high-speed lookup table and a real-time calculator.
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A payment processing company needs to detect fraudulent transactions in real time. The system must have sub-second latency for high-value transactions and use a machine learning model. Which two components should be part of the architecture? (Choose TWO.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Bigtable to store user profiles and transaction history for fast lookups
Bigtable is a fully managed, scalable NoSQL database that provides consistent sub-10ms latency for high-throughput read/write operations, making it ideal for real-time lookups of user profiles and transaction history in fraud detection. Its ability to handle large volumes of data with low latency supports the sub-second requirement for high-value transactions.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Storage for transaction logs
Why it's wrong here
Cloud Storage is batch-oriented and does not support low-latency access needed for online fraud detection.
- ✓
Bigtable to store user profiles and transaction history for fast lookups
Why this is correct
Bigtable offers sub-millisecond latency for point lookups, essential for real-time fraud scoring.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Dataflow for stream processing with sliding windows
Why this is correct
Dataflow can aggregate events in sliding windows and call a machine learning model for each window.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud SQL to store reference data
Why it's wrong here
Cloud SQL cannot handle the throughput and latency requirements for real-time fraud detection with high transaction volumes.
- ✗
Cloud Functions for long-running batch model training
Why it's wrong here
Cloud Functions has a timeout limit and is not suitable for training models; it is event-driven and short-lived.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between storage services optimized for real-time access (Bigtable) versus batch/archive (Cloud Storage) and between stream processing (Dataflow) versus batch processing or short-lived compute (Cloud Functions).
Detailed technical explanation
How to think about this question
Bigtable uses a distributed, sorted key-value store with automatic sharding and compaction, enabling consistent single-digit millisecond latency even under heavy write loads. In fraud detection, sliding windows in Dataflow allow aggregating transaction counts over time (e.g., 5-minute windows) to detect velocity anomalies, while Bigtable stores the user's historical transaction patterns for immediate comparison against incoming transactions.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Designing data processing systems — study guide chapter
Learn the concepts, then practise the questions
- →
Designing data processing systems practice questions
Targeted practice on this topic area only
- →
All PDE questions
499 questions across all exam domains
- →
Google Professional Data Engineer study guide
Full concept coverage aligned to exam objectives
- →
PDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PDE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Designing data processing systems practice questions
Practise PDE questions linked to Designing data processing systems.
Building and operationalizing data processing systems practice questions
Practise PDE questions linked to Building and operationalizing data processing systems.
Operationalizing machine learning models practice questions
Practise PDE questions linked to Operationalizing machine learning models.
Ensuring solution quality practice questions
Practise PDE questions linked to Ensuring solution quality.
PDE fundamentals practice questions
Practise PDE questions linked to PDE fundamentals.
PDE scenario practice questions
Practise PDE questions linked to PDE scenario.
PDE troubleshooting practice questions
Practise PDE questions linked to PDE troubleshooting.
Practice this exam
Start a free PDE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this PDE question test?
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Bigtable to store user profiles and transaction history for fast lookups — Bigtable is a fully managed, scalable NoSQL database that provides consistent sub-10ms latency for high-throughput read/write operations, making it ideal for real-time lookups of user profiles and transaction history in fraud detection. Its ability to handle large volumes of data with low latency supports the sub-second requirement for high-value transactions.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 30, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.