- A
Row keys are too short
Why wrong: Length not the primary cause; descending order causes hotspots.
- B
Row keys cause hotspotting because of descending timestamps
Correct: descending timestamps concentrate writes on a single tablet server, causing hotspots.
- C
There are too many column families
Why wrong: Number of column families does not cause scan latency due to hotspotting.
- D
Column family design is incorrect
Why wrong: Column families affect read performance but not the hotspotting from row key pattern.
Question 178 of 980
Design Scalable and Highly Available Cloud Database Solutions →mediumMultiple ChoiceObjective-mapped
PCD Practice Question: Design Scalable and Highly Available Cloud Database Solutions
This PCD practice question tests your understanding of design scalable and highly available cloud database solutions. 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.
Your Bigtable instance is experiencing high latency on read queries that scan a large range of rows. The row keys are timestamps in descending order (e.g., '2024-01-01#user123'). What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Row keys cause hotspotting because of descending timestamps
Descending timestamps as row keys cause hotspotting because new writes (which are always the most recent timestamp) are concentrated on a single tablet server node, creating a 'hot node' that also serves read queries for that range. Bigtable splits and load-balances by row key prefix, so sequential descending keys like '2024-01-01#...', '2024-01-02#...' are written to the same tablet, leading to uneven load and high read latency for scans over large ranges.
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.
- ✗
Row keys are too short
Why it's wrong here
Length not the primary cause; descending order causes hotspots.
- ✓
Row keys cause hotspotting because of descending timestamps
Why this is correct
Correct: descending timestamps concentrate writes on a single tablet server, causing hotspots.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
There are too many column families
Why it's wrong here
Number of column families does not cause scan latency due to hotspotting.
- ✗
Column family design is incorrect
Why it's wrong here
Column families affect read performance but not the hotspotting from row key pattern.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that descending timestamps are a good way to keep recent data at the top of scans, but the trap is that this creates a hotspot on the last tablet, causing both write and read bottlenecks.
Detailed technical explanation
How to think about this question
Bigtable uses a lexicographic sort on row keys to determine tablet boundaries; descending timestamps cause all recent writes to land in the same tablet because the key space is not uniformly distributed. To avoid this, you can prefix the timestamp with a hash or a field that varies (e.g., user ID) to spread writes across tablets, while still allowing range scans by using a composite key like 'user123#2024-01-01'. In production, this pattern is common for time-series data where you need both write distribution and efficient time-range queries.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
- →
Design Scalable and Highly Available Cloud Database Solutions — study guide chapter
Learn the concepts, then practise the questions
- →
Design Scalable and Highly Available Cloud Database Solutions practice questions
Targeted practice on this topic area only
- →
All PCD questions
980 questions across all exam domains
- →
Google Professional Cloud Developer study guide
Full concept coverage aligned to exam objectives
- →
PCD practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PCD practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Manage a Solution that Can Span Multiple Database Systems practice questions
Practise PCD questions linked to Manage a Solution that Can Span Multiple Database Systems.
Deploy Scalable and Highly Available Databases in Google Cloud practice questions
Practise PCD questions linked to Deploy Scalable and Highly Available Databases in Google Cloud.
Design Scalable and Highly Available Cloud Database Solutions practice questions
Practise PCD questions linked to Design Scalable and Highly Available Cloud Database Solutions.
Migrate Data Solutions practice questions
Practise PCD questions linked to Migrate Data Solutions.
Designing highly scalable, available, and reliable cloud-native applications practice questions
Practise PCD questions linked to Designing highly scalable, available, and reliable cloud-native applications.
Building and testing applications practice questions
Practise PCD questions linked to Building and testing applications.
Deploying applications practice questions
Practise PCD questions linked to Deploying applications.
Integrating Google Cloud services practice questions
Practise PCD questions linked to Integrating Google Cloud services.
Managing application performance monitoring practice questions
Practise PCD questions linked to Managing application performance monitoring.
PCD fundamentals practice questions
Practise PCD questions linked to PCD fundamentals.
PCD scenario practice questions
Practise PCD questions linked to PCD scenario.
PCD troubleshooting practice questions
Practise PCD questions linked to PCD troubleshooting.
Practice this exam
Start a free PCD 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 PCD question test?
Design Scalable and Highly Available Cloud Database Solutions — This question tests Design Scalable and Highly Available Cloud Database Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Row keys cause hotspotting because of descending timestamps — Descending timestamps as row keys cause hotspotting because new writes (which are always the most recent timestamp) are concentrated on a single tablet server node, creating a 'hot node' that also serves read queries for that range. Bigtable splits and load-balances by row key prefix, so sequential descending keys like '2024-01-01#...', '2024-01-02#...' are written to the same tablet, leading to uneven load and high read latency for scans over large ranges.
What should I do if I get this PCD question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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: Jul 4, 2026
This PCD 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 PCD 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.