Question 897 of 999

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. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?

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

Cloud Bigtable

Cloud Bigtable is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, making it ideal for petabyte-scale time-series IoT sensor data. It supports single-digit millisecond latency for reads and writes at millions of operations per second through its underlying storage engine (based on Google's Chubby and SSTable technology) and its native integration with the HBase API, which provides efficient key-value access with timestamp-based versioning.

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 Bigtable

    Why this is correct

    Bigtable is the correct choice: wide-column NoSQL, designed for time-series and IoT workloads, single-digit ms latency, scales to millions of QPS.

    Related concept

    Read the scenario before looking for a memorised answer.

  • BigQuery

    Why it's wrong here

    BigQuery is an analytics data warehouse with query latency in seconds, not designed for millisecond lookups at IoT scale.

  • Firestore

    Why it's wrong here

    Firestore is a document database suited for mobile/web apps with hierarchical data, not for high-throughput time-series.

  • Cloud Spanner

    Why it's wrong here

    Spanner is a globally distributed relational database for ACID transactions, not single-digit ms time-series reads at millions of QPS.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The exam tests the misconception that BigQuery is suitable for real-time operational queries because of its 'fast' reputation, but the trap here is confusing a data warehouse optimized for analytical batch queries with a NoSQL database designed for high-throughput, low-latency key-value access.

Detailed technical explanation

How to think about this question

Cloud Bigtable stores data in sorted key-value maps where the row key is typically a composite of timestamp and device ID, enabling efficient range scans for time-series queries. Under the hood, Bigtable uses a distributed, sharded tablet architecture where each tablet is a sorted list of rows stored in SSTables, and the system automatically splits and rebalances tablets to maintain performance. A real-world scenario is Google's own use of Bigtable for its search indexing and Google Maps traffic data, where it handles petabytes of data with consistent sub-10ms latency.

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.

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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: Cloud Bigtable — Cloud Bigtable is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, making it ideal for petabyte-scale time-series IoT sensor data. It supports single-digit millisecond latency for reads and writes at millions of operations per second through its underlying storage engine (based on Google's Chubby and SSTable technology) and its native integration with the HBase API, which provides efficient key-value access with timestamp-based versioning.

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.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jul 4, 2026

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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.