- A
Use a separate table per sensor type
Why wrong: Leads to table proliferation and complex queries across tables.
- B
Store the sensor data in a JSON column
JSON provides schema flexibility and cost-effective storage for varying fields.
- C
Use a schema with a STRUCT containing all possible fields as optional
Why wrong: Similar to wide table; still requires schema changes for new fields.
- D
Use a wide table with many nullable columns
Why wrong: High storage costs due to many NULLs; schema changes still needed for new fields.
Question 526 of 1,000
PCDE Design and implement database schemas Practice Question
This PCDE practice question tests your understanding of design and implement database schemas. 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.
You are designing a BigQuery schema for IoT sensor data. The sensor readings have varying fields depending on the sensor type. You want to minimize storage costs and avoid schema maintenance when new sensor types are added. What is the best schema design?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Store the sensor data in a JSON column
Option B is correct because storing sensor data in a JSON column leverages BigQuery's native support for semi-structured data (the `JSON` data type), allowing you to ingest records with varying fields without schema changes. This minimizes storage costs by avoiding the overhead of many NULL columns and eliminates the need for schema maintenance when new sensor types are added, as BigQuery can query JSON fields directly using functions like `JSON_EXTRACT` or dot notation.
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.
- ✗
Use a separate table per sensor type
Why it's wrong here
Leads to table proliferation and complex queries across tables.
- ✓
Store the sensor data in a JSON column
Why this is correct
JSON provides schema flexibility and cost-effective storage for varying fields.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a schema with a STRUCT containing all possible fields as optional
Why it's wrong here
Similar to wide table; still requires schema changes for new fields.
- ✗
Use a wide table with many nullable columns
Why it's wrong here
High storage costs due to many NULLs; schema changes still needed for new fields.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that a STRUCT with optional fields is equivalent to a JSON column, but the trap is that a STRUCT still requires a fixed schema definition, whereas JSON allows fully dynamic fields without schema changes.
Trap categories for this question
Similar concept trap
Similar to wide table; still requires schema changes for new fields.
Detailed technical explanation
How to think about this question
BigQuery's `JSON` data type stores data in a native binary format (using the `STRUCT` representation internally) that allows efficient querying without full deserialization. Under the hood, BigQuery compresses JSON columns using columnar compression (e.g., run-length encoding for repeated keys), which can be more space-efficient than storing many sparse columns. In a real-world IoT scenario with thousands of sensor types, using JSON eliminates the need for frequent ALTER TABLE statements and allows schema-on-read flexibility, where new fields are automatically queryable as soon as they appear in the data.
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.
Quick reference
Common DNS Record Types
| Record | Purpose | Example |
|---|---|---|
| A | IPv4 address mapping | example.com → 93.184.216.34 |
| AAAA | IPv6 address mapping | example.com → 2606:2800::1 |
| CNAME | Alias to another hostname | www → example.com |
| MX | Mail server for domain | example.com → mail.example.com (priority 10) |
| TXT | Text data (SPF, DKIM, verification) | v=spf1 include:_spf.example.com ~all |
| NS | Authoritative name servers | example.com NS ns1.example.com |
| PTR | Reverse DNS (IP → hostname) | 34.216.184.93.in-addr.arpa → example.com |
| SOA | Zone authority record | Primary NS, admin email, serial, TTL defaults |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PCDE question test?
Design and implement database schemas — This question tests Design and implement database schemas — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Store the sensor data in a JSON column — Option B is correct because storing sensor data in a JSON column leverages BigQuery's native support for semi-structured data (the `JSON` data type), allowing you to ingest records with varying fields without schema changes. This minimizes storage costs by avoiding the overhead of many NULL columns and eliminates the need for schema maintenance when new sensor types are added, as BigQuery can query JSON fields directly using functions like `JSON_EXTRACT` or dot notation.
What should I do if I get this PCDE 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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
This PCDE 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 PCDE exam.
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