- A
The results are delivered in real-time as data is ingested
Why wrong: The query processed already stored data; it is not a streaming query.
- B
The query required dedicated GPU clusters
Why wrong: BigQuery uses distributed computing on standard hardware, not GPUs.
- C
The ability to analyze petabytes of data without provisioning servers
BigQuery is a serverless data warehouse, eliminating the need for hardware management.
- D
The cloud provider automatically encrypts data at rest and in transit
Why wrong: While Google Cloud does offer encryption, this is not demonstrated by the query output.
- E
The pay-per-query model reduces costs compared to maintaining an on-premises data warehouse
Users pay only for the data scanned, which can be more cost-effective than owning and operating hardware.
Cloud Digital Leader Why cloud technology is transforming business Practice Question
This GCDL practice question tests your understanding of why cloud technology is transforming business. 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 data analyst runs the above query on Google BigQuery. Which TWO statements correctly describe how cloud technology is transforming business in this scenario?
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
The ability to analyze petabytes of data without provisioning servers
Option C is correct because Google BigQuery is a serverless data warehouse that automatically scales to handle petabytes of data without requiring users to provision or manage any servers. This eliminates the operational overhead of capacity planning and infrastructure management, directly demonstrating how cloud technology abstracts physical hardware and enables on-demand analytics at massive scale.
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.
- ✗
The results are delivered in real-time as data is ingested
Why it's wrong here
The query processed already stored data; it is not a streaming query.
- ✗
The query required dedicated GPU clusters
Why it's wrong here
BigQuery uses distributed computing on standard hardware, not GPUs.
- ✓
The ability to analyze petabytes of data without provisioning servers
Why this is correct
BigQuery is a serverless data warehouse, eliminating the need for hardware management.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The cloud provider automatically encrypts data at rest and in transit
Why it's wrong here
While Google Cloud does offer encryption, this is not demonstrated by the query output.
- ✓
The pay-per-query model reduces costs compared to maintaining an on-premises data warehouse
Why this is correct
Users pay only for the data scanned, which can be more cost-effective than owning and operating hardware.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that 'serverless' means 'real-time' or that cloud analytics require specialized hardware like GPUs, when in fact serverless services like BigQuery abstract infrastructure entirely and use distributed CPU-based compute for analytical workloads.
Trap categories for this question
Command / output trap
While Google Cloud does offer encryption, this is not demonstrated by the query output.
Detailed technical explanation
How to think about this question
BigQuery's serverless architecture leverages a distributed, multi-tenant compute layer that dynamically allocates slots (virtual CPUs) per query, allowing it to scan terabytes in seconds without any user-managed clusters. Under the hood, it uses a columnar storage format (Capacitor) and a tree-based execution engine (Dremel) to parallelize query processing across thousands of nodes, which is why it can handle petabyte-scale datasets without GPU acceleration. A real-world scenario is a retail company analyzing years of transaction data to identify seasonal trends—BigQuery's pay-per-query model (Option E) makes this cost-effective compared to maintaining an on-premises Hadoop cluster.
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.
- →
Why cloud technology is transforming business — study guide chapter
Learn the concepts, then practise the questions
- →
Why cloud technology is transforming business practice questions
Targeted practice on this topic area only
- →
All GCDL questions
507 questions across all exam domains
- →
Google Cloud Digital Leader study guide
Full concept coverage aligned to exam objectives
- →
GCDL practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related GCDL practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Why cloud technology is transforming business practice questions
Practise GCDL questions linked to Why cloud technology is transforming business.
Fundamental cloud concepts practice questions
Practise GCDL questions linked to Fundamental cloud concepts.
Google Cloud products, services, and solutions practice questions
Practise GCDL questions linked to Google Cloud products, services, and solutions.
Scaling with Google Cloud operations practice questions
Practise GCDL questions linked to Scaling with Google Cloud operations.
Trust and security with Google Cloud practice questions
Practise GCDL questions linked to Trust and security with Google Cloud.
GCDL fundamentals practice questions
Practise GCDL questions linked to GCDL fundamentals.
GCDL scenario practice questions
Practise GCDL questions linked to GCDL scenario.
GCDL troubleshooting practice questions
Practise GCDL questions linked to GCDL troubleshooting.
Practice this exam
Start a free GCDL 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 GCDL question test?
Why cloud technology is transforming business — This question tests Why cloud technology is transforming business — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The ability to analyze petabytes of data without provisioning servers — Option C is correct because Google BigQuery is a serverless data warehouse that automatically scales to handle petabytes of data without requiring users to provision or manage any servers. This eliminates the operational overhead of capacity planning and infrastructure management, directly demonstrating how cloud technology abstracts physical hardware and enables on-demand analytics at massive scale.
What should I do if I get this GCDL 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 GCDL 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 GCDL 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.