Cloud Digital Leader Why cloud technology is transforming business Practice Question
Network Topology
A data analyst runs a query on Google BigQuery. Which TWO statements correctly describe how cloud technology is transforming business in this scenario?
⚠ Common exam trap
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. A common trap is also assuming that 'pay-per-query' is more expensive than on-premises, but it actually reduces costs by eliminating idle capacity and operational overhead.
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 BigQuery is a fully managed, serverless data warehouse, so the analyst can run SQL over petabyte-scale datasets without provisioning or managing any infrastructure. Option E is correct because BigQuery's on-demand pricing charges per bytes processed (or via flat-rate/editions capacity), eliminating the capital and operational costs of building and maintaining an on-premises warehouse. Option A is not necessarily true: BigQuery is an analytical warehouse queried on demand, not a real-time streaming delivery engine, so results are not inherently delivered as data is ingested. Option B is incorrect because BigQuery runs on Google's managed compute infrastructure and does not require the customer to provision dedicated GPU clusters for standard SQL queries. Option D is a true statement about Google Cloud encryption (AES-256 at rest, TLS in transit), but it describes a security property rather than how cloud technology is transforming business in this scenario, so it is not one of the two marked answers.
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
BigQuery executes queries against data already persisted in tables, not in a continuous streaming fashion. When you run a SQL query, it reads the existing stored data (or the current contents of a streaming buffer) and returns the result only after the distributed job completes. Real-time delivery would require a streaming pipeline like Dataflow, not a batch query against historical storage.
- ✗
The query required dedicated GPU clusters
Why it's wrong here
BigQuery's underlying infrastructure is a massively parallel processing (MPP) system built on clusters of commodity CPUs, not GPU accelerators. Query execution is distributed across many compute slots by Dremel/Borg, with data read from columnar storage (Capacitor) over a high-speed network. Dedicated GPU clusters are used for specialized ML workloads, not standard SQL analytics.
- ✓
The ability to analyze petabytes of data without provisioning servers
Why this is correct
BigQuery is a fully serverless data warehouse, so there are no servers to provision, patch, or manage. The query engine automatically allocates and scales compute resources across a massive grid, enabling analyses of petabytes in seconds. This serverless model is the primary technological advantage that lets users focus on SQL logic rather than infrastructure capacity planning.
- ✗
The cloud provider automatically encrypts data at rest and in transit
Why it's wrong here
While Google Cloud does encrypt data at rest (AES-256) and in transit (TLS), this security attribute is invisible in the query output and is not the characteristic being demonstrated by the query. The question is about the operational and analytical benefits of BigQuery, such as scalability and cost, not its security posture. Encryption is a baseline compliance feature, not the reason for being able to analyze petabytes.
- ✓
The pay-per-query model reduces costs compared to maintaining an on-premises data warehouse
Why this is correct
BigQuery's on-demand pricing charges per byte of data scanned (currently $5/TB, with free tier), so you pay only for actual query consumption rather than for pre-provisioned hardware. Maintaining an on-premises warehouse involves capital expenditures, power, cooling, DBA staffing, and overprovisioning for peak load. Pay-per-query shifts the cost model to variable OPEX, which is more economical for spiky or exploratory workloads.
Quick reference
Symmetric Encryption Algorithm Comparison
| Algorithm | Key Size | Block Size | Status | Notes |
|---|---|---|---|---|
| AES-128 | 128-bit | 128-bit | Current standard | NIST approved; WPA3, TLS |
| AES-256 | 256-bit | 128-bit | Current standard | Preferred for sensitive / govt data |
| 3DES | 112-bit effective | 64-bit | Deprecated (2023) | Replaced by AES |
| DES | 56-bit | 64-bit | Broken | Cracked in < 24 h; never deploy |
| ChaCha20 | 256-bit | Stream cipher | Current | TLS 1.3, WireGuard |
Go deeper
Related to this question
Learn chapter
Compute Options on Google Cloud
Key term
Encryption
Encryption is the process of converting readable data into a secret code to prevent unauthorized access.
Key term
Advanced Encryption Standard
Advanced Encryption Standard (AES) is a widely used symmetric encryption algorithm that protects electronic data by converting readable information into a scrambled format that can only be unscrambled with the correct secret key.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.