Google PCA Practice Question: Managing Implementation and Ensuring Solution and Operations Reliability
You need to create a Cloud Logging sink that exports logs to a BigQuery dataset for long-term analysis. Which destination type should you specify?
⚠ Common exam trap
PCA often tests the distinction between log routing destinations by matching the destination to the use case — candidates incorrectly pick Pub/Sub for 'analysis' when the question implies batch SQL analytics, which is BigQuery's domain.
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
✓
BigQuery
Cloud Logging sinks support three destination types: Cloud Storage, BigQuery, and Pub/Sub (plus custom destinations via Pub/Sub or Logging API). BigQuery is the correct choice here because the requirement is long-term analysis of log data, and BigQuery provides a fully managed, serverless data warehouse with SQL querying, partitioning, and clustering capabilities ideal for analytical workloads on exported logs.
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 Storage
Why it's wrong here
Cloud Storage sinks write log entries as objects, not queryable tables, so BigQuery SQL analysis is impossible. It tempts because Cloud Storage suits cheap archival retention and log replay, but the stem explicitly requires a BigQuery dataset destination for analysis.
- ✓
BigQuery
Why this is correct
Cloud Logging sinks route log entries to supported destinations, and BigQuery is a native sink destination that stores exported logs in datasets for SQL analysis and long-term retention. Specifying BigQuery as the destination type satisfies the requirement to export logs into a BigQuery dataset.
- ✗
Pub/Sub
Why it's wrong here
Pub/Sub delivers log entries as messages to subscribers, requiring extra code to batch and load them into BigQuery. It is tempting for streaming logs to Cloud Functions or Dataflow, but it is not a direct BigQuery sink destination.
- ✗
Custom HTTP endpoint
Why it's wrong here
A custom HTTP endpoint pushes log entries to an arbitrary receiver, which cannot write into a BigQuery dataset. It is tempting when routing logs to third-party SIEMs or bespoke collectors, but BigQuery ingestion requires the dedicated BigQuery sink destination.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Cloud SQL and Managed Data Stores
Key term
Cloud storage
Cloud storage is a service that lets you save data on remote servers accessed over the internet instead of on your computer's hard drive.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
About these practice questions
This PCA question is part of Courseiva's 807-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PCA 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 PCA exam.