- A
Install the Cloud Logging agent on the VM running the microservice.
Why wrong: The agent is for VM-based logging, not for containers or serverless; also not automatic for stdout.
- B
Publish logs to a Pub/Sub topic and later store them.
Why wrong: Pub/Sub is a messaging layer, not a log storage service; additional components needed.
- C
Write logs directly to Cloud Storage.
Why wrong: Writing to Cloud Storage would require custom code and does not support Logs Explorer.
- D
Use the Cloud Logging client library (google-cloud-logging) for the microservice's language.
The client library automatically sends structured logs to Cloud Logging, enabling centralized analysis.
Question 521 of 1,000
How to Collect Logs from Microservices Using Cloud Logging Client Library
This PDE practice question tests your understanding of ensuring solution quality. 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 team developed a microservice that writes logs to stdout. They want to centralize logs for analysis. Which GCP service should they use to automatically collect and store logs?
Quick Answer
The answer is to use the Cloud Logging client library (google-cloud-logging) for the microservice's language. This is correct because the client library is designed to automatically capture stdout logs from containerized applications and stream them directly into Cloud Logging, eliminating the need for a separate agent or sidecar. On the Google Professional Data Engineer exam, this question tests your understanding of log collection strategies for modern architectures, specifically distinguishing between agent-based collection for VMs and client library integration for microservices. A common trap is confusing the Cloud Logging agent, which is intended for Compute Engine instances, with the client library used in containers. Remember the key distinction: agents are for VMs, client libraries are for code. A useful memory tip is "code captures containers" — when your microservice writes to stdout, the client library embedded in your code captures that stream natively, making it the simplest and most direct path to centralized logging.
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
✓
Use the Cloud Logging client library (google-cloud-logging) for the microservice's language.
Option D is correct because the Cloud Logging client library (google-cloud-logging) allows the microservice to write logs directly to Cloud Logging via the Cloud Logging API, without needing a separate agent or intermediate storage. This is the recommended approach for applications running in environments like GKE, Cloud Run, or Compute Engine when you want structured, automatically collected logs that are immediately available for analysis in Cloud Logging.
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.
- ✗
Install the Cloud Logging agent on the VM running the microservice.
Why it's wrong here
The agent is for VM-based logging, not for containers or serverless; also not automatic for stdout.
- ✗
Publish logs to a Pub/Sub topic and later store them.
Why it's wrong here
Pub/Sub is a messaging layer, not a log storage service; additional components needed.
- ✗
Write logs directly to Cloud Storage.
Why it's wrong here
Writing to Cloud Storage would require custom code and does not support Logs Explorer.
- ✓
Use the Cloud Logging client library (google-cloud-logging) for the microservice's language.
Why this is correct
The client library automatically sends structured logs to Cloud Logging, enabling centralized analysis.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that you must install an agent (Option A) to collect logs from any application, but the trap here is that modern microservices can use client libraries to send logs directly to Cloud Logging, making agents unnecessary for custom applications.
Detailed technical explanation
How to think about this question
The Cloud Logging client library uses gRPC or HTTP REST to send log entries to the Cloud Logging API, which supports structured logging with severity levels, resource labels, and custom metadata. Under the hood, the library batches log entries and handles retries with exponential backoff, ensuring reliable delivery even under high throughput. In a real-world scenario, a microservice on GKE can use the client library to automatically associate logs with the Kubernetes pod and container, enabling powerful filtering and monitoring without manual configuration.
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.
- →
Ensuring solution quality — study guide chapter
Learn the concepts, then practise the questions
- →
Ensuring solution quality practice questions
Targeted practice on this topic area only
- →
All PDE questions
1,000 questions across all exam domains
- →
Google Professional Data Engineer study guide
Full concept coverage aligned to exam objectives
- →
PDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PDE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Designing Data Processing Systems practice questions
Practise PDE questions linked to Designing Data Processing Systems.
Ingesting and Processing the Data practice questions
Practise PDE questions linked to Ingesting and Processing the Data.
Storing the Data practice questions
Practise PDE questions linked to Storing the Data.
Preparing and Using Data for Analysis practice questions
Practise PDE questions linked to Preparing and Using Data for Analysis.
Maintaining and Automating Data Workloads practice questions
Practise PDE questions linked to Maintaining and Automating Data Workloads.
Building and operationalizing data processing systems practice questions
Practise PDE questions linked to Building and operationalizing data processing systems.
Operationalizing machine learning models practice questions
Practise PDE questions linked to Operationalizing machine learning models.
Ensuring solution quality practice questions
Practise PDE questions linked to Ensuring solution quality.
PDE fundamentals practice questions
Practise PDE questions linked to PDE fundamentals.
PDE scenario practice questions
Practise PDE questions linked to PDE scenario.
PDE troubleshooting practice questions
Practise PDE questions linked to PDE troubleshooting.
Practice this exam
Start a free PDE 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 PDE question test?
Ensuring solution quality — This question tests Ensuring solution quality — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the Cloud Logging client library (google-cloud-logging) for the microservice's language. — Option D is correct because the Cloud Logging client library (google-cloud-logging) allows the microservice to write logs directly to Cloud Logging via the Cloud Logging API, without needing a separate agent or intermediate storage. This is the recommended approach for applications running in environments like GKE, Cloud Run, or Compute Engine when you want structured, automatically collected logs that are immediately available for analysis in Cloud Logging.
What should I do if I get this PDE 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 →
Keep practising
More PDE practice questions
- A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated…
- A company runs a Dataflow streaming pipeline that reads from Cloud Pub/Sub and writes to BigQuery. The pipeline uses a s…
- A company uses Cloud Dataproc for ephemeral clusters to run batch jobs. They want to ensure job reliability and data qua…
- Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from B…
- A company wants to use BigQuery to query data stored in Parquet files in Cloud Storage without loading the data into Big…
- A company has deployed a machine learning model to AI Platform Prediction. The model uses a custom container with a Tens…
Last reviewed: Jul 4, 2026
This PDE 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 PDE 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.