This PMLE practice question tests your understanding of monitoring ml solutions. 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 scientist wants to log prediction inputs and outputs for model monitoring. Which Google Cloud service is best suited for this?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Cloud Monitoring
Why wrong: Cloud Monitoring is for metrics and alerting, not log storage.
Cloud Storage
Why wrong: Cloud Storage is for object storage, not real-time logging.
Cloud Logging
Cloud Logging can ingest and store prediction logs.
BigQuery
Why wrong: BigQuery is for analytics, not primary log ingestion.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Cloud Logging
Cloud Logging is the best choice because it is designed to ingest, store, and analyze log data, including custom log entries from applications. The data scientist can use the Cloud Logging API to write structured log entries containing prediction inputs and outputs, then query them using Logs Explorer or export them for further analysis. This aligns with the requirement to log prediction inputs and outputs for model monitoring, as Cloud Logging provides a centralized, scalable, and queryable log management service.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Cloud Monitoring
Why it's wrong here
Cloud Monitoring is for metrics and alerting, not log storage.
Cloud Storage
Why it's wrong here
Cloud Storage is for object storage, not real-time logging.
Cloud Logging
Why this is correct
Cloud Logging can ingest and store prediction logs.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
BigQuery
Why it's wrong here
BigQuery is for analytics, not primary log ingestion.
Common exam traps
Google Cloud often tests the distinction between logging (Cloud Logging) and monitoring (Cloud Monitoring), where candidates mistakenly choose Cloud Monitoring because they think 'monitoring' includes logging, but Cloud Monitoring is for metrics and alerts, not for storing and querying log data.
Detailed technical explanation
Cloud Logging uses a structured log entry format based on the LogEntry protobuf, which includes fields like `jsonPayload` for arbitrary JSON data, making it ideal for capturing prediction inputs and outputs as key-value pairs. Under the hood, Cloud Logging integrates with the Cloud Logging Agent (fluentd-based) or the Cloud Logging API, and logs are automatically indexed for fast search using the Logs Query Language. In a real-world scenario, a data scientist might use the `logging` Python client library to emit logs with prediction drift metrics, then set up log-based metrics and alerts in Cloud Monitoring to detect performance degradation.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Cloud Logging — Cloud Logging is the best choice because it is designed to ingest, store, and analyze log data, including custom log entries from applications. The data scientist can use the Cloud Logging API to write structured log entries containing prediction inputs and outputs, then query them using Logs Explorer or export them for further analysis. This aligns with the requirement to log prediction inputs and outputs for model monitoring, as Cloud Logging provides a centralized, scalable, and queryable log management service.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PMLE 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 PMLE exam.
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