- A
Cloud DLP
Why wrong: Cloud DLP is used for inspecting and classifying sensitive data.
- B
AI Platform Continuous Evaluation
This service provides monitoring for model predictions and drift analysis.
- C
Cloud Monitoring
Why wrong: Cloud Monitoring tracks system metrics, not model-specific drift.
- D
Cloud Audit Logs
Why wrong: Audit logs record API calls, not model quality metrics.
Question 834 of 1,000
Detect Data Drift in Deployed Models Using AI Platform Continuous Evaluation
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 is deploying a model on AI Platform Prediction. They want to monitor for data drift to maintain model quality. Which service should they use?
Quick Answer
The answer is AI Platform Continuous Evaluation, the correct service for monitoring model data drift in deployed models. This tool is specifically designed to analyze prediction requests against training data distributions, automatically detecting when input data shifts over time and alerting teams to potential degradation in model quality. On the Google Professional Data Engineer exam, this question tests your understanding of MLOps monitoring services, often appearing as a distractor where Cloud Monitoring (infrastructure metrics) or Cloud Audit Logs (API activity) are incorrectly chosen. The common trap is confusing general observability tools with model-specific monitoring; remember that Continuous Evaluation is purpose-built for model performance and drift, not system health. A useful memory tip is to associate “Continuous” with “model lifecycle” and “Evaluation” with “data quality checks,” distinguishing it from Cloud Monitoring’s focus on CPU or latency metrics.
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
✓
AI Platform Continuous Evaluation
AI Platform Continuous Evaluation (CE) is the correct service because it is specifically designed to monitor deployed models for data drift and feature skew. It automatically compares the distribution of incoming prediction requests against the training data distribution, alerting when statistically significant drift is detected, which directly addresses the need to maintain model quality over time.
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.
- ✗
Cloud DLP
Why it's wrong here
Cloud DLP is used for inspecting and classifying sensitive data.
- ✓
AI Platform Continuous Evaluation
Why this is correct
This service provides monitoring for model predictions and drift analysis.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Monitoring
Why it's wrong here
Cloud Monitoring tracks system metrics, not model-specific drift.
- ✗
Cloud Audit Logs
Why it's wrong here
Audit logs record API calls, not model quality metrics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that general-purpose monitoring or logging services (like Cloud Monitoring or Audit Logs) are sufficient for ML-specific drift detection, when in fact only a dedicated ML evaluation service like AI Platform Continuous Evaluation provides the necessary statistical comparison against training data.
Detailed technical explanation
How to think about this question
AI Platform Continuous Evaluation uses a sliding window approach to compute feature distributions from recent predictions and compares them to the training baseline using statistical tests like the Kolmogorov-Smirnov test for numerical features and chi-squared test for categorical features. It can also detect prediction drift by comparing the distribution of model outputs over time. In a real-world scenario, a model trained on historical e-commerce data might start receiving queries for new product categories, causing categorical feature drift that CE would flag before accuracy degrades.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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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: AI Platform Continuous Evaluation — AI Platform Continuous Evaluation (CE) is the correct service because it is specifically designed to monitor deployed models for data drift and feature skew. It automatically compares the distribution of incoming prediction requests against the training data distribution, alerting when statistically significant drift is detected, which directly addresses the need to maintain model quality over time.
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
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Last reviewed: Jul 4, 2026
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