- A
Cloud Monitoring and Cloud Logging
Why wrong: These are for monitoring infrastructure, not for data skew detection.
- B
Cloud DLP and Cloud KMS
Why wrong: These are for data security and encryption, not for data skew.
- C
Vertex AI Model Monitoring and Vertex AI Pipelines
Correct: Vertex AI Model Monitoring detects skew, and pipelines orchestrate the process.
- D
BigQuery and Dataflow
Why wrong: BigQuery and Dataflow can be used for data processing but not for built-in skew detection.
Question 118 of 1,000
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. 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 company wants to implement continuous training for their ML model. The pipeline should be triggered when new training data arrives in Cloud Storage, and after training, the model should be automatically deployed to a staging endpoint if evaluation metrics pass a threshold. They also need to detect skew between training data and serving data. Which two services should they use for skew detection?
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
✓
Vertex AI Model Monitoring and Vertex AI Pipelines
Vertex AI Model Monitoring provides built-in skew detection by comparing training data statistics with serving data statistics, alerting when distribution shifts exceed thresholds. Vertex AI Pipelines orchestrates the continuous training workflow, including triggering on new data arrival, model evaluation, and conditional deployment to a staging endpoint, making C the correct pair for both skew detection and pipeline automation.
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 Monitoring and Cloud Logging
Why it's wrong here
These are for monitoring infrastructure, not for data skew detection.
- ✗
Cloud DLP and Cloud KMS
Why it's wrong here
These are for data security and encryption, not for data skew.
- ✓
Vertex AI Model Monitoring and Vertex AI Pipelines
Why this is correct
Correct: Vertex AI Model Monitoring detects skew, and pipelines orchestrate the process.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BigQuery and Dataflow
Why it's wrong here
BigQuery and Dataflow can be used for data processing but not for built-in skew detection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the distinction between general-purpose monitoring/logging services (Cloud Monitoring/Logging) and ML-specific monitoring (Vertex AI Model Monitoring), leading candidates to pick A because they think 'monitoring' means the same thing.
Detailed technical explanation
How to think about this question
Vertex AI Model Monitoring uses a reference distribution (e.g., training data statistics) and compares it to serving data via techniques like Jensen-Shannon divergence or L-infinity distance for categorical features, and z-score or Mahalanobis distance for numerical features. It can be configured to monitor skew on a per-feature basis with custom alert thresholds, and integrates directly with Vertex AI Pipelines to trigger retraining or rollback actions when drift is detected. In a real-world scenario, a financial services model might use this to detect when production data drifts from training data due to seasonal changes, automatically triggering a pipeline to retrain on recent data.
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.
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Automating and Orchestrating ML Pipelines — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Automating and Orchestrating ML Pipelines — This question tests Automating and Orchestrating ML Pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Vertex AI Model Monitoring and Vertex AI Pipelines — Vertex AI Model Monitoring provides built-in skew detection by comparing training data statistics with serving data statistics, alerting when distribution shifts exceed thresholds. Vertex AI Pipelines orchestrates the continuous training workflow, including triggering on new data arrival, model evaluation, and conditional deployment to a staging endpoint, making C the correct pair for both skew detection and pipeline automation.
What should I do if I get this PMLE 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 →
Last reviewed: Jul 4, 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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