The answer is that model2 uses a different model artifact (fraud_detection_v2) that inherently produces different predictions. This is correct because in Vertex AI, a traffic split routes the same live input data to both model versions, but each model artifact processes that data independently through its own trained logic. If model2’s predictions shift significantly and the fraud detection rate rises, the root cause is the artifact itself—not data drift, routing errors, or environment differences. On the Google Professional Data Engineer exam, this question tests your understanding that a traffic split isolates model behavior, so prediction differences directly reflect artifact changes, not infrastructure issues. A common trap is to blame data skew or endpoint configuration, but the key is that both models receive identical requests. Memory tip: “Same data, different artifact = different output.”
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 deploys a new version of a fraud detection model (model2) alongside the existing model (model1) on the same Vertex AI endpoint with a 70/30 traffic split. After 24 hours, the team notices that model2's predictions are significantly different from model1's, and the fraud detection rate has increased. What is the most likely explanation for the change in predictions?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Model2 uses a different model artifact (fraud_detection_v2) that produces different predictions.
Option D is correct because the most straightforward explanation for a significant change in predictions and an increased fraud detection rate is that model2 uses a different model artifact (fraud_detection_v2) that was designed to produce different outputs. In Vertex AI, deploying a new model version with a traffic split means both models receive the same input data, but each model artifact independently processes it. If model2's predictions differ substantially, it indicates the model artifact itself has been updated or replaced, not that there is a data or routing issue.
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.
✗
Model2 was trained on data that leaked future information, causing unrealistic results.
Why it's wrong here
No evidence of data leakage; the difference is due to model version.
✗
Model2 is receiving corrupted input data due to a bug in the traffic routing.
Why it's wrong here
The traffic split is functioning as configured; no routing bug.
✗
The traffic split is misconfigured and sending all traffic to model2.
Why it's wrong here
The exhibit shows a 70/30 split, so model1 still receives majority traffic.
✓
Model2 uses a different model artifact (fraud_detection_v2) that produces different predictions.
Why this is correct
The environment variable MODEL_NAME points to different model versions, causing output differences.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that a traffic split or routing issue can cause prediction differences, when in fact the split only controls which model receives the request, not the content of the request or the model's internal logic.
Trap categories for this question
Command / output trap
The exhibit shows a 70/30 split, so model1 still receives majority traffic.
Detailed technical explanation
How to think about this question
In Vertex AI, when you deploy multiple model versions to the same endpoint, each version is a separate container with its own model artifact and serving binaries. The traffic split is implemented at the load balancer level using weighted random selection per request, not by modifying the request payload. This means that even with a 70/30 split, both models receive identical input data; any divergence in predictions must stem from differences in the model artifacts themselves, such as different training data, hyperparameters, or architecture. A real-world scenario is A/B testing a new model version where the new artifact intentionally uses a different decision threshold to increase recall, which would directly explain a higher fraud detection rate.
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.
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Model2 uses a different model artifact (fraud_detection_v2) that produces different predictions. — Option D is correct because the most straightforward explanation for a significant change in predictions and an increased fraud detection rate is that model2 uses a different model artifact (fraud_detection_v2) that was designed to produce different outputs. In Vertex AI, deploying a new model version with a traffic split means both models receive the same input data, but each model artifact independently processes it. If model2's predictions differ substantially, it indicates the model artifact itself has been updated or replaced, not that there is a data or routing issue.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist has iterated on a model and produced a new version. The organization requires the ability to roll back to the previous version quickly if the new version performs poorly in production. Which approach should be used?
easy
A.Store each model version in a separate Cloud Storage bucket.
B.Keep the previous model in a container image and redeploy via Cloud Run.
C.Use Cloud Source Repositories to tag model versions.
✓ D.Upload both versions to Vertex AI Model Registry and use endpoint traffic splitting to route 100% to the safe version if needed.
Why D: Vertex AI Model Registry allows you to deploy multiple model versions and use endpoint traffic splitting to gradually shift traffic or instantly route 100% to a specific version. This enables immediate rollback by setting the traffic split to 100% for the previous model version without redeploying or changing infrastructure.
Variation 2. A data scientist wants to test a new model version on a small percentage of traffic before full rollout. Which Vertex AI feature allows this?
easy
A.A/B testing
✓ B.Endpoint traffic splitting
C.Model monitoring
D.Model versioning with canary deployments
Why B: Vertex AI Endpoint traffic splitting allows you to route a specified percentage of inference requests to different model versions deployed on the same endpoint. This enables gradual rollout by directing a small fraction of traffic (e.g., 5%) to the new model while the rest goes to the current version, without needing separate endpoints or manual routing logic.
Last reviewed: Jun 30, 2026
Question Discussion
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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.
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