- A
Switch to a different base model
Why wrong: Base model may not be the root cause if fine-tuning data is flawed.
- B
Increase the cluster size
Why wrong: Cluster size affects performance, not accuracy.
- C
Use a serverless endpoint
Why wrong: Endpoint type does not fix accuracy issues.
- D
Check the training data for bias or quality issues
Training data quality directly impacts model accuracy.
Question 616 of 991
1Z0-1127 Using OCI Generative AI Service Practice Question
This 1Z0-1127 practice question tests your understanding of using oci generative ai service. 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 fine-tuned a model on OCI Gen AI using a dedicated AI cluster. After deployment, the model gives inaccurate results. Which troubleshooting step should they take first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
✓
Check the training data for bias or quality issues
The most common cause of inaccurate results after fine-tuning is poor training data quality or bias. Before adjusting infrastructure or switching models, you must validate the dataset for issues like label errors, class imbalance, or sampling bias, as these directly degrade model performance regardless of compute resources.
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.
- ✗
Switch to a different base model
Why it's wrong here
Base model may not be the root cause if fine-tuning data is flawed.
- ✗
Increase the cluster size
Why it's wrong here
Cluster size affects performance, not accuracy.
- ✗
Use a serverless endpoint
Why it's wrong here
Endpoint type does not fix accuracy issues.
- ✓
Check the training data for bias or quality issues
Why this is correct
Training data quality directly impacts model accuracy.
Clue confirmation
The clue word "first" 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
The exam often tests the misconception that performance problems are always solved by scaling infrastructure (more compute or serverless endpoints), when in fact the first step in troubleshooting model accuracy must be data validation.
Detailed technical explanation
How to think about this question
Fine-tuning on OCI Gen AI uses a dedicated AI cluster with GPU nodes; the model's final weights are a direct function of the training dataset. If the data contains systematic bias (e.g., underrepresentation of certain classes) or label noise, the model learns incorrect patterns. OCI's fine-tuning service logs training metrics like loss and accuracy per epoch; a plateau or divergence often indicates data issues before any deployment change is considered.
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 practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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 1Z0-1127 question test?
Using OCI Generative AI Service — This question tests Using OCI Generative AI Service — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Check the training data for bias or quality issues — The most common cause of inaccurate results after fine-tuning is poor training data quality or bias. Before adjusting infrastructure or switching models, you must validate the dataset for issues like label errors, class imbalance, or sampling bias, as these directly degrade model performance regardless of compute resources.
What should I do if I get this 1Z0-1127 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: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This 1Z0-1127 practice question is part of Courseiva's free Oracle 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 1Z0-1127 exam.
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