- A
Inference endpoint location.
Why wrong: Endpoint location affects latency but is not a model selection criterion.
- B
Number of parameters.
More parameters generally mean better performance but higher cost.
- C
Model training data cut-off date.
Why wrong: Cut-off date is relevant for recency but not a primary factor for summarization tasks.
- D
Context window size.
The context window limits the length of input text that can be summarized in one pass.
- E
Supported languages.
The model must support the language of the text to be summarized.
Question 238 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. 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.
Which THREE factors should be considered when choosing a model for a summarization task using OCI Generative AI?
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
✓
Number of parameters.
The number of parameters (B) is a key factor because it directly correlates with the model's capacity to learn complex patterns and generate coherent summaries. Larger parameter models generally produce higher-quality summaries but require more resources. The context window size (D) determines how much text the model can consider at once, which is critical for summarizing long documents. Supported languages (E) ensure the model can handle the input language appropriately. These three factors are directly relevant to summarization task performance in OCI Generative AI.
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.
- ✗
Inference endpoint location.
Why it's wrong here
Endpoint location affects latency but is not a model selection criterion.
- ✓
Number of parameters.
Why this is correct
More parameters generally mean better performance but higher cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Model training data cut-off date.
Why it's wrong here
Cut-off date is relevant for recency but not a primary factor for summarization tasks.
- ✓
Context window size.
Why this is correct
The context window limits the length of input text that can be summarized in one pass.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Supported languages.
Why this is correct
The model must support the language of the text to be summarized.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Common misconceptions include thinking that inference endpoint location (A) is a model selection factor, when it is an infrastructure choice, and that training data cut-off date (C) is relevant for summarization, but it only matters for tasks requiring current factual accuracy.
Detailed technical explanation
How to think about this question
Context window size (D) determines the maximum input length the model can process in a single inference; for summarization, a larger context window (e.g., 4,096 vs. 8,192 tokens) allows the model to handle longer documents without truncation, directly impacting summary completeness. Supported languages (E) are critical because OCI Generative AI models are primarily optimized for English; using a model on non-supported languages will produce garbled or meaningless summaries, as the tokenizer and training data are language-specific.
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.
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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: Number of parameters. — The number of parameters (B) is a key factor because it directly correlates with the model's capacity to learn complex patterns and generate coherent summaries. Larger parameter models generally produce higher-quality summaries but require more resources. The context window size (D) determines how much text the model can consider at once, which is critical for summarizing long documents. Supported languages (E) ensure the model can handle the input language appropriately. These three factors are directly relevant to summarization task performance in OCI Generative AI.
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.
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
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