1Z0-1127 Deploying and Managing Generative AI on OCI Practice Question
This 1Z0-1127 practice question tests your understanding of deploying and managing generative ai on oci. 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 data scientist deployed a fine-tuned Llama 2 7B model on OCI Model Deployment with a single VM.GPU.A10.1 shape. Users report average latency of 3 seconds per request, which is too high for the intended real-time application. The model is used for short text generation (max 128 tokens). The data scientist wants to reduce per-request latency without significant accuracy loss. Which action would be most effective?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply 4-bit quantization using AWQ
4-bit quantization using AWQ reduces the model's memory footprint and computational requirements by compressing weights to 4-bit integers, which directly decreases inference latency on the VM.GPU.A10.1 shape. This technique preserves most of the model's accuracy while enabling faster token generation, making it the most effective single action for reducing per-request latency in a real-time short text generation scenario.
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.
✗
Increase the number of workers per replica
Why it's wrong here
More workers on a single GPU can lead to contention and may not reduce per-request latency.
✗
Increase the max_tokens parameter for the model
Why it's wrong here
Increasing max_tokens increases the output length, thereby increasing latency.
✗
Enable response streaming for the model endpoint
Why it's wrong here
Streaming allows partial output but does not reduce the total processing time.
✓
Apply 4-bit quantization using AWQ
Why this is correct
Quantization reduces model size and inference time with minimal accuracy loss.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse throughput improvements (Option A) or perceived latency (Option C) with actual per-request latency reduction, or mistakenly think increasing max_tokens (Option B) would help, when in fact it worsens the problem.
Trap categories for this question
Command / output trap
Increasing max_tokens increases the output length, thereby increasing latency.
Detailed technical explanation
How to think about this question
AWQ (Activation-aware Weight Quantization) works by analyzing activation patterns to identify salient weights that require higher precision, then applying mixed-precision quantization to minimize accuracy loss. On an A10 GPU with 24 GB VRAM, a 7B model in FP16 consumes ~14 GB, leaving limited headroom for KV cache and activations; 4-bit quantization reduces memory to ~3.5 GB, enabling faster memory-bound operations and potentially allowing larger batch sizes. In practice, this can cut latency by 2-4x on short text generation tasks while maintaining over 99% of the original model's perplexity.
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.
Deploying and Managing Generative AI on OCI — This question tests Deploying and Managing Generative AI on OCI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply 4-bit quantization using AWQ — 4-bit quantization using AWQ reduces the model's memory footprint and computational requirements by compressing weights to 4-bit integers, which directly decreases inference latency on the VM.GPU.A10.1 shape. This technique preserves most of the model's accuracy while enabling faster token generation, making it the most effective single action for reducing per-request latency in a real-time short text generation scenario.
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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Question Discussion
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