1Z0-1127 Timed Practice Test 1 — 10 Questions

Question 1

A developer wants to deploy a RAG application using OCI Generative AI for both embedding and text generation while minimizing costs. Which strategy is most effective?

Accepted Answer

Cache frequent queries and their embeddings. Caching embeddings for frequent queries eliminates repeated embedding API calls, directly reducing cost.

Answer

Use a larger generation model

Answer

Reduce chunk size to decrease embedding calls

Answer

Use a larger embedding model for better accuracy

Question 2

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?

Accepted Answer

Check the training data for bias or quality issues. Option B is correct because inaccurate results often stem from training data issues such as bias, quality, or insufficient diversity. Other options may be considered later but data quality is the primary suspect.

Answer

Switch to a different base model

Answer

Increase the cluster size

Answer

Use a serverless endpoint

Question 3

Users report that inference requests to the OCI Generative AI service are taking longer than expected. The application uses the on-demand endpoint. What is the most likely cause of the increased latency?

Accepted Answer

The on-demand endpoint experiences shared resource contention.. On-demand endpoints share resources; during peak usage, resource contention increases latency. Dedicated AI clusters provide predictable performance.

Answer

The inference model is not fine-tuned for the use case.

Answer

The selected model is too large for the use case.

Answer

The API request timeout is set too low.

Question 4

Refer to the exhibit. A developer runs the command and receives the error. What is the issue?

Accepted Answer

The max-tokens value exceeds the allowed range.. The max-tokens parameter is set to 600, which exceeds the allowed range of 1 to 500.

Answer

The message is too short.

Answer

The chat-id is invalid.

Answer

The endpoint is incorrect.

Question 5

A developer wants to integrate OCI GenAI into a Java application. Which SDK should they use?

Accepted Answer

OCI Java SDK.. Option B is correct because the OCI Java SDK provides native Java support for calling OCI services including GenAI.

Answer

OCI JavaScript SDK.

Answer

OCI Python SDK.

Answer

OCI CLI.

Question 6

Which TWO factors most significantly influence the computational cost of fine-tuning a large language model?

Accepted Answer

Number of model parameters. Model size (parameters) directly determines FLOPs and memory. Training sequence length affects memory and compute per step. Option C is wrong because batch size affects throughput but not fundamental cost per token. Option D is wrong because quantization usually reduces cost. Option E is wrong because dataset size affects total steps but per-step cost is dominated by model size and sequence length.

Answer

Batch size

Answer

Quantization bits

Answer

Dataset size

Question 7

An organization wants to use an LLM to summarize legal documents. Which consideration is most important for ensuring accurate summaries?

Accepted Answer

Fine-tune the model on a curated legal corpus. Legal documents require precise understanding, so fine-tuning on legal data is critical. Option B is wrong because larger models don't guarantee domain accuracy. Option C is wrong because pre-training from scratch is expensive and unnecessary. Option D is wrong because zero-shot may miss legal nuances.

Answer

Use the largest available general-purpose model

Answer

Rely on zero-shot summarization with careful prompting

Answer

Pre-train a new model from scratch on legal texts

Question 8

A healthcare startup is building an AI assistant to help doctors draft clinical notes from patient-physician conversations. They have a large language model that is fine-tuned on medical data. During testing, they notice the model occasionally generates plausible-sounding but incorrect medical recommendations. The startup wants to deploy the assistant to assist doctors, not replace them. They have the following options: (A) Deploy the model as-is and rely on doctors to catch errors, (B) Add a disclaimer that the model may make mistakes, (C) Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base before presenting to doctors, (D) Reduce the model's temperature to 0 to ensure deterministic outputs. Which option best balances safety and utility?

Accepted Answer

Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base.. Option C is correct because it directly addresses the factual accuracy issue by validating outputs. Option A is wrong because relying on doctors to catch all errors is unsafe and burdensome. Option B is wrong because a disclaimer does not prevent harm. Option D is wrong because deterministic outputs do not guarantee correctness; the model can still be confidently wrong.

Answer

Add a disclaimer that the model may make mistakes.

Answer

Deploy the model as-is and rely on doctors to catch errors.

Answer

Reduce the model's temperature to 0 to ensure deterministic outputs.

Question 9

A team is fine-tuning a large language model for a domain-specific Q&A application. After fine-tuning, they observe that the model performs well on the training distribution but struggles with out-of-distribution (OOD) questions. Which approach would best improve OOD robustness?

Accepted Answer

Include a diverse set of examples from related domains in the fine-tuning dataset.. Option C is correct because incorporating diverse data during fine-tuning helps the model generalize to OOD inputs. Option A is wrong because increasing learning rate may cause catastrophic forgetting. Option B is wrong because reducing model size reduces capacity. Option D is wrong because early stopping on training loss may not help OOD.

Answer

Use early stopping based on training loss to avoid overfitting.

Answer

Reduce the model size to prevent overfitting to the training data.

Answer

Increase the learning rate during fine-tuning to adapt faster to new patterns.

Question 10

Which TWO measures can help reduce the risk of generating toxic or unsafe content when using OCI Generative AI Service?

Accepted Answer

Use few-shot prompting with examples that demonstrate safe and appropriate responses.. Few-shot prompting provides the model with explicit examples of safe, appropriate responses, which helps steer the model's behavior toward desired outputs and reduces the likelihood of generating toxic or unsafe content. This technique leverages in-context learning to align the model's responses with the provided examples, making it a practical measure for content safety.

Answer

Disable model monitoring and logging to reduce overhead.

Answer

Increase the temperature parameter to make output more deterministic.

Answer

Fine-tune the model on a large dataset without any safety filtering.