1Z0-1127-25 Practice Test 46 — 15 Questions

Question 1

A user wants to quickly test different prompts and parameters (temperature, max tokens) with various OCI Generative AI models without writing any code. Which tool should they use?

Accepted Answer

The OCI Generative AI Playground. The OCI Generative AI Playground is a no-code web interface that lets users experiment with different models, prompts, and parameters such as temperature and max tokens. It is designed for quick testing and comparison without writing any code. The SDK, CLI, and Cloud Shell all require coding or command-line interaction.

Answer

The InferenceClient SDK in Python

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OCI CLI with generative-ai commands

Answer

OCI Cloud Shell

Question 2

A prompt engineer is iteratively refining a prompt for a product review summarizer. They want to test which prompt version yields the most accurate summaries. Which approach should they use?

Accepted Answer

Conduct an A/B test with a diverse set of inputs and compare outputs against established evaluation criteria. A/B testing with a diverse input set and established evaluation criteria is the only method that provides statistically meaningful, reproducible comparison between prompt versions. It controls for input variability and uses objective metrics (e.g., ROUGE, BLEU, human ratings, or task-specific rubrics) to determine which prompt performs better. This is the standard prompt engineering evaluation methodology.

Answer

Increase temperature to get varied outputs and choose the most common one

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Use a different model for each prompt version

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Run both prompts on a single example and pick the better-looking output

Question 3

A prompt engineer wants the model to adopt a formal and authoritative tone when generating financial reports. Which approach is MOST effective?

Accepted Answer

Set the system prompt to 'You are a financial expert. Always respond with a formal, authoritative tone.'. The system prompt is the authoritative mechanism for setting the model's persona, tone, and behavioral constraints across all turns of a conversation. Instructing the model in the system prompt to adopt a formal, authoritative tone as a financial expert establishes a persistent behavioral frame that applies to every response. This is more reliable than per-message instructions.

Answer

Set the temperature to 0 to ensure deterministic output

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Use few-shot examples of formal responses in the user message

Answer

Include the instruction 'Be formal' in the user message

Question 4

Which TWO actions should be taken to monitor model drift in a deployed generative AI model? (Select TWO)

Accepted Answer

Compare inference statistics over time. Option A (Compare inference statistics over time) is correct because monitoring drift requires tracking changes in the statistical distribution of inputs and outputs (e.g., feature distributions, prompt/response patterns) across time windows, which is the core mechanism for detecting data or concept drift in a deployed generative AI model. Option D (Set up alerts on accuracy metrics) is correct because drift is only actionable when it is surfaced through automated alerting on relevant quality/accuracy metrics (e.g., evaluation scores, hallucination or groundedness rates), enabling timely remediation before degradation impacts users. Option B (Retrain the model weekly) is not a monitoring action; retraining is a remediation step and a fixed weekly cadence does not detect drift. Option C (Use OCI Data Labeling for new data) supports data preparation and evaluation but is not itself a drift monitoring mechanism. Option E (Deploy multiple model versions) is a deployment strategy (e.g., canary or A/B) and does not by itself monitor drift.

Answer

Retrain the model weekly

Answer

Use OCI Data Labeling for new data

Answer

Deploy multiple model versions

Question 5

During inference with OCI Generative AI, you notice that the model is generating repetitive phrases. Which combination of parameters can help reduce repetition?

Accepted Answer

Top_p = 0.9, frequency_penalty = 0.5. A high Top_p value (0.9) allows the model to consider a diverse set of tokens, reducing the chance of getting stuck in repetitive loops, while a positive frequency_penalty (0.5) actively penalizes tokens that have already been generated, discouraging the model from repeating the same phrases. Together, these parameters balance creativity and repetition suppression.

Answer

Top_p = 0.1, frequency_penalty = 0.5

Answer

Top_p = 0.9, frequency_penalty = 0.0

Answer

Top_p = 1.0, frequency_penalty = 0.0

Question 6

A company wants to use OCI Generative AI to summarize customer reviews. Which model parameter should be adjusted to control the creativity of the summary?

Accepted Answer

Temperature. Temperature controls the randomness of token selection in the model's output distribution. A higher temperature (e.g., 0.9) makes the summary more creative and diverse, while a lower temperature (e.g., 0.1) makes it more deterministic and focused. For summarizing customer reviews, adjusting temperature directly influences how novel or conservative the generated text will be.

Answer

Frequency penalty

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Top-k

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Presence penalty

Question 7

A retail company uses OCI Generative AI to generate product descriptions. They observe the model occasionally produces biased content. Which technique should be applied to reduce bias in model outputs?

Accepted Answer

Apply prompt engineering with explicit instructions to avoid bias.. Prompt engineering allows you to explicitly instruct the model to avoid biased content, such as by including directives like 'Ensure the description is neutral and unbiased' in the system or user prompt. This technique directly influences the model's output generation without altering its underlying parameters, making it a targeted and effective approach for reducing bias in OCI Generative AI models.

Answer

Increase the max_tokens parameter.

Answer

Reduce the model's inference temperature to 0.

Answer

Use a different random seed for each request.

Question 8

A developer is creating a fine-tuning job for a Cohere Command R model using OCI Generative AI. Which TWO of the following are required when submitting the fine-tuning job?

Accepted Answer

Base model name (e.g., Cohere Command R). Option A is correct because a fine-tuning job must specify which base model is being customized — for example, Cohere Command R — so OCI Generative AI knows the starting weights and architecture to adapt. Option D is correct because fine-tuning requires a training dataset in JSONL format containing prompt/completion pairs, which the service uses as supervised examples to update the model. Option B is not required: a Dedicated AI Cluster ID is needed for hosting a fine-tuned model on dedicated infrastructure, not for submitting the fine-tuning job itself. Option C is not required: a pre-trained embedding model is unrelated to fine-tuning a generative Command R model. Option E is not required: hyperparameters can be supplied optionally, but they are not mandatory and are not required to be in a YAML file.

Answer

A Dedicated AI Cluster ID

Answer

A pre-trained embedding model

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Hyperparameters file in YAML format

Question 9

A financial firm deploys a RAG application using OCI OpenSearch. They observe that the LLM sometimes generates incorrect answers that are not supported by the retrieved documents. Which technique directly addresses this issue?

Accepted Answer

Implement a post-generation verification step that checks if the answer is grounded in the retrieved chunks.. It directly addresses the problem of hallucination by verifying that the LLM's output is factually supported by the retrieved documents. In a RAG pipeline, the LLM may still generate unsupported content even with good retrieval; a post-generation grounding check explicitly validates each claim against the source chunks, ensuring answer fidelity.

Answer

Use a more detailed system prompt instructing the model to not make up information.

Answer

Increase the temperature parameter of the LLM to reduce creativity.

Answer

Increase the number of retrieved documents to provide more context.

Question 10

A prompt engineer wants to reduce the model's tendency to generate overly creative or off-topic responses when summarizing financial reports. Which parameter adjustment is MOST appropriate?

Accepted Answer

Decrease temperature to 0.2. Temperature controls the randomness of token sampling: lower values sharpen the probability distribution toward the most likely tokens, producing more deterministic, focused, and on-topic outputs. For financial summarization, where factual fidelity and consistency matter more than creativity, setting temperature to 0.2 is the standard, most appropriate adjustment.

Answer

Increase top-k to 100

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Decrease frequency penalty to 0

Answer

Increase temperature to 1.0

Question 11

An enterprise is deploying a chat application using a large language model. Users report that the model sometimes generates toxic or biased responses. Which best practice should be applied to mitigate this issue?

Accepted Answer

Implement a content filtering layer using a safety classifier to detect and block toxic outputs.. Implementing a content filtering layer using a safety classifier is a proven best practice to detect and block toxic or biased outputs in real-time. This approach acts as a guardrail, intercepting harmful responses before they reach users, and is independent of the model's internal parameters or training data.

Answer

Use few-shot prompting with examples of toxic responses so the model learns to avoid them.

Answer

Increase the max_tokens parameter to allow the model more context to correct itself.

Answer

Disable the temperature parameter to make outputs deterministic.

Question 12

A prompt engineer notices that the model's output frequently repeats the same phrases. Which parameter adjustment is MOST likely to reduce this repetition?

Accepted Answer

Increase the frequency penalty. Repetition in LLM output is typically caused by the model assigning high probability to tokens it has already generated. The frequency penalty parameter directly penalizes tokens based on how many times they have already appeared in the output, which discourages the model from reusing the same phrases and reduces loops.

Answer

Increase max tokens

Answer

Decrease top-p to 0.5

Answer

Increase temperature to 0.9

Question 13

An organization is implementing a RAG system using OCI GenAI. Which two are best practices for optimizing retrieval and generation? (Choose two.)

Accepted Answer

Use semantic search (embeddings) for document retrieval. Option C is correct because semantic search using embeddings is the core retrieval mechanism in a RAG pipeline: documents and the user query are converted into vector representations, and similarity search (e.g., cosine similarity or dot product over a vector index) retrieves the most contextually relevant chunks to feed the LLM, which dramatically improves answer grounding compared to keyword-only matching. Option D is correct because caching responses for frequently asked questions reduces redundant embedding lookups, vector searches, and LLM inference calls, lowering latency and cost while maintaining consistent answers for repeated queries. Option A is incorrect because the embedding model is used only for retrieval and indexing, while generation is performed by a separate large language model; they are distinct model types and need not match. Option B is incorrect because dumping all documents into a single large index degrades retrieval precision and scalability; best practice is chunking documents and using partitioned or filtered indexes. Option E is incorrect because summarization (or context compression) is a useful technique to fit relevant content within the model's context window and improve generation quality, so disabling it is not a best practice.

Answer

Use the same embedding model for both retrieval and generation

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Store all documents in a single large index

Answer

Disable summarization to save inference costs

Question 14

Which OCI Generative AI model is designed to rerank and improve the relevance of documents retrieved by a search system?

Accepted Answer

Cohere Rerank. Cohere Rerank is specifically designed to take a set of retrieved documents and reorder them by relevance to a query, improving the quality of search and RAG results. It uses a cross-encoder approach to score query-document pairs more accurately than embedding similarity alone. The other models serve different purposes: embeddings, text generation, or general chat.

Answer

Cohere Embed

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Cohere Command R

Answer

Meta Llama 3

Question 15

A team is deploying a RAG system that uses OCI Generative AI to answer questions about internal HR policies. The system must comply with data residency requirements: all data processing must stay within a specific OCI region. The team uses OCI Data Science for orchestration. Which architecture BEST meets the data residency requirement?

Accepted Answer

Deploy the generative AI model endpoints within the same OCI region as the data and compute.. Deploying the generative AI model endpoints within the same OCI region as the data and compute ensures that all data processing—including inference, embedding generation, and vector search—occurs entirely within the required region, satisfying data residency requirements. OCI Generative AI endpoints are region-specific and do not automatically route requests to other regions, so co-locating all components avoids any cross-region data transfer.

Answer

Use OCI Generative AI endpoints in a different region but store data in the required region.

Answer

Use an external third-party LLM endpoint that guarantees data residency.

Answer

Store embeddings in a different region but run inference in the required region.