1Z0-1127-25 Practice Test 24 — 15 Questions

Question 1

A developer is using chain-of-thought prompting to solve a multi-step math problem. The model produces an incorrect final answer, but the intermediate reasoning steps appear logical. Which technique should be applied to improve accuracy?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use self-consistency by generating multiple reasoning chains and picking the majority answer. Self-consistency is the technique designed for exactly this failure mode: when individual chain-of-thought traces look plausible but lead to wrong answers, generating multiple independent reasoning chains and taking the majority-vote answer significantly improves accuracy. It works because correct reasoning paths tend to converge on the same answer, while erroneous paths diverge. This is a well-established inference-time improvement over single-chain CoT.

Answer

Reduce the max_tokens parameter so the model does not over-reason

Answer

Switch to zero-shot prompting to avoid reasoning errors

Answer

Increase the temperature to 1.5 to encourage more diverse reasoning

Question 2

A company uses OCI Generative AI service to power a chatbot. After deployment, the chatbot starts generating inappropriate responses. Which action should be taken first?

Accepted Answer

Adjust the prompt template to include safety instructions.. Adjusting the prompt template to include safety instructions is the fastest and most direct way to mitigate inappropriate responses without retraining or changing model parameters. In OCI Generative AI, prompt engineering—including explicit safety guidelines—can immediately constrain the model's output behavior by providing clear guardrails in the context window.

Answer

Increase the temperature parameter.

Answer

Fine-tune the model on customer-specific data.

Answer

Switch to a larger model.

Question 3

A team is implementing a RAG pipeline in OCI. They have a large collection of PDF documents. After chunking and embedding the documents, retrieval quality is poor. Which step is MOST likely the root cause?

Accepted Answer

The chunk size is too large, causing each chunk to contain multiple topics. In a RAG pipeline, chunk size directly affects retrieval precision. If chunks are too large, each chunk contains multiple topics, so the embedding becomes a diluted average of several concepts. A query about one topic will match the chunk only weakly, and the retrieved context may include irrelevant information, degrading answer quality. Smaller, semantically coherent chunks (e.g., 200–500 tokens with overlap) typically improve retrieval.

Answer

The retrieval step uses greedy decoding

Answer

The embedding model is a generation model, not an embedding model

Answer

Cosine similarity is not appropriate for comparing embeddings

Question 4

Which OCI Generative AI service model family supports fine-tuning with custom datasets?

Accepted Answer

Cohere Command. Cohere Command is the model family within OCI Generative AI that supports fine-tuning with custom datasets, allowing users to adapt the model for domain-specific tasks like summarization or classification. In contrast, Cohere Embed is designed for generating text embeddings, Cohere Summarize is a specialized endpoint for summarization without fine-tuning support, and GPT-3 is not natively available in OCI Generative AI for fine-tuning.

Answer

Cohere Embed

Answer

Cohere Summarize

Answer

GPT-3

Question 5

An organization needs to deploy a custom fine-tuned model for real-time inference with consistent low latency, and they must keep the model isolated from other tenants. Which deployment option should they choose?

Accepted Answer

Provision a dedicated AI cluster with model units. A dedicated AI cluster with model units provides isolated, dedicated compute for the fine-tuned model, ensuring consistent low latency and tenant isolation — exactly the two requirements stated. Dedicated clusters prevent noisy-neighbor effects and give predictable performance for real-time inference. This is the OCI Generative AI deployment option designed for custom fine-tuned models requiring isolation.

Answer

Use the shared infrastructure endpoint with an on-demand serving

Answer

Deploy the model on OCI Data Science using a custom container

Answer

Use the OCI Generative AI Agents service

Question 6

An organization maintains a library of prompt templates for various use cases. Which three practices are essential for effective prompt management? (Choose three.)

Accepted Answer

Store prompt templates in a shared, accessible repository. Option B is correct because storing prompt templates in a shared, accessible repository centralizes them so teams can reuse, review, and update prompts consistently instead of duplicating them in silos. Option C is correct because defining evaluation criteria gives objective, repeatable metrics (such as accuracy, relevance, or task success rate) to judge whether a prompt actually performs well. Option E is correct because versioning each prompt template and tracking changes provides an audit trail, enables rollback to a known-good prompt, and supports reproducibility across model or data updates. Option A is not essential to prompt management itself, since temperature and top-p are inference-time generation parameters that may be tuned per use case but are not required for managing the template library. Option D is also not essential, because A/B testing every single prompt change is impractical and unnecessary; testing should be risk-based rather than mandatory for all edits.

Answer

Adjust temperature and top-p for each prompt variant

Answer

Conduct A/B testing on every prompt change

Question 7

A company is deploying a generative AI model on OCI for an internal application that must comply with strict security policies. The model will be accessed by a limited group of users. Which three actions should the administrator take to ensure security? (Choose three.)

Accepted Answer

Deploy the model in a private VCN subnet. Option B is correct because deploying the model in a private VCN subnet keeps the endpoint off the public internet, so only resources with routing and security-list/NSG access within the VCN can reach it, which aligns with strict security policies for a limited user group. Option C is correct because OCI IAM policies define who can invoke or manage the model endpoint, letting the administrator grant access only to the specific users or groups authorized for the internal application. Option E is correct because storing model authentication keys in OCI Vault centralizes secret management with encryption, access policies, and auditability, preventing hardcoded or exposed credentials. Option A is incorrect because exposing the endpoint to the internet contradicts the strict security requirement and unnecessarily broadens the attack surface. Option D is incorrect because disabling audit logging removes the accountability and monitoring trail needed for compliance and incident investigation.

Answer

Expose the model endpoint to the internet for ease of access

Answer

Disable audit logging to minimize storage costs

Question 8

A company is building a code generation assistant. They want the model to output only valid JSON and to avoid any natural language explanations. Which TWO settings should be applied?

Accepted Answer

Add a system prompt: "You are a JSON-only code generator. Output only JSON, no explanation.". Option B is correct because a system prompt that explicitly instructs the model to act as a JSON-only code generator and to output only JSON with no explanation directly constrains the model's behavior at the highest-priority instruction level, making it the primary mechanism for enforcing format compliance. Option D is correct because reinforcing the same constraint in the user message—'Return only the JSON object without any additional text'—provides an explicit, task-level instruction that further reduces the likelihood of the model appending prose, and combining system and user constraints is a standard prompt-engineering practice for strict output formatting. Option A is not correct because max_tokens only caps the response length; a short response can still contain natural language or malformed JSON, so it does not guarantee valid JSON output. Option C is not correct because temperature 0.5 controls randomness and creativity, not output format, and a mid-range value could actually increase variability rather than enforce strict JSON. Option E is not correct because a stop sequence like '
' merely halts generation at a newline and does not ensure the emitted content is valid JSON or free of explanatory text.

Answer

Set max_tokens to a low value to force brevity

Answer

Set temperature to 0.5 for balanced creativity

Answer

Use a stop sequence like '
' to prevent extra lines

Question 9

Which THREE factors directly influence the quality of responses in a RAG system? (Choose three.)

Accepted Answer

The prompt template used to ask the LLM. Option A is correct because the prompt template directly shapes how retrieved context and the user query are presented to the LLM, and a well-structured template with clear instructions and grounding constraints materially improves answer relevance and faithfulness. Option B is correct because chunk size during document processing determines the granularity of retrieved passages: chunks that are too small lose context, while chunks that are too large dilute the embedding and introduce irrelevant text, both degrading retrieval precision and the resulting answer quality. Option E is correct because the embedding model determines the semantic vector space used for indexing and retrieval; a stronger embedding model yields better semantic similarity matching between queries and documents, directly improving which context reaches the LLM. Option C is not among the marked correct answers: while temperature affects output randomness and style, it is a generation sampling parameter rather than a factor that directly determines the quality of retrieval-augmented responses. Option D is not marked correct because the number of GPUs affects throughput, latency, and cost of inference, not the intrinsic quality of the RAG response.

Answer

The temperature parameter of the LLM

Answer

The number of GPUs allocated to the LLM

Question 10

A company uses LangChain with OCI Generative AI. They notice that their agent-based application occasionally exceeds the rate limits of the OCI Generative AI service, causing errors. Which strategy is MOST effective for handling rate limits in a production LangChain application?

Accepted Answer

Implement a retry mechanism with exponential backoff when calling the model. Exponential backoff with retry is the canonical, production-grade pattern for handling HTTP 429/rate-limit responses from OCI Generative AI. When the service throttles a request, the client waits an increasing interval (e.g., 1s, 2s, 4s, 8s with jitter) before retrying, giving the token bucket time to refill and preventing a thundering-herd effect. LangChain exposes this via the `max_retries` parameter on ChatOCIGenAI/OCIGenAI LLM wrappers, which internally uses exponential backoff. This directly addresses the transient nature of rate-limit errors without degrading retrieval quality or model capability.

Answer

Increase the k value in the retriever to reduce the number of API calls

Answer

Switch to a smaller model to reduce token consumption

Answer

Reduce the chunk_size parameter in text splitters

Question 11

A team wants to deploy a LangChain agent that can perform mathematical calculations, look up current weather, and search the web. Which tools should they include in the agent's toolkit?

Accepted Answer

Calculator tool, weather API tool, and web search tool. An agent needs a distinct tool for each capability it must perform: a Calculator tool for math, a weather API tool for current conditions, and a web search tool for open-domain queries. Each tool exposes a clear description so the LLM can select the right one per step, and the agent orchestrates them in a ReAct-style loop. Providing all three matches the stated requirements exactly.

Answer

Only a web search tool, because the LLM can handle calculations internally

Answer

Calculator tool and a retriever tool that fetches from a pre-defined knowledge base

Answer

A single LLM tool that can handle all three tasks

Question 12

An organization is deploying a RAG application with Oracle AI Vector Search. They need to ensure that the vector index supports low-latency queries and can handle updates to the underlying documents (inserts, deletes, modifications) without significant performance degradation. Which two index features should they consider? (Choose TWO.)

Accepted Answer

Use an IVF index with periodic rebuilds to maintain performance after many updates. An IVF (Inverted File) index with periodic rebuilds is well-suited for RAG applications that experience frequent updates (inserts, deletes, modifications). IVF indexes are designed for approximate nearest neighbor search, offering low-latency queries, but they can degrade over time as data changes; periodic rebuilds restore performance without requiring a full re-index of the entire dataset. This approach balances query speed with update tolerance, making it a practical choice for dynamic document collections in Oracle AI Vector Search.

Answer

Use a VECTOR data type with a default B-tree index

Answer

Enable exact nearest neighbor search to avoid index maintenance

Answer

Disable indexing and rely on full table scan for simplicity

Question 13

A team is iteratively refining a prompt for a summarization task. Which THREE activities are essential for effective iterative prompt refinement?

Accepted Answer

Establish evaluation criteria (e.g., accuracy, coherence, conciseness). Option A is correct because effective iterative prompt refinement requires clearly defined evaluation criteria such as accuracy, coherence, and conciseness, which give an objective basis for comparing prompt versions and deciding whether a change is an improvement. Option D is correct because A/B testing different prompt variants on a held-out set provides a controlled, unbiased comparison that reveals which phrasing or structure generalizes better rather than overfitting to the examples used during development. Option E is correct because testing with diverse and edge-case inputs exposes failure modes, ambiguity handling, and robustness gaps that a narrow sample would miss, which is essential for meaningful iteration. Option B is not appropriate because relying only on a static set of examples prevents discovering how the prompt behaves on new or unusual inputs and encourages overfitting. Option C is not essential because increasing max_tokens is a generation parameter tweak, not a prompt-refinement activity, and it does not by itself improve prompt quality or evaluation rigor.

Answer

Test the prompt with a static set of examples only

Answer

Increase max_tokens gradually

Question 14

Which LangChain document loader would be most appropriate to load content from a public website for inclusion in a knowledge base?

Accepted Answer

WebBaseLoader. WebBaseLoader is specifically designed to fetch and parse HTML content from URLs, making it the correct choice for loading a public website. It uses the requests library to retrieve the page and BeautifulSoup to extract text, stripping away HTML tags and scripts. This loader is ideal for building a knowledge base from web content because it handles the complexities of web scraping, such as handling different content types and encoding.

Answer

PDFLoader

Answer

CSVLoader

Answer

TextLoader

Question 15

A prompt engineer is designing a system to answer customer queries using OCI Generative AI. They want to ensure the model's responses are concise, accurate, and consistent. Which TWO prompt engineering practices should they implement? (Select two.)

Accepted Answer

Include a system prompt specifying 'You are a customer support agent. Answer concisely in two sentences.'. Option A is correct because a system prompt that assigns the role 'You are a customer support agent' and constrains the output to 'Answer concisely in two sentences' directly enforces the conciseness, tone, and consistency the scenario requires, and OCI Generative AI supports system-level instructions to steer model behavior. Option D is correct because a low temperature such as 0.1 sharpens the probability distribution over next tokens, making sampling nearly greedy and yielding deterministic, repeatable, and accurate answers rather than varied ones. Option B is wrong because removing context strips the model of the customer-specific information needed for accurate answers, increasing hallucination risk rather than improving accuracy. Option C is wrong because tree-of-thought is a multi-path reasoning technique suited to complex problem solving, and applying it to every query adds latency and cost without benefiting simple customer support responses. Option E is wrong because a temperature of 0.9 increases randomness and creativity, which directly contradicts the goals of concise, accurate, and consistent outputs.

Answer

Remove any context from the prompt to avoid biasing the model

Answer

Use the tree-of-thought technique for every query

Answer

Set temperature to 0.9 to encourage creative responses