1Z0-1127-25 Practice Test 45 — 15 Questions

Question 1

A data scientist is designing a prompt for code generation and needs to reduce the likelihood of the model generating incorrect or hallucinated code. Which two parameter adjustments are most effective? (Choose two.)

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

Accepted Answer

Set frequency_penalty to a moderate value (e.g., 0.5). Lowering temperature reduces randomness, making outputs more deterministic and less prone to hallucinations. Frequency penalty reduces repetitive mistakes. Top-k and presence penalty are less directly effective.

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Set top-k to a high value (e.g., 100)

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Increase max_tokens significantly

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Set presence_penalty to 0

Question 2

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.

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

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

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

Question 3

A developer wants to compose a LangChain pipeline using the LCEL (LangChain Expression Language) to combine a prompt template, a model, and an output parser. Which operator is used for this composition?

Accepted Answer

| (pipe) operator. In LangChain Expression Language (LCEL), the pipe operator `|` is used to compose components like prompt templates, models, and output parsers into a single chain. This operator passes the output of one component as the input to the next, enabling a clean, declarative pipeline syntax.

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>> (right shift) operator

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-> (arrow) operator

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+ (plus) operator

Question 4

A team is building a Retrieval-Augmented Generation (RAG) pipeline using OCI Generative AI. They need to store and retrieve document embeddings for semantic search. Which OCI service is most appropriate as the vector store?

Accepted Answer

OCI Search with OpenSearch. OCI Search with OpenSearch is the most appropriate vector store for a RAG pipeline because it natively supports storing and querying high-dimensional vector embeddings using the k-nearest neighbor (k-NN) algorithm. It integrates directly with OCI Generative AI to enable semantic search over ingested documents, providing the required similarity search capabilities for retrieval-augmented generation.

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OCI Streaming

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OCI Object Storage

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OCI Autonomous Database with AI Vector Search

Question 5

A data scientist is evaluating different models for a summarization task. Which two metrics are commonly used to evaluate the quality of generated summaries?

Accepted Answer

ROUGE. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a standard metric for summarization that measures the overlap of n-grams, word sequences, or word pairs between the generated summary and reference summaries. It focuses on recall, making it well-suited for evaluating how well the generated summary captures the key content from the reference.

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F1 score

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Mean Average Precision

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Perplexity

Question 6

Refer to the exhibit. The output is very short and cuts off mid-sentence. Which parameter is most likely the cause?

Accepted Answer

Max-tokens is too low. The 'max-tokens' parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. When set too low, the model is forced to stop generating output before completing its thought, resulting in a truncated or cut-off sentence. This is the most direct cause of an output that ends abruptly mid-sentence.

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Temperature is too high

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Model ID incorrect

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Top-p is too high

Question 7

A developer wants to integrate generative AI capabilities into an application using REST API calls. Which OCI Generative AI service endpoint should they use for text generation?

Accepted Answer

/completions. The OCI Generative AI service exposes a REST API endpoint at `/completions` specifically for text generation tasks. This endpoint accepts a prompt and returns a generated text completion, aligning directly with the developer's requirement to integrate generative AI capabilities via REST API calls.

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/models

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/inference

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/chat

Question 8

A team deploys a generative AI model endpoint and notices intermittent 429 Too Many Requests errors. The endpoint is configured with auto-scaling using a dedicated AI cluster. What is the most likely cause?

Accepted Answer

The auto-scaling policy is not aggressive enough. The 429 Too Many Requests error indicates that the endpoint is receiving more requests than it can handle. With auto-scaling enabled on a dedicated AI cluster, the most likely cause is that the auto-scaling policy is not aggressive enough to keep up with the request rate, meaning it scales up too slowly or has insufficient maximum instance limits to handle the traffic spike.

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The model's context window exceeded

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Insufficient storage on the cluster

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Rate limiting at the OCI API Gateway

Question 9

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.

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Fine-tune the model on customer-specific data.

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Switch to a larger model.

Question 10

A company is building a customer support chatbot that uses Retrieval-Augmented Generation (RAG) with OCI Generative AI. They need low-latency responses and the ability to update the knowledge base daily. Which architecture best meets these requirements?

Accepted Answer

Use OCI Search with OpenSearch for the vector database, OCI Generative AI for inference, and Oracle Database for metadata.. It combines OCI Search with OpenSearch as a vector database for efficient similarity search, OCI Generative AI for inference, and Oracle Database for metadata management. This architecture provides low-latency responses by leveraging OpenSearch's optimized vector indexing and allows daily knowledge base updates through Oracle Database's robust data management capabilities.

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Store embeddings in OCI Object Storage and use OCI Functions to perform similarity search.

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Use OCI Data Science Notebook Sessions to run the RAG pipeline with a managed Cohere model.

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Use OCI Streaming to ingest documents and OCI Data Flow to update a knowledge base in OCI Object Storage.

Question 11

A company uses OCI Generative AI Agents to build a RAG application that answers questions from documents stored in OCI Object Storage. The knowledge base is updated daily. Which step is necessary to ensure the agent incorporates the latest documents?

Accepted Answer

Re-sync the knowledge base after updating the documents in Object Storage. The knowledge base indexes the data sources. To reflect changes in the source documents, you must re-sync or re-index the knowledge base. The agent endpoint or session API does not automatically refresh content.

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Use the Embedding API to manually index each new document

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Set the session API to refresh automatically

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Recreate the agent each time documents change

Question 12

A financial firm wants to use OCI Generative AI for contract analysis. They need to reduce costs by using a smaller, specialized model. Which approach should they take?

Accepted Answer

Fine-tune a smaller base model on a dedicated AI cluster. Fine-tuning a smaller base model on a dedicated AI cluster allows the financial firm to tailor the model specifically for contract analysis tasks, reducing computational overhead and cost compared to using a large general-purpose model. OCI Generative AI supports fine-tuning of smaller models like Cohere Command Light on dedicated AI clusters, enabling domain-specific optimization without the expense of running a large model for every inference.

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Use a large base model (e.g., Cohere Command) on a serverless endpoint

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Use a large base model on a dedicated AI cluster

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Use a third-party LLM

Question 13

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.

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

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Enable exact nearest neighbor search to avoid index maintenance

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Disable indexing and rely on full table scan for simplicity

Question 14

Which OCI Generative AI service component is designed to convert text into vector representations for use in semantic search?

Accepted Answer

Embedding API. The Embedding API generates vector embeddings from text, which can be used for semantic search, clustering, etc.

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Chat API

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Summarisation API

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Generate API

Question 15

A company is designing a generative AI application using OCI Generative AI. Which two factors should be considered when selecting the appropriate model? (Choose two.)

Accepted Answer

Model's training data cutoff date. The model's training data cutoff date determines the temporal scope of the model's knowledge. For generative AI applications requiring up-to-date information or compliance with data recency requirements, selecting a model with a cutoff date that aligns with the use case is critical. OCI Generative AI models have specific cutoff dates (e.g., June 2023 for certain models), and using a model with an older cutoff may produce outdated or factually incorrect responses.

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Availability in all OCI regions

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Supported languages

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Built-in safety filters