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

Question 1

A team is building a code generation assistant using OCI Generative AI. They notice that the model occasionally produces code with subtle security vulnerabilities. Which approach would most effectively reduce this risk without compromising the assistant's usefulness?

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

Accepted Answer

Fine-tune the model on a dataset of secure code examples and security best practices. Fine-tuning on a curated dataset of secure code examples can teach the model to avoid common vulnerability patterns while retaining its general coding ability. RAG with security docs could also help, but fine-tuning directly addresses the model's behavior more comprehensively.

Answer

Use a larger context window to include all project files in every prompt

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Switch to a model with more parameters

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Use greedy decoding to reduce randomness in code generation

Question 2

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Train a custom model from scratch on the policy documents each month

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Fine-tune a base LLM on the policy documents monthly

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Use a larger foundation model with a longer context window and paste all documents into each prompt

Question 3

A company wants to build a RAG application using OCI Generative AI Agents. Which TWO components are required to set up the agent?

Accepted Answer

A knowledge base. A knowledge base is the core repository that stores the indexed content used by OCI Generative AI Agents to retrieve relevant information for answering queries. Without a knowledge base, the agent has no source of domain-specific data to ground its responses, making it a mandatory component.

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An embedding model

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A dedicated AI cluster

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A fine-tuned model

Question 4

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. Chunking strategy (size and overlap) directly affects how well the retrieval step can find relevant passages. Too large or poorly split chunks can dilute semantic meaning.

Answer

The retrieval step uses greedy decoding

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

Answer

Cosine similarity is not appropriate for comparing embeddings

Question 5

An enterprise needs to deploy a custom fine-tuned model for real-time inference with strict latency requirements. They also need to manage costs by paying only for usage. Which three steps are required to achieve this? (Select THREE)

Accepted Answer

Create an endpoint using InferenceClient to point to the dedicated cluster. To deploy a custom model for dedicated low-latency inference, you need a Dedicated AI Cluster, host the model on it, and create an endpoint to access it. Using shared infrastructure does not guarantee low latency. Deletion of cluster is not needed. On-demand tokens are for pay-as-you-go, not dedicated clusters.

Answer

Use the on-demand token-based inference on shared infrastructure

Answer

Delete the fine-tuning job after deployment to save costs

Question 6

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Train a custom model from scratch on the policy documents each month

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Question 7

An enterprise is deploying an LLM application on OCI and must minimize hallucinations. Which TWO strategies should they implement? (Choose two.)

Accepted Answer

Apply prompt engineering techniques such as asking the model to cite sources. RAG grounds the model in retrieved documents, and prompt engineering (e.g., asking the model to cite sources) can reduce hallucinations. Fine-tuning on factual data helps but may not eliminate hallucinations entirely. Increasing temperature increases randomness, which can worsen hallucinations. Using a smaller model typically reduces capability, making hallucinations more likely.

Answer

Increase the temperature parameter to encourage more diverse outputs

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Use a smaller model to reduce complexity

Answer

Fine-tune the model on a dataset of factual question-answer pairs

Question 8

A developer notices that a text generation model produces repetitive phrases when using greedy decoding. Which sampling strategy would best introduce controlled randomness to reduce repetition while maintaining coherence?

Accepted Answer

Apply temperature sampling with temperature > 1. Temperature sampling scales the logits before applying softmax, controlling the randomness of token selection. A temperature > 1 increases diversity, reducing repetition, while still allowing the model to generate coherent text.

Answer

Use greedy decoding but with a repetition penalty

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Increase the beam width in beam search

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Disable top-k sampling

Question 9

A company wants to use OCI Generative AI Agents to build a question-answering system over documents stored in OCI Object Storage. Which component acts as the knowledge source for the agent?

Accepted Answer

A knowledge base created from the Object Storage bucket. In OCI Generative AI Agents, a knowledge base is the indexed representation of data sources (like Object Storage buckets). The agent uses the knowledge base to retrieve relevant information.

Answer

A Dedicated AI Cluster

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The OCI Generative AI Playground

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An endpoint created via the InferenceClient

Question 10

A developer is using the Cohere Command R model with a preamble set to "You are a helpful assistant." The user message contains a question. The model's response is factual but lacks detail. To encourage more comprehensive answers, which modification should be made?

Accepted Answer

Add "Provide a thorough and detailed response." to the preamble. Adding a directive in the preamble, such as "Provide thorough and detailed responses," sets the desired behavior from the start.

Answer

Use a stop sequence like "
" to force longer responses

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Set the temperature to 0.0

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Increase the max_tokens parameter to 1000

Question 11

Which TWO are benefits of using few-shot prompting compared to zero-shot prompting?

Accepted Answer

It helps the model understand the desired pattern, especially for uncommon tasks. Few-shot provides examples that improve format adherence and guide the model, especially for complex tasks.

Answer

It always reduces the need for parameter tuning

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It eliminates the need for a system prompt

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It reduces the number of tokens in the output

Question 12

Which TWO are best practices for prompt management in production environments?

Accepted Answer

Maintain a prompt library with reusable templates. Versioning and maintaining a library of templates are essential for tracking changes and reusability.

Answer

Avoid using system prompts to keep prompts simple

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Keep all prompts as hard-coded strings in the application code

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Use the same prompt for all use cases to reduce complexity

Question 13

A team is deploying a chatbot that must never output harmful or biased statements. They plan to use a pre-trained LLM with in-context learning. Which additional measure is MOST effective at reducing harmful outputs without retraining?

Accepted Answer

Include few-shot examples in the system prompt that demonstrate appropriate responses. Providing few-shot examples of desired behavior in the system prompt (in-context learning) can guide the model toward safe responses. Fine-tuning would require retraining, and prompt engineering is broader; few-shot examples are a specific, effective technique.

Answer

Apply a larger context window to include more safety instructions

Answer

Fine-tune the model on a curated dataset of safe dialogues

Answer

Use beam search with a high beam width

Question 14

An organization needs to choose a model for a multilingual customer support chatbot that must understand and respond in five different languages. Which TWO models available in OCI Generative AI are suitable?

Accepted Answer

Meta Llama 3. Cohere Command R+ and Meta Llama 3 support multiple languages.

Answer

Cohere Command R

Answer

Cohere Rerank

Answer

Cohere embed-english-v3.0

Question 15

An enterprise RAG application experiences high latency during peak hours. The architecture uses OCI OpenSearch with a single node cluster storing 5 million vectors (768 dimensions). The search uses exact k-NN (EF_SEARCH=500). The average query takes 1.5 seconds, but the SLA requires <500ms. The team considers several options: A) Switch to ANN with lower recall (HNSW with ef_search=50), B) Scale OpenSearch cluster to 3 nodes, C) Reduce embedding dimension to 256 using PCA, D) Increase the number of shards from 1 to 10. Which option provides the best balance of latency reduction and minimal impact on retrieval quality? (Assume all options are feasible)

Accepted Answer

Increase the number of shards from 1 to 10. Increasing shards on the same node partitions the index, so each shard contains fewer vectors, making exact search faster. This reduces latency without sacrificing accuracy. ANN reduces recall, scaling adds cost and complexity, and dimension reduction can degrade embedding quality.

Answer

Scale OpenSearch cluster to 3 nodes

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Switch to ANN (HNSW with ef_search=50)

Answer

Reduce embedding dimension to 256 using PCA