1Z0-1127-25 Practice Test 13 — 15 Questions

Question 1

A development team wants to generate code snippets from natural language. Which model strategy should they adopt?

Accepted Answer

Use a code-specific model like Code Llama.. Code Llama is a specialized variant of Llama 2 that has been fine-tuned on code datasets, enabling it to generate syntactically and semantically correct code from natural language prompts. This makes it the optimal choice for code generation tasks, as general-purpose LLMs lack the targeted training on code structures and programming languages.

Answer

Use a general-purpose LLM like Llama 2.

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Use a multimodal model.

Answer

Use an embedding model for text.

Question 2

A developer is using OCI GenAI to generate structured data. They often get responses that include additional commentary or markdown. Which prompt engineering technique should they use to ensure only JSON output?

Accepted Answer

Add 'Return only JSON' at the end of the prompt.. Explicitly instructing the model to 'Return only JSON' directly constrains the output format, reducing the likelihood of extraneous commentary or markdown. This technique leverages prompt engineering to guide the model's behavior without altering inference parameters like temperature or top_p, which control randomness rather than output structure.

Answer

Set top_p to 0.1.

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Use a model with a larger context window.

Answer

Increase the temperature to 1.5.

Question 3

An AI engineer is designing a prompt to generate a report summary. The prompt currently says: 'Summarize the following text.' The output is often too verbose. Which modification would best enforce a concise, bullet-list format?

Accepted Answer

Change the prompt to: 'Summarize the following text in a bullet list of at most 5 items. Each bullet must be under 10 words.'. Explicitly specifying the output format (bullet list, max 5 items) gives the model clear constraints, reducing verbosity.

Answer

Set temperature to 1.0 for more focused outputs

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Increase the frequency penalty to 2.0

Answer

Add an example summary at the end of the prompt

Question 4

You are a cloud architect at a global e-commerce company. The company is building a RAG-based product support chatbot using OCI Generative AI Service and OCI OpenSearch. The chatbot must answer customer questions in real-time by retrieving from a product knowledge base containing over 10 million documents. The current architecture uses a single vector index with all documents, and the LLM (Cohere Command R+) returns answers in English only. The team observes that queries from non-English customers often return irrelevant results, and the chatbot sometimes fails to generate answers within the 5-second SLA. The leadership wants to support 10 languages and reduce the average response time to under 3 seconds. You need to propose a solution that improves both relevance and latency. Which course of action should you take?

Accepted Answer

Replace the embedding model with a multilingual model and partition the vector index by language to reduce search space.. Partitioning the vector index by language reduces the search space for each query, directly improving retrieval latency, while using a multilingual embedding model ensures that non-English queries are semantically matched to documents in their original language, improving relevance. This combination addresses both the 3-second SLA and the 10-language requirement without relying on translation, which introduces latency and potential loss of meaning.

Answer

Increase the number of OCI OpenSearch nodes and upgrade the LLM to a faster variant.

Answer

Translate all non-English queries to English before retrieval and use an English-only embedding model.

Answer

Implement a caching layer for frequent queries and use a larger LLM for better accuracy.

Question 5

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 6

A team is implementing a conversational chatbot that needs to remember a user's previous messages within the same session. They are using LangChain with OCI Generative AI. Which memory type and persistence approach should they choose for session-only memory?

Accepted Answer

ConversationBufferMemory stored in-memory. ConversationBufferMemory stores the full message history in memory, suitable for short sessions. For session-only memory, in-memory persistence is sufficient; no external database is needed. Summary memory is more suited for long conversations where token limits are a concern.

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ConversationBufferMemory persisted in a vector store

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ConversationSummaryMemory stored in a local file

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ConversationSummaryMemory persisted in Oracle Database

Question 7

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 8

Which OCI service provides a managed vector database capability that can be used as a knowledge base in a RAG architecture?

Accepted Answer

OCI Search with OpenSearch. OCI Search with OpenSearch provides a managed vector database capability through its k-nearest neighbor (k-NN) plugin, which supports storing and querying vector embeddings. This makes it suitable as a knowledge base in a Retrieval-Augmented Generation (RAG) architecture, where vector similarity search retrieves relevant context for LLM prompts.

Answer

OCI MySQL HeatWave

Answer

OCI Database (Autonomous Database)

Answer

OCI Object Storage

Question 9

A retail company wants to generate product descriptions from attribute data. They have no prior AI experience. Which approach is most appropriate?

Accepted Answer

Use the Cohere Command model with carefully crafted prompts.. The Cohere Command model is specifically designed for text generation tasks like creating product descriptions from attribute data. With no prior AI experience, using a pre-trained model with carefully crafted prompts is the most efficient and accessible approach, requiring no custom training or complex infrastructure.

Answer

Train a custom model from scratch.

Answer

Fine-tune a model on a synthetic dataset.

Answer

Use the Cohere Embed model to generate embeddings and then decode.

Question 10

A financial services company has deployed a custom fine-tuned model using OCI Generative AI service on a dedicated AI cluster for automated report generation. They use a Python application that sends prompts via the OCI SDK. Recently, they started seeing 429 Too Many Requests errors intermittently. The dedicated cluster has 2 replicas and the application is making about 100 requests per second. The cluster's documented throughput is 50 requests per second per replica. The company has not set up any throttling limits. What is the most likely cause of the 429 errors?

Accepted Answer

The application is exceeding the cluster's replica capacity.. The dedicated AI cluster has 2 replicas, each supporting 50 requests per second, for a total capacity of 100 requests per second. The application is sending exactly 100 requests per second, which matches the cluster's maximum throughput. However, without any throttling limits configured, the service will still enforce its built-in rate limits at the replica level, and any transient burst or uneven distribution across replicas can trigger 429 Too Many Requests errors. The most likely cause is that the application is operating at the cluster's replica capacity boundary, leading to intermittent throttling.

Answer

The OCI SDK version is outdated.

Answer

The API calls are not authenticated.

Answer

The model's context window is too small for the prompts.

Question 11

Refer to the exhibit. A developer ran the OCI CLI command shown and received the JSON output. What does the output indicate about the model's confidence and why?

Accepted Answer

The model is highly confident the text is positive, as indicated by the 0.98 score.. The JSON output shows a sentiment score of 0.98 for 'positive', which is very close to 1.0, indicating the model is highly confident that the text is positive. In sentiment analysis models, scores represent probabilities for each class, and a value near 1.0 for one class with much lower scores for others reflects strong confidence.

Answer

The model is uncertain because all scores are roughly equal.

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The model is neutral because the neutral score is lowest.

Answer

The model is unsure because the scores are probabilities that sum to 1.

Question 12

A company has deployed a generative AI model endpoint on OCI. They want to monitor token usage and latency for cost optimization. Which OCI service should they use to collect these metrics?

Accepted Answer

OCI Monitoring. A is correct because OCI Monitoring is the native telemetry service that collects and stores metrics such as token usage (e.g., input/output token counts) and latency (e.g., model inference latency) from OCI Generative AI endpoints. These metrics are automatically emitted by the OCI Generative AI service and can be queried via the Monitoring API or visualized in the Console, enabling cost optimization by tracking consumption patterns.

Answer

OCI Events

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OCI Notifications

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OCI Logging

Question 13

A company deployed OCI Generative AI for a customer service chatbot. They are using the Cohere command model. The chatbot is generating responses that are too brief and often cut off mid-sentence. They have limited budget. What should they do?

Accepted Answer

Increase max tokens to 1024.. The chatbot's responses are too brief and cut off mid-sentence, which indicates the model is hitting the maximum token limit for generation. Increasing the max tokens to 1024 allows the Cohere command model to produce longer, complete responses without truncation. This is the most direct and cost-effective fix for the described symptom.

Answer

Decrease the temperature to 0.2.

Answer

Increase the temperature to 0.9.

Answer

Use a different base model like Llama.

Question 14

A developer is using a large language model to generate code snippets. The model often produces code that is syntactically correct but functionally incorrect. What is the most effective way to improve the functional correctness of the generated code?

Accepted Answer

Provide few-shot examples of correct code in the prompt.. Providing few-shot examples of correct code in the prompt directly demonstrates the desired functional behavior to the model. This technique, known as few-shot prompting, grounds the model's output in concrete examples, significantly improving the likelihood that the generated code will be functionally correct by aligning the model's pattern completion with the intended logic, not just syntax.

Answer

Increase the temperature parameter to generate more creative solutions.

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Ask the model to only output syntactically valid code.

Answer

Set max_tokens to a very high value to allow the model more room to think.

Question 15

A developer is implementing a text generation pipeline using OCI Generative AI and needs to produce diverse, creative outputs for a marketing campaign. Which sampling strategy should they choose?

Accepted Answer

Top-p sampling with p=0.9. Top-p (nucleus) sampling selects from a dynamically chosen set of tokens whose cumulative probability exceeds p, allowing diversity while avoiding the long tail of improbable tokens.

Answer

Beam search

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Temperature sampling with temperature=0.0

Answer

Greedy decoding