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

Question 1

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

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Greedy decoding

Question 2

Which THREE models are available as part of the OCI Generative AI service?

Accepted Answer

Llama 3. Llama 3 (A) is correct because OCI Generative AI offers Meta's Llama family, including Llama 3, as pre-trained and fine-tunable chat models in the playground and API. Cohere Command (C) is correct because Cohere's Command models (e.g., Command R/R+) are among the foundational text-generation models hosted by the OCI Generative AI service. Cohere Embed (E) is correct because Cohere's Embed models for generating text embeddings are also provided by the service for semantic search and RAG use cases. GPT-4 (B) is not offered by OCI Generative AI, as it is an OpenAI model accessed through Azure OpenAI or OpenAI's own APIs. Stable Diffusion (D) is an image-generation model and is not part of the OCI Generative AI text/embedding model catalog.

Answer

GPT-4

Answer

Stable Diffusion

Question 3

A user wants to use OCI Generative AI to generate marketing copy. They want the output to be more creative and varied. Which parameter should they adjust?

Accepted Answer

Increase the temperature parameter.. Increasing the temperature parameter makes the model's output more random and diverse, which is ideal for creative tasks like generating marketing copy. A higher temperature (e.g., 0.7–1.0) increases the probability of sampling less likely tokens, leading to more varied and imaginative text. Setting temperature to 0 would make the output deterministic and repetitive, which is the opposite of what the user wants.

Answer

Set temperature to 0.

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Decrease the temperature parameter.

Answer

Increase the max_tokens parameter.

Question 4

A developer is using ChatPromptTemplate with MessagesPlaceholder to handle conversation history. What is the purpose of MessagesPlaceholder in the prompt template?

Accepted Answer

To provide a slot where conversation history messages can be injected dynamically. MessagesPlaceholder in LangChain's ChatPromptTemplate is specifically designed to inject conversation history messages dynamically at a designated position in the prompt. It acts as a slot that can hold a list of messages (e.g., from a chat memory buffer) and is replaced at runtime with the actual history, allowing the LLM to maintain context across turns.

Answer

To insert a single user message at a specific position

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To add a system instruction that is always present

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To convert the chat history into a plain string for the LLM

Question 5

An organization is evaluating two LLMs for a code generation task. Model A has a perplexity of 1.5 on the validation set, and Model B has a perplexity of 3.0. However, Model A generates more syntactically incorrect code. Which conclusion is MOST valid?

Accepted Answer

Perplexity alone is insufficient; evaluate with task-specific metrics like BLEU or human review. Perplexity measures how well a language model predicts a sequence, but it does not directly assess code correctness or syntactic validity. Model A's lower perplexity (1.5) indicates it is more confident in its predictions, yet it produces more syntactically incorrect code, showing that perplexity alone is not a reliable indicator of code generation quality. Task-specific metrics like BLEU (for n-gram overlap) or human review are necessary to evaluate functional and syntactic correctness, making option C the most valid conclusion.

Answer

Model A's perplexity indicates overfitting, so Model B is preferable

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Model B is better because higher perplexity correlates with more diverse outputs

Answer

Model A is better because lower perplexity always indicates higher quality outputs

Question 6

An OCI GenAI practitioner wants to deploy a model that can generate code from natural language descriptions. Which type of model is most suitable?

Accepted Answer

GPT. GPT (Generative Pre-trained Transformer) is the most suitable model for code generation from natural language because it is an autoregressive language model optimized for text generation tasks. Unlike encoder-only models, GPT generates coherent, contextually relevant sequences of tokens, making it ideal for producing code based on descriptive prompts.

Answer

T5

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ResNet

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BERT

Question 7

Which prompt engineering technique involves providing the model with a few input-output examples within the prompt to guide its response?

Accepted Answer

Few-shot prompting. Few-shot prompting (Option C) is the correct answer because it explicitly involves providing the model with a small set of input-output examples within the prompt to guide its response. This technique helps the model understand the desired task format, style, or reasoning pattern without requiring fine-tuning, leveraging in-context learning to adapt its output based on the provided demonstrations.

Answer

Tree-of-thought prompting

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Zero-shot prompting

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Chain-of-thought prompting

Question 8

A team is optimizing a RAG pipeline for OCI Generative AI. They observe that the model's responses are verbose and often include irrelevant details from the retrieved chunks, reducing user satisfaction. They have already tuned the prompt template. What is the most effective next step?

Accepted Answer

Implement a re-ranking step using a cross-encoder model.. Implementing a re-ranking step with a cross-encoder model directly addresses the problem of verbose and irrelevant responses. Cross-encoders evaluate the query-document pair jointly, producing a fine-grained relevance score that filters out noisy or off-topic chunks before they reach the generation model. This improves the quality of the context provided to the LLM, reducing verbosity and irrelevance without requiring retraining or altering the retrieval threshold.

Answer

Apply instruction tuning on the generation model.

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Reduce the number of retrieved chunks from 5 to 3.

Answer

Increase the similarity threshold for retrieval from 0.7 to 0.85.

Question 9

A company has deployed a model on a Dedicated AI Cluster and needs to monitor inference performance metrics such as request latency, throughput, and error rates. Which OCI service provides built-in monitoring dashboards for these metrics?

Accepted Answer

OCI Monitoring. OCI Monitoring is the correct service because it provides built-in dashboards and metrics for inference performance, including request latency, throughput, and error rates, specifically for Dedicated AI Cluster deployments. These metrics are automatically collected and visualized in the OCI Monitoring console, allowing real-time tracking of model inference health without additional configuration.

Answer

OCI Logging

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OCI Notifications

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OCI Events

Question 10

An enterprise is deploying a RAG application for compliance document analysis using OCI. They use OCI OpenSearch as the vector store and have millions of documents. Retrieval latency is critical. Currently, a single query takes over 2 seconds. The index uses a flat (brute-force) distance computation. They have considered using approximate nearest neighbor (ANN) algorithms but are unsure about the impact on recall. They need to reduce latency to under 500ms while maintaining high recall. What should they do?

Accepted Answer

Switch to an HNSW algorithm with an appropriate M and ef_search parameters.. Switching to HNSW with appropriate parameters provides fast approximate search with configurable recall. Option A (reducing dimensions) can degrade embedding quality. Option B (reducing shards) may not achieve the required latency reduction. Option D (increasing top-k) would increase latency.

Answer

Use a smaller embedding dimension by truncating the existing embeddings.

Answer

Reduce the number of shards in the OpenSearch index to improve parallelism.

Answer

Increase the top-k parameter to retrieve more candidates then filter.

Question 11

A team is using LangChain's ConversationalRetrievalChain with ConversationBufferMemory to build a chatbot. After a few turns, the chatbot starts repeating information from earlier messages. What is the MOST likely cause?

Accepted Answer

The conversation history accumulates in the prompt, causing the model to focus on past messages. The most likely cause is that ConversationBufferMemory accumulates the entire conversation history into the prompt, so as turns increase, the model sees a growing context of past messages and tends to repeat or over-focus on earlier information. This is a known limitation of buffer-based memory, which does not summarize or prune old turns.

Answer

The chunk_size in the text splitter is too large

Answer

The LLM's max_tokens parameter is set too low

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The retriever's similarity search returns irrelevant chunks

Question 12

A practitioner is developing a legal document summarization system and needs to reduce hallucinations. Which prompting technique is most effective for improving factual accuracy by exploring multiple reasoning paths?

Accepted Answer

Self-consistency prompting. Self-consistency prompting improves factual accuracy by generating multiple diverse reasoning paths for the same prompt and then selecting the most consistent answer through majority voting. This reduces hallucinations by leveraging the idea that correct reasoning tends to converge, while incorrect reasoning diverges. It is especially effective for tasks requiring complex reasoning, such as legal document summarization.

Answer

Few-shot prompting

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Zero-shot prompting

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Chain-of-thought prompting

Question 13

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 14

A developer is building a code generation assistant and needs to ensure the LLM follows a specific output format (e.g., JSON). Which approach is MOST effective for achieving format adherence without retraining?

Accepted Answer

Provide a few-shot example of the desired JSON format in the prompt. Few-shot prompting—providing explicit examples of the desired JSON format in the prompt—directly guides the LLM's output structure without requiring retraining. This technique leverages in-context learning, where the model infers the required schema from the examples, making it the most effective and efficient method for format adherence.

Answer

Lower the temperature to 0 to reduce output variability

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Fine-tune the model on a dataset of JSON code examples

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Increase the context window to include more code context

Question 15

A company is deploying a large language model for a customer service chatbot. The model needs to understand industry-specific jargon and maintain low latency. Which approach best balances these requirements?

Accepted Answer

Fine-tune a small open-source LLM on domain-specific data. Fine-tuning a small open-source LLM on domain-specific data is the best approach because it adapts the model to understand industry-specific jargon while keeping the model small enough to maintain low latency. Unlike larger models, a fine-tuned small model can run efficiently on local hardware, reducing inference time and avoiding the overhead of external API calls or large model sizes.

Answer

Employ retrieval-augmented generation (RAG) with a general model

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Rely solely on prompt engineering with a general model

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Use a large general-purpose LLM with zero-shot prompting