1Z0-1127-25 Practice Test 19 — 15 Questions

Question 1

A user wants to test different prompt variations with a generative model interactively without writing code. Which OCI Generative AI tool should they use?

Accepted Answer

OCI Generative AI Playground. The OCI Generative AI Playground provides a no-code interface to test models, adjust parameters, and experiment with prompts interactively.

Answer

OCI Generative AI API with InferenceClient

Answer

OCI Data Science Notebook

Answer

OCI Generative AI Agents

Question 2

An OCI Generative AI user notices that a model generates repetitive phrases when summarizing technical articles. Which parameter adjustment is MOST likely to reduce this repetition?

Accepted Answer

Increase the frequency penalty. Frequency penalty reduces the likelihood of repeating tokens that have already appeared, directly targeting repetition. Presence penalty also helps but frequency penalty is stronger for repeated phrases.

Answer

Decrease max tokens

Answer

Set top-p to 0.95

Answer

Increase temperature to 0.9

Question 3

A prompt engineering team is designing a system prompt for a chatbot that should adopt a professional and concise tone. Which prompt pattern is BEST suited for this requirement?

Accepted Answer

Role prompting in the system message: 'You are a professional customer support agent. Respond concisely and accurately.'. Role prompting in the system message is best suited for setting a consistent tone and behavior for a chatbot. By defining the role as a professional customer support agent and instructing concise, accurate responses, the model adopts the desired persona and style across all interactions. This is a standard prompt engineering pattern for controlling tone.

Answer

Chain-of-thought prompting to ensure reasoning before answering

Answer

Zero-shot prompting with a direct instruction in the user message

Answer

Few-shot prompting with examples of professional responses in the user message

Question 4

A team has deployed a generative AI model and needs to monitor inference performance and set up alerts for increased error rates. Which OCI service should they integrate with?

Accepted Answer

OCI Monitoring. OCI Monitoring is the correct service because it provides metrics and alarms for tracking inference performance (e.g., latency, throughput) and error rates from deployed generative AI models. It allows you to set up threshold-based alerts on custom or predefined metrics, enabling proactive incident response. This directly addresses the requirement to monitor inference performance and alert on increased error rates.

Answer

OCI Cloud Guard

Answer

OCI Events

Answer

OCI Logging

Question 5

Which OCI Generative AI service component is specifically designed to convert text into numerical vectors (embeddings) that can be used for semantic search and clustering?

Accepted Answer

Embedding. The Embedding component in OCI Generative AI service is specifically designed to convert text into numerical vectors (embeddings). These embeddings capture semantic meaning, enabling use cases like semantic search and clustering by measuring vector similarity (e.g., cosine similarity).

Answer

Summarisation

Answer

Chat

Answer

Rerank

Question 6

A startup is using OCI Generative AI serverless inference for a text generation application. They notice that the latency is high during peak hours. They have a budget to increase costs moderately. Which action would most effectively reduce latency?

Accepted Answer

Switch to dedicated AI cluster.. Switching to a dedicated AI cluster is the most effective way to reduce latency during peak hours because it provides dedicated, isolated compute capacity that is not subject to the shared, multi-tenant contention of serverless inference. With serverless, throughput and latency can degrade when many customers share the same model endpoints. A dedicated cluster gives predictable, reserved GPU capacity and can be sized to handle peak load, directly addressing the latency spike.

Answer

Enable content filtering.

Answer

Increase the number of concurrent requests.

Answer

Use a smaller model.

Question 7

Which of the following is a primary limitation of large language models that can lead to generating factually incorrect information?

Accepted Answer

Hallucinations. Hallucinations are a primary limitation of large language models because they cause the model to generate text that is factually incorrect, nonsensical, or not grounded in the training data. This occurs due to the probabilistic nature of token prediction, where the model prioritizes fluency and coherence over factual accuracy, especially when the prompt lacks sufficient context or the model is asked to recall specific facts not well-represented in its training.

Answer

Bias in training data

Answer

Context window limitation

Answer

Knowledge cutoff

Question 8

A legal firm needs an AI assistant that can answer questions based on a large corpus of internal regulations that change quarterly. The firm also requires high accuracy and the ability to cite sources. Which approach should the firm choose?

Accepted Answer

Build a RAG application with vector search and citation generation. Retrieval-Augmented Generation (RAG) with vector search allows the legal firm to index its quarterly-changing regulations into a vector database, retrieve the most relevant chunks for each query, and generate answers with source citations. This approach ensures high accuracy by grounding the LLM's output in the current, authoritative documents without requiring retraining, and citation generation provides the necessary source traceability for legal compliance.

Answer

Use a pre-trained model without customization

Answer

Implement a rule-based search engine

Answer

Fine-tune a pre-trained model on the current regulations

Question 9

Which TWO parameters directly control the randomness and diversity of generated tokens?

Accepted Answer

Temperature. Temperature (A) is correct because it scales the logits before the softmax, directly flattening or sharpening the probability distribution: higher values increase randomness and diversity of token selection, while lower values make output more deterministic. Top-p (D), also called nucleus sampling, is correct because it restricts sampling to the smallest set of tokens whose cumulative probability reaches p, directly controlling how diverse the candidate pool is at each step. Stop sequences (B) only define strings that halt generation and do not affect token probability or sampling randomness. Frequency penalty (C) adjusts logits based on how often a token has already appeared, discouraging repetition, but it is a repetition-control parameter rather than a direct randomness/diversity sampler. Max tokens (E) merely caps the total length of the generated output and has no effect on how tokens are sampled.

Answer

Stop sequences

Answer

Frequency penalty

Answer

Max tokens

Question 10

Which OCI Generative AI API is used to send a message and receive a model-generated response while maintaining a conversation history?

Accepted Answer

Chat API. The Chat API is specifically designed for multi-turn conversations, supporting system prompts and history. The Generate API is for single-turn text generation. InferenceClient is a client class, not an API endpoint. The Embeddings API is for vector generation.

Answer

InferenceClient

Answer

Embeddings API

Answer

Generate API

Question 11

A LangChain application uses an agent with a calculator tool and a search tool. The agent is supposed to answer a question that requires both arithmetic and web lookup, but it only uses the search tool and gives an approximate answer. Which agent type is MOST likely to correctly combine the tools?

Accepted Answer

ReAct agent (e.g., zero-shot-react-description). The ReAct agent (zero-shot-react-description) is designed to reason step-by-step and decide which tool to use based on the task. It can combine the calculator and search tools by first performing a web lookup to get necessary data, then using the calculator tool for arithmetic, producing an exact answer. This agent type explicitly supports multi-step reasoning and tool selection, unlike simpler agents that may default to a single tool.

Answer

Zero-shot agent without ReAct (e.g., zero-shot-react-description)

Answer

Structured tool chat agent

Answer

Conversational agent with memory

Question 12

When using LangChain's RetrievalQA chain with `chain_type="stuff"`, what happens if the retrieved documents exceed the model's context window?

Accepted Answer

The chain raises an error because the input is too long. The `stuff` chain type concatenates all retrieved documents into a single prompt and sends it to the LLM in one call. If the combined text plus the question exceeds the model's context window, LangChain does not truncate, summarize, or switch models — it passes the oversized prompt to the LLM, which raises a context-length error (e.g., a token-limit exception from the provider). This is the defining limitation of the `stuff` approach.

Answer

The chain automatically truncates the documents to fit

Answer

The chain uses a sliding window to summarize documents

Answer

The chain switches to a different model with larger context

Question 13

A developer is implementing a text generation pipeline and wants to produce diverse, creative outputs. They set temperature=1.2, top_k=50, and top_p=1.0. What is the MOST likely effect of this combination?

Accepted Answer

The output will be diverse and creative, but may occasionally be incoherent or off-topic. Temperature=1.2 is above 1.0, which flattens the softmax distribution and increases randomness, making the model more likely to pick lower-probability tokens. top_k=50 restricts sampling to the 50 most likely tokens, which still allows significant diversity. top_p=1.0 means no nucleus filtering is applied (all tokens are considered). The combination yields diverse, creative output but raises the risk of incoherent or off-topic text because high temperature can select unlikely tokens that derail the sequence.

Answer

The output will be identical to greedy decoding because top_p=1.0 disables sampling

Answer

The output will be mostly factual because top_k filters out unlikely tokens

Answer

The output will be highly deterministic and repetitive

Question 14

An ML engineer is selecting a pre-trained model for a code generation task. The model must be able to generate syntactically correct code in multiple programming languages. Which model family is BEST suited for this task?

Accepted Answer

Meta Llama (Code Llama variant). Models like Code Llama (a variant of Llama) are specifically fine-tuned on code and are known for strong code generation capabilities. While other models can generate code, Code Llama is the best fit among the options.

Answer

BERT

Answer

Cohere Command

Answer

Mistral

Question 15

A developer is building a RAG application using OCI Generative AI. They notice that the generated responses often contain outdated information even though the knowledge base is updated daily. What is the most likely cause?

Accepted Answer

The vector database index is not rebuilt after data updates.. In a RAG pipeline, the vector database index is a static snapshot of the embedded knowledge base. When the knowledge base is updated daily, the index must be rebuilt or incrementally updated to reflect the new data. Without rebuilding, the retrieval step will still search the old index, returning outdated chunks and causing the LLM to generate stale responses.

Answer

The embedding model is not fine-tuned on the latest data.

Answer

The retrieval top-k is set too high.

Answer

The chunk size is too small, causing loss of context.