1Z0-1127-25 Practice Test 5 — 25 Questions

Question 1

A data scientist is debugging a RAG system where the generated answers are not relevant to the retrieved documents. Which TWO factors are MOST likely causing this issue?

Accepted Answer

The retriever is returning irrelevant chunks due to poor embeddings or low similarity threshold. If the retriever returns irrelevant chunks or the generation model ignores the context, the answers will be off-topic. Checking retrieval relevance and prompt instruction is key.

Answer

The context window of the generation model is smaller than the retrieved chunks

Answer

The temperature is set too low, making outputs deterministic

Answer

The chunking strategy produces chunks that are too small, losing context

Question 2

A team fine-tuned a Cohere Command R model in OCI GenAI and validated it. They now need to deploy it for production inference with a dedicated endpoint. What is the correct sequence of steps?

Accepted Answer

Provision a Dedicated AI Cluster, deploy the model to the cluster, then create an endpoint. In OCI Generative AI, the correct sequence for deploying a fine-tuned model to a dedicated endpoint is: first provision a Dedicated AI Cluster (which provides the isolated compute infrastructure), then deploy the model to that cluster, and finally create an endpoint that exposes the deployed model for inference. This ensures the model is hosted on dedicated resources before the endpoint is created.

Answer

Create an endpoint, deploy the model, then provision a Dedicated AI Cluster

Answer

Deploy the model to a shared endpoint, then provision a Dedicated AI Cluster for scaling

Answer

Create an endpoint, then provision a Dedicated AI Cluster, then deploy the model

Question 3

Which LangChain document loader would be most appropriate to load content from a public website for inclusion in a knowledge base?

Accepted Answer

WebBaseLoader. WebBaseLoader is specifically designed to load documents from web URLs, fetching the HTML content and converting it to LangChain Document objects. PDFLoader, CSVLoader, and TextLoader are for local files of specific formats.

Answer

PDFLoader

Answer

CSVLoader

Answer

TextLoader

Question 4

A retail company uses OCI Generative AI Service to build a RAG chatbot for product recommendations. The chatbot should consider both the user's query and the retrieved product descriptions. Which component of the RAG pipeline is responsible for combining these inputs before sending to the LLM?

Accepted Answer

Prompt template. The prompt template is the component in a RAG pipeline that structures the final input to the LLM by combining the user's query with the retrieved product descriptions. It defines the format and instructions (e.g., 'Based on these product descriptions, recommend...') that the LLM uses to generate a coherent response. Without a prompt template, the raw query and documents would be sent without context, leading to poor or irrelevant outputs.

Answer

Reranker

Answer

Document retriever

Answer

Embedding model

Question 5

A global enterprise is deploying a generative AI application that requires high availability across multiple OCI regions. The application must automatically fail over to a secondary region if the primary region becomes unavailable. What is the recommended architecture to achieve this?

Accepted Answer

Deploy endpoints in two regions behind an OCI Load Balancer with cross-region failover. OCI Load Balancer supports cross-region failover by distributing traffic across backend sets in multiple regions, enabling automatic failover to a secondary region when the primary region becomes unavailable. This architecture ensures high availability for generative AI applications by leveraging health checks and failover policies at the load balancer level, which is the recommended approach for multi-region active-passive setups.

Answer

Deploy OCI Generative AI endpoints in two regions and use a global DNS round-robin

Answer

Use OCI Streaming to replicate requests between regions

Answer

Use DNS failover with a single endpoint in the primary region

Question 6

A developer uses OCI Generative AI's chat endpoint with a system message placed after user messages. The model ignores the system message. What is the most likely reason?

Accepted Answer

The system message is placed after user messages. In OCI Generative AI's chat endpoint, the system message must be placed before user messages to establish the model's behavior and context. When placed after user messages, the model treats it as part of the conversation history rather than a directive, causing it to be ignored. This ordering is a fundamental requirement for the chat API's message structure.

Answer

The system message is too long

Answer

Temperature is set too high

Answer

The model has not been fine-tuned for instruction following

Question 7

A data scientist is preparing to fine-tune a foundation model on OCI. Which two actions should they take to optimize costs? (Select TWO.)

Accepted Answer

Use the smallest model that meets accuracy requirements. Using the smallest model that meets accuracy requirements directly reduces the number of parameters and computational operations required during fine-tuning. On OCI, larger models consume significantly more GPU memory and compute hours, so selecting the minimal viable model minimizes both training time and associated costs. This aligns with cost optimization best practices for generative AI workloads.

Answer

Use a single OCPU shape to minimize per-hour cost

Answer

Use spot preemptible instances to save on compute

Answer

Store training data in Archive Storage to reduce storage costs

Question 8

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

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

Answer

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

Answer

Fine-tune a base LLM on the policy documents monthly

Question 9

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

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

Answer

Fine-tune a base LLM on the policy documents monthly

Question 10

During fine-tuning a model using T-Few in OCI Generative AI, the job fails with a 'dataset format error'. The training dataset is a JSONL file. Which of the following is the MOST likely cause?

Accepted Answer

Some lines have an extra 'metadata' key. Each JSONL line must contain exactly 'prompt' and 'completion' keys. Extra keys or missing keys cause format errors.

Answer

The file contains more than 1000 lines

Answer

The completion field contains more than 1024 tokens

Answer

The file uses Unix line endings

Question 11

A company is building a chatbot using OCI Generative AI service. They want to ensure that the model responses are grounded in their internal knowledge base. Which approach should they use?

Accepted Answer

Retrieval-Augmented Generation (RAG). Retrieval-Augmented Generation (RAG) is the correct approach because it retrieves relevant documents from the company's internal knowledge base at inference time and provides them as context to the LLM, ensuring the model's responses are grounded in verifiable, up-to-date information without modifying the model itself. This directly addresses the requirement to ground responses in an internal knowledge base while avoiding the cost and complexity of retraining.

Answer

Prompt engineering with few-shot examples

Answer

Fine-tuning the model on the internal knowledge base

Answer

Model distillation to compress the knowledge base

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 generates several reasoning chains and aggregates the results, increasing reliability and reducing hallucinations in tasks requiring factual accuracy.

Answer

Few-shot prompting

Answer

Zero-shot prompting

Answer

Chain-of-thought prompting

Question 13

An AI assistant needs to solve complex math word problems step by step. Which prompting technique is most suitable?

Accepted Answer

Chain-of-thought prompting with few-shot examples.. Chain-of-thought prompting with few-shot examples is most suitable because it guides the LLM to break down complex math word problems into intermediate reasoning steps, mimicking human problem-solving. Few-shot examples provide a template for the desired reasoning structure, which significantly improves accuracy on multi-step arithmetic tasks compared to direct answer generation.

Answer

Zero-shot prompting with the problem only.

Answer

Prompting with a high temperature setting.

Answer

Using a model with a larger context window.

Question 14

Which evaluation metric is designed to measure the overlap of n-grams between a generated summary and a reference summary, focusing on recall of content words?

Accepted Answer

ROUGE. ROUGE-N measures n-gram recall (how many n-grams from the reference appear in the generated text). BLEU measures n-gram precision. BERTScore leverages contextual embeddings. Perplexity measures likelihood under the model.

Answer

Perplexity

Answer

BERTScore

Answer

BLEU

Question 15

What is the PRIMARY purpose of the 'stop sequences' parameter in text generation?

Accepted Answer

To define sequences that, when generated, cause the model to cease generation. Stop sequences tell the model when to stop generating further tokens, allowing control over output length or termination upon specific strings.

Answer

To prevent the model from generating offensive content

Answer

To specify tokens that the model should avoid using

Answer

To encourage the model to include specific phrases in the output

Question 16

An organization is deploying multiple generative AI models on a shared dedicated AI cluster. They need to isolate resource usage for each model to avoid interference. Which strategy is recommended?

Accepted Answer

Deploy each model on its own dedicated AI cluster. Deploying each model on its own dedicated AI cluster provides complete hardware-level isolation, ensuring that resource usage (e.g., GPU memory, compute cycles) for one model does not interfere with another. In OCI Generative AI, dedicated AI clusters are single-tenant instances, so each model gets exclusive access to its allocated infrastructure, eliminating contention. This is the recommended strategy for strict isolation in shared environments.

Answer

Use separate fine-tuning jobs for each model

Answer

Configure multiple virtual clusters within the dedicated AI cluster using compartment quotas

Answer

Use OCI Resource Manager to allocate resources

Question 17

A developer uses the ReAct agent in LangChain with a calculator tool and a search tool. The agent receives the question: 'What is the population of Paris multiplied by 3?' The agent first calls the search tool to find the population, then calls the calculator tool to multiply it by 3. Which component is responsible for deciding the sequence of tool calls?

Accepted Answer

The AgentExecutor. The AgentExecutor orchestrates the agent's reasoning loop: it calls the LLM which decides the next action (tool call) based on the prompt (ReAct pattern). The LLM generates actions, the AgentExecutor executes them and feeds results back to the LLM until a final answer is produced. Tools are just functions, the Agent defines the prompt, and Memory stores history.

Answer

The LLM

Answer

The Memory

Answer

The Tool

Question 18

A developer is using LangChain to build a RAG pipeline with Oracle Database 23ai as the vector store. Which LangChain wrapper should they use to create embeddings and store them in the database?

Accepted Answer

OCIGenAIEmbeddings for embeddings and OracleVS for storage. OCIGenAIEmbeddings is the LangChain wrapper for Oracle's embedding models, and OracleVS connects to Oracle Database 23ai for vector storage. The other options either refer to incorrect wrappers or are not designed for this purpose.

Answer

OCIGenAI for embeddings and FAISS for storage

Answer

HuggingFaceEmbeddings for embeddings and OracleVS for storage

Answer

ChatOCIGenAI for embeddings and Chroma for storage

Question 19

A team wants to use an LLM to answer questions about a private codebase that is updated hourly. They cannot afford to fine-tune every hour. Which OCI feature or approach is most suitable?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a vector database. Retrieval-Augmented Generation (RAG) with a vector database is the most suitable approach because it allows the LLM to answer questions about a frequently updated private codebase without retraining. RAG retrieves relevant code snippets from a vector index at query time, ensuring the model always has access to the latest code without the cost and latency of hourly fine-tuning.

Answer

Use a long-context model with full codebase in prompt

Answer

Fine-tune a model on the codebase daily

Answer

Use a smaller model with faster inference

Question 20

A practitioner is using a Cohere Command model on OCI for a translation task. They notice that the output is often incomplete and cuts off mid-sentence. Which parameter should they adjust to address this?

Accepted Answer

Max tokens. The 'Max tokens' parameter controls the maximum length of the generated output. When a model cuts off mid-sentence, it means the token limit has been reached before the model could complete its response. Increasing this value allows the model to generate more tokens, thus completing the translation.

Answer

Temperature

Answer

Frequency penalty

Answer

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