1Z0-1127-25 Practice Test 42 — 15 Questions

Question 1

A security administrator needs to grant a group of data scientists the ability to use OCI Generative AI service resources in the compartment 'genai-dev'. They want to allow the group to create endpoints and run inference, but not to manage IAM policies. Which policy statement is correct?

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

Accepted Answer

Allow group DataScientists to use generative-ai-family in compartment genai-dev. The 'use' verb in OCI IAM policies grants the ability to perform operational actions on resources without allowing management or administrative changes. For the OCI Generative AI service, 'use generative-ai-family' permits data scientists to create endpoints and run inference (the core actions needed), while explicitly preventing them from managing IAM policies or altering resource configurations. This aligns with the principle of least privilege required by the security administrator.

Answer

Allow group DataScientists to manage generative-ai-family in compartment genai-dev

Answer

Allow group DataScientists to inspect generative-ai-family in compartment genai-dev

Answer

Allow group DataScientists to read generative-ai-family in compartment genai-dev

Question 2

A data scientist needs to fine-tune a model on OCI Generative AI. Which of the following is a required parameter in the fine-tuning request?

Accepted Answer

All of the above. In OCI Generative AI, the fine-tuning request requires all three parameters: hyperparameters (to define training behavior like learning rate and epochs), model_name (to specify the base model being fine-tuned), and dataset_type (to indicate the format of the training data, such as 'TEXT' or 'MULTI_TURN'). Therefore, 'All of the above' is correct because each listed option is a mandatory field in the fine-tuning API call.

Answer

hyperparameters

Answer

model_name

Answer

dataset_type

Question 3

A prompt engineer is designing a system that must extract structured data from unstructured text. The model occasionally outputs extra text beyond the required JSON. Which parameter should be adjusted to enforce strict output format?

Accepted Answer

Set a stop sequence to the closing delimiter of the JSON (e.g., '}'). Stop sequences tell the model when to stop generating. By adding a stop sequence like '}' (end of JSON), the model will terminate after the JSON object, preventing extra text.

Answer

Increase the frequency penalty

Answer

Reduce the temperature to 0

Answer

Increase the top-p value

Question 4

An OCI user wants to reduce the cost of running a generative AI model while maintaining output quality. Which THREE strategies can help achieve this?

Accepted Answer

Use a smaller model from the same family. Using a smaller model from the same family (e.g., switching from Llama 3 70B to Llama 3 8B) reduces the number of parameters and computational resources required per inference, directly lowering cost. Smaller models often retain strong performance on many tasks, especially when the task complexity does not demand the full capacity of the larger model, thus maintaining output quality while reducing token processing costs.

Answer

Use greedy decoding instead of sampling

Answer

Increase max tokens to ensure complete answers

Question 5

A company wants to implement a retrieval-augmented generation (RAG) chatbot using OCI Generative AI Agents. Which TWO services or components are required for this solution?

Accepted Answer

Knowledge Base. A Knowledge Base is the core repository that stores the documents or data sources the RAG chatbot retrieves from. OCI Generative AI Agents use a knowledge base to perform retrieval-augmented generation, where the agent first retrieves relevant chunks from the knowledge base and then passes them to the generative model to produce a grounded, context-aware response.

Answer

Cohere Embed model

Answer

Fine-tuned model

Answer

Dedicated AI Cluster

Question 6

A developer is building a RAG pipeline with LangChain. They have loaded PDF documents with PDFLoader. Which TWO steps must they perform before indexing the documents into a vector store?

Accepted Answer

Split the documents into chunks using a text splitter. Documents loaded from PDFs must be split into smaller chunks (using a text splitter) and then embedded into vector representations before they can be indexed in a vector store.

Answer

Translate the documents into English

Answer

Encrypt the documents with AES-256

Answer

Compress the documents with gzip

Question 7

A company is using OCI Generative AI service with a dedicated AI cluster for text generation. They notice that the latency is higher than expected. The cluster is in the Ashburn region, and users are distributed globally. What is the most effective way to reduce latency?

Accepted Answer

Deploy dedicated AI clusters in regions closer to the users. Latency for globally distributed users is primarily driven by network distance and the speed of light. Deploying dedicated AI clusters in regions closer to the users reduces the physical distance data must travel, directly minimizing network round-trip time (RTT). This is the most effective architectural change because OCI's Generative AI service processes each request on the dedicated cluster and cannot bypass geographic latency through software optimizations alone.

Answer

Enable the OCI Generative AI inference optimizer

Answer

Increase the number of nodes in the dedicated AI cluster

Answer

Use a content delivery network (CDN) to cache responses

Question 8

A data scientist is evaluating a summarization model on a news article dataset. They compute ROUGE-L and BLEU scores. The ROUGE-L score is high, but the BLEU score is low. Which of the following best explains this discrepancy?

Accepted Answer

The summaries are very short and use different vocabulary from the reference. ROUGE-L measures the longest common subsequence, emphasizing recall of key content (n-grams in order). BLEU measures n-gram precision, penalizing short or non-fluent outputs. A high ROUGE-L but low BLEU suggests the summaries capture the main ideas but use different phrasing or are too short.

Answer

The BLEU score is unreliable for summarization tasks

Answer

The model is overfitting to the training data

Answer

The summaries are too long and contain many irrelevant n-grams

Question 9

A company wants to use OCI Generative AI Agents for a question-answering system over their internal knowledge base stored in OCI Object Storage. The data consists of PDF and Word documents. What is the first step to make this data usable by the agent?

Accepted Answer

Create a knowledge base and associate it with the Object Storage bucket. A knowledge base must be created and linked to the data source (Object Storage bucket) so the agent can index and retrieve the content. Creating an agent first without a knowledge base would not work. The Embedding API is lower-level; the agent service abstracts this.

Answer

Use the Embedding API to generate embeddings for all documents

Answer

Create a dedicated AI cluster for inference

Answer

Create an OCI Generative AI Agent

Question 10

A startup is building a chatbot for customer support using OCI Generative AI Service. The chatbot needs to answer queries about product features based on a knowledge base of product documentation. Which configuration is most appropriate for this use case?

Accepted Answer

Use a Cohere Command model with the knowledge base as context in a prompt, and enable retrieval-augmented generation (RAG) via OCI Generative AI Agents.. OCI Generative AI Agents with retrieval-augmented generation (RAG) allows the chatbot to dynamically retrieve relevant chunks from the product documentation knowledge base and inject them as context into a Cohere Command model prompt. This approach ensures answers are grounded in the latest documentation without requiring fine-tuning, and it scales efficiently as the knowledge base grows.

Answer

Use the Summarization task type to generate concise answers from the documentation.

Answer

Fine-tune a Llama 2 70B model on the product documentation to create a custom model.

Answer

Use the Code Generation model to produce SQL queries that retrieve answers from a database.

Question 11

Which OCI Generative AI model family is specifically designed to convert text into vector embeddings for semantic search and clustering tasks?

Accepted Answer

Cohere Embed. Cohere Embed models (e.g., embed-english-v3.0, embed-multilingual-v3.0) are explicitly designed for generating text embeddings. Cohere Command R and R+ are for generation, Meta Llama 3 is a general-purpose LLM, and Cohere Rerank is for re-ranking search results.

Answer

Meta Llama 3

Answer

Cohere Command R

Answer

Cohere Rerank

Question 12

A developer is using the OCI Generative AI Chat API with a system prompt to guide the assistant's behavior. They notice that after a few turns, the assistant starts ignoring the system instructions. What is the MOST likely cause?

Accepted Answer

The system prompt (preamble) is only sent once and may be truncated as the conversation grows. The preamble is sent only on the first turn; it may be truncated or lost as conversation history grows. The developer should inject the preamble in each request or manage history carefully.

Answer

The temperature is set too high, causing the model to ignore instructions

Answer

The model is not fine-tuned to follow system prompts

Answer

The conversation history is not being sent with subsequent requests, so the model loses context

Question 13

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 allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining.

Answer

Fine-tune a base LLM on the policy documents monthly

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

Question 14

Which OCI Generative AI model would you use to reorder search results to improve relevance ranking?

Accepted Answer

Cohere Rerank. Cohere Rerank is specifically designed to reorder documents based on relevance to a query. The other models are for generation or embedding.

Answer

Cohere Command R+

Answer

Cohere embed-english-v3.0

Answer

Meta Llama 3 70B

Question 15

Which of the following is NOT an available model in OCI Generative AI service?

Accepted Answer

OpenAI GPT-4. OCI GenAI offers Cohere Command R, Command R+, Meta Llama 3, Cohere Embed models, and Cohere Rerank. GPT-4 is an OpenAI model and is not available in OCI GenAI.

Answer

Cohere Embed English v3.0

Answer

Meta Llama 3

Answer

Cohere Command R+