1Z0-1127-25 Practice Test 20 — 15 Questions

Question 1

A developer implements a RAG chatbot using OCI Generative AI with streaming enabled. The chatbot fails to remember earlier conversation turns during a session. What is the most likely cause?

Accepted Answer

The application does not include previous messages in the request.. A RAG chatbot with streaming enabled still requires the application to manage conversation state by including previous messages in each request. The OCI Generative AI streaming endpoint processes each request independently; without explicitly passing the conversation history, the model has no context of prior turns, causing it to fail to remember earlier interactions.

Answer

The max_tokens parameter is set too low.

Answer

The streaming endpoint does not support conversation history.

Answer

The temperature parameter is too high.

Question 2

Refer to the exhibit. A user in GenAI-Users group tries to run a text generation inference but gets permission denied. What is the most likely issue?

Accepted Answer

The operation condition is too restrictive.. The policy attached to the GenAI-Users group includes a condition that restricts the operation to a specific compartment or resource, but the user is attempting to run inference in a different compartment or without meeting the condition. Since the condition is too restrictive, the IAM policy denies the action even though the user is in the correct group and the resource type is valid.

Answer

The policy resource type is wrong.

Answer

The group name mismatch.

Answer

The user is not in the compartment.

Question 3

A developer sends this request but receives an error: "modelId not found". Which is the most likely cause?

Accepted Answer

The modelId "cohere.command" is deprecated.. The error 'modelId not found' indicates that the model identifier specified in the request does not exist or is no longer available. In Oracle Cloud Infrastructure (OCI) Generative AI service, model IDs like 'cohere.command' are deprecated and replaced with newer versions (e.g., 'cohere.command-r'). Using a deprecated modelId causes the service to reject the request because it cannot locate a valid model for that identifier.

Answer

The temperature parameter is out of range.

Answer

The compartment ID is incorrect.

Answer

The presencePenalty parameter is misspelled.

Question 4

Which THREE factors should be considered when choosing a model for a summarization task using OCI Generative AI?

Accepted Answer

Number of parameters.. Option B (Number of parameters) matters because parameter count drives the model's capacity to capture language patterns and its trade-off between output quality and inference cost/latency, which directly affects summarization quality. Option D (Context window size) is critical because summarization requires ingesting the full source document; if the input exceeds the model's token context window, the text must be truncated or chunked, degrading the summary. Option E (Supported languages) must be considered because OCI Generative AI models differ in language coverage, and a model must support the language of the source text to produce a coherent summary. Option A (Inference endpoint location) is not a model-selection factor for summarization quality; endpoints are a deployment concern, not a model capability. Option C (Model training data cut-off date) is not a primary selection criterion for summarization, since the task is text transformation rather than knowledge retrieval, so recency of training data does not determine summarization suitability.

Answer

Inference endpoint location.

Answer

Model training data cut-off date.

Question 5

A developer needs to include the conversation history in a prompt for a LangChain chatbot. They want to insert previous exchanges between the user and the AI into the prompt at a specific position. Which component should they use?

Accepted Answer

MessagesPlaceholder. MessagesPlaceholder is the correct component because it is specifically designed to inject a list of messages (e.g., conversation history) at a designated position within a ChatPromptTemplate. It acts as a slot that gets replaced with the actual message list at runtime, allowing the developer to control exactly where previous exchanges appear in the prompt.

Answer

PromptTemplate

Answer

ChatPromptTemplate

Answer

ConversationBufferMemory

Question 6

A team is deploying an LLM-based application that must adhere to strict data residency requirements. All processing must occur within a specific OCI region. Which OCI service should they use to host and serve the LLM?

Accepted Answer

OCI Data Science with a model deployment endpoint. OCI Data Science with a model deployment endpoint is the correct choice because it provides a managed infrastructure for hosting and serving LLMs within a specific OCI region, ensuring all processing and data remain within that region to meet strict data residency requirements. The model deployment endpoint runs on dedicated compute resources in the chosen region, allowing low-latency inference while adhering to regional data boundaries.

Answer

OCI Streaming

Answer

OCI Functions

Answer

OCI Object Storage

Question 7

An enterprise is using OCI Generative AI with a RAG architecture. They observe that the LLM sometimes produces hallucinated answers that are not supported by the retrieved documents. Which strategy is most effective in reducing these hallucinations?

Accepted Answer

Provide clear instructions in the system prompt to answer only based on the provided context.. Explicitly instructing the LLM to answer only based on the provided context directly addresses the root cause of hallucinations in a RAG pipeline: the model's tendency to rely on its parametric knowledge rather than the retrieved documents. This system prompt acts as a behavioral constraint, forcing the model to ground its responses in the supplied context, which is the most effective and widely recommended mitigation strategy.

Answer

Increase the temperature parameter to make outputs more focused.

Answer

Use a smaller LLM to reduce model capacity.

Answer

Retrieve more chunks (increase top-k) to provide more context.

Question 8

A company wants to build a RAG application using OCI Generative AI Agents. Which TWO components are required to set up the agent?

Accepted Answer

A knowledge base. In OCI Generative AI Agents, a knowledge base (option B) is a required component because it stores the vectorized content that the agent retrieves from during RAG (retrieval-augmented generation) to ground its responses. A data source such as OCI Object Storage (option D) is also required, since the knowledge base must ingest documents from a source to build its index; without ingested data there is nothing to retrieve. An embedding model (option A) is used internally by the service to vectorize the data, but it is not a separately provisioned component the customer must set up, so it is not one of the two required setup components. A dedicated AI cluster (option C) is not needed, as OCI Generative AI Agents is a managed service that does not require provisioning dedicated infrastructure. A fine-tuned model (option E) is optional and unrelated to the basic RAG setup, since the agent can use a base model with retrieval instead of fine-tuning.

Answer

An embedding model

Answer

A dedicated AI cluster

Answer

A fine-tuned model

Question 9

A company wants to reduce costs for a high-volume, latency-tolerant text generation workload using OCI Generative AI. Which TWO strategies should they consider?

Accepted Answer

Use on-demand (pay-per-token) pricing instead of a dedicated AI cluster. Option A is correct because on-demand pay-per-token pricing in OCI Generative AI charges only for tokens consumed, which is cost-effective for high-volume but latency-tolerant workloads that don't require guaranteed dedicated capacity. Option E is correct because smaller models such as Cohere Command R consume fewer resources and cost less per token than larger models like Command R+, directly reducing cost for a workload that tolerates latency and doesn't demand maximum quality. Option B is not appropriate because a dedicated AI cluster incurs fixed provisioning costs aimed at consistent throughput, which is unnecessary and more expensive for a latency-tolerant workload. Option C is incorrect because temperature controls randomness of output, not token consumption, so raising it does not reduce cost. Option D is incorrect because using the largest model increases cost per token and is not a cost-reduction strategy.

Answer

Provision a dedicated AI cluster for consistent throughput

Answer

Increase the temperature parameter to reduce token usage

Answer

Use the largest available model to ensure best quality

Question 10

A startup wants to quickly prototype a chatbot using OCI Generative AI service. They have no prior experience with OCI. They want to test different models and parameters without writing any code and within a few minutes. They also want to save prompts and compare results. Which approach should they use?

Accepted Answer

Use the OCI Generative AI Playground.. The OCI Generative AI Playground is a no-code, web-based interface that allows users to quickly test different models and parameters, save prompts, and compare results without writing any code or having prior OCI experience. It directly meets the startup's requirement for rapid prototyping within minutes.

Answer

Create a dedicated AI cluster and use the OCI SDK.

Answer

Use OCI Data Science Notebooks with the GenAI SDK.

Answer

Use OCI Functions to invoke the GenAI API.

Question 11

A data scientist is fine-tuning a model on OCI Generative AI with a custom dataset. They receive a "QuotaExceeded" error during training. What is the most likely cause?

Accepted Answer

Exceeded the training compute unit quota. The 'QuotaExceeded' error during fine-tuning on OCI Generative AI specifically indicates that the training job has consumed more compute units than allocated in the service limit. Fine-tuning requires dedicated training compute units (TCUs) which are a separate quota from inference or API calls. When this quota is exhausted, the service rejects new training jobs with this error.

Answer

Exceeded the API call rate limit

Answer

Exceeded the model storage limit

Answer

Exceeded the data transfer out limit

Question 12

In LangChain's Expression Language (LCEL), what does the pipe (|) operator do when connecting components?

Accepted Answer

It passes the output of the left component as input to the right component. In LCEL, the | operator chains components by passing the output of the left component as input to the right component. This allows composing chains like prompt | model | output parser.

Answer

It conditionally selects one of the two components based on a flag

Answer

It repeats the left component's execution until a condition is met

Answer

It runs both components in parallel and merges their outputs

Question 13

A company is deploying a customer-facing chatbot using OCI Generative AI. They need to prevent the model from generating offensive or harmful content. Which feature should they implement?

Accepted Answer

Enable OCI Generative AI guardrails with content filtering.. OCI Generative AI guardrails provide built-in content filtering that automatically detects and blocks offensive or harmful content in both user inputs and model outputs. This is the recommended, native feature for safety compliance, eliminating the need for custom post-processing and ensuring consistent policy enforcement across all chatbot interactions.

Answer

Custom post-processing to scan each response.

Answer

Limit the user input length.

Answer

Use a smaller model that is less capable of generating harm.

Question 14

A team wants to use a prompt pattern that combines reasoning with acting, where the model alternates between thinking and taking actions (e.g., tool calls). Which pattern fits this requirement?

Accepted Answer

ReAct pattern. The ReAct pattern (Reason + Act) prompts the model to output reasoning steps and then perform actions (like API calls), enabling interactive problem-solving.

Answer

Template pattern

Answer

Role prompting

Answer

Recipe pattern

Question 15

A developer is building a RAG application with LangChain and Oracle AI Vector Search. They need to choose a text splitter that respects semantic boundaries (e.g., paragraphs, sentences) and a vector store that supports transactional updates in Oracle Database 23ai. Which TWO options should they select?

Accepted Answer

OracleVS. Option E, RecursiveCharacterTextSplitter, is correct because it recursively splits text using an ordered list of separators (defaulting to paragraph breaks, then newlines, spaces, and finally characters), which preserves semantic boundaries like paragraphs and sentences—exactly what the developer needs. Option A, OracleVS, is correct because it is LangChain's vector store integration for Oracle Database 23ai, which supports transactional DML operations (INSERT/UPDATE/DELETE with commit/rollback) on vector data, satisfying the transactional-update requirement. Option B, FAISS, is not appropriate because it is an in-memory similarity-search library without transactional update semantics tied to Oracle Database 23ai. Option C, CharacterTextSplitter, splits on a single fixed separator and does not recursively respect paragraph/sentence structure, so it fails the semantic-boundary requirement. Option D, TokenTextSplitter, splits by token count rather than semantic boundaries, so it also does not meet the requirement.

Answer

FAISS

Answer

CharacterTextSplitter

Answer

TokenTextSplitter