1Z0-1127-25 Practice Test 33 — 15 Questions

Question 1

A multi-turn chatbot needs to maintain context across user queries. The context window is limited. What design should be used?

Accepted Answer

Use a summary of previous turns and add new input.. Summarizing previous turns and appending the new input efficiently manages the limited context window of large language models (LLMs). This approach preserves essential conversational context without exceeding token limits, ensuring coherent multi-turn interactions.

Answer

Store context in a separate database and retrieve each time.

Answer

Reset context after each turn.

Answer

Keep the entire conversation history in each request.

Question 2

You want to test different prompts and parameters (temperature, max tokens) for a summarization task using a foundation model without writing any code. Which OCI tool should you use?

Accepted Answer

OCI Generative AI Playground. The OCI Generative AI Playground provides an interactive UI to experiment with models and parameters. CLI, SDK, and console navigation are not for interactive experimentation.

Answer

OCI Console -> Generative AI -> Models

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OCI CLI with the 'oci generative-ai' commands

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Python SDK with InferenceClient

Question 3

A team is building a RAG pipeline on OCI. Which THREE steps are essential components of a standard RAG pipeline? (Select THREE)

Accepted Answer

Embedding each chunk into a dense vector using an embedding model. Option E is correct because chunking documents into passages is the foundational ingestion step in a RAG pipeline, splitting source content into retrievable units that fit the embedding model's context window. Option A is correct because each chunk must be converted into a dense vector via an embedding model so it can be indexed and compared semantically in vector space. Option B is correct because retrieval ranks stored chunk vectors against the query embedding, typically using cosine similarity, to fetch the most relevant context for the generator. Option C is not part of a standard RAG pipeline, since RAG augments a pre-existing LLM rather than pretraining a custom model from scratch on the corpus. Option D is also unnecessary, as RAG supplies retrieved chunks at inference time through the prompt context and does not require fine-tuning the generation model on those chunks.

Answer

Training a custom LLM from scratch on the document corpus

Answer

Fine-tuning the generation model on the retrieved chunks

Question 4

A company has deployed a fine-tuned GPT model on OCI Generative AI using a dedicated AI cluster with 2 nodes. The endpoint is used by an internal application that generates product descriptions. Recently, the application started receiving timeouts and slow responses. The monitoring dashboard shows that the cluster's CPU utilization is consistently above 90%, and the request queue is growing. The team has verified that the model and code have not changed. The application traffic has increased by 20% over the past month. What should the team do to resolve the issue?

Accepted Answer

Increase the number of nodes in the dedicated AI cluster from 2 to 4.. The dedicated AI cluster with 2 nodes is experiencing sustained CPU utilization above 90% and a growing request queue due to a 20% increase in traffic. Scaling out the cluster by adding more nodes (from 2 to 4) increases the available compute capacity, allowing the cluster to handle the higher inference load without timeouts. This directly addresses the resource bottleneck without requiring code or model changes.

Answer

Switch to a serverless endpoint to handle variable traffic.

Answer

Reduce the batch size in the inference requests to lower CPU usage.

Answer

Implement a caching layer for frequently requested descriptions.

Question 5

An enterprise deployed a custom fine-tuned model for generating financial reports. After the first month, the model's outputs began to include outdated information and occasional factual errors. The team suspects data drift. What is the best course of action?

Accepted Answer

Retrain the model on the latest financial data and monitor for drift.. Data drift occurs when the statistical properties of the input data change over time, causing a fine-tuned model's learned patterns to become stale. The only way to restore accuracy is to retrain the model on current, representative financial data and then continuously monitor for further drift. This directly addresses the root cause rather than masking the symptom.

Answer

Switch to a newer base model like Llama 3.1 without retraining.

Answer

Decrease the temperature parameter to 0.1 to reduce model creativity.

Answer

Increase the max tokens value to allow longer responses.

Question 6

A team is building a LangChain agent that needs to answer questions using both a company-internal knowledge base (stored in Oracle AI Vector Search) and live web search. Which THREE components should they include in the agent setup?

Accepted Answer

AgentExecutor to manage the ReAct loop. A LangChain agent requires a set of tools (vector store retriever as a tool, web search tool), and an AgentExecutor to orchestrate the reasoning and tool calls. The LLM is already part of the agent definition.

Answer

ConversationBufferMemory for storing chat history

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A custom tool for logging agent steps

Question 7

A developer notices that an LLM occasionally generates harmful or biased responses despite a system prompt instructing it to be safe. Which technique can help mitigate this at inference time without retraining?

Accepted Answer

Add a detailed system prompt with explicit safety constraints and use content filtering if available. Adding a detailed system prompt with explicit safety constraints, combined with content filtering if the platform provides it, is the correct inference-time mitigation because it requires no retraining and directly addresses harmful outputs at generation time. System prompts set behavioral guardrails, and content filters provide a second layer that catches violations the prompt alone misses. This defense-in-depth approach is the standard no-retraining mitigation.

Answer

Increase the top-p value to 0.95

Answer

Use a higher temperature to encourage safer outputs

Answer

Fine-tune the model on a curated safe dataset

Question 8

Which TWO of the following are common prompt failures?

Accepted Answer

Ambiguous instructions. Option B (Ambiguous instructions) is correct because vague or underspecified prompts leave the model guessing at intent, scope, or constraints, which commonly produces off-target or inconsistent outputs—this is a classic prompt failure mode. Option C (Prompt injection vulnerabilities) is correct because untrusted input can override or subvert the intended instructions, causing the model to ignore the system prompt or leak data, which is a well-known prompt-level failure. Option A is not a common failure—providing too many examples is usually a token/cost concern, and few-shot examples generally improve accuracy. Option D is not a failure—using the system prompt to set persona is a standard, recommended technique for steering behavior. Option E is not a failure—specifying JSON output is a valid formatting instruction that improves structure, though it may require schema guidance to be reliable.

Answer

Providing too many examples

Answer

Using system prompt to set persona

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Specifying output format as JSON

Question 9

An organization stores its knowledge base in Oracle Autonomous Database and wants to build a RAG chatbot using OCI Generative AI. The chatbot must retrieve the most relevant documents based on user queries. Which indexing approach is BEST suited for efficient similarity search on text embeddings?

Accepted Answer

Create an ANN index on the embedding vector column.. Approximate Nearest Neighbor (ANN) indexes are specifically designed for high-dimensional vector spaces, enabling efficient similarity search on embedding vectors. In Oracle Autonomous Database, ANN indexes (e.g., using IVF or HNSW algorithms) drastically reduce search latency compared to brute-force scans, which is critical for real-time RAG chatbot responses.

Answer

Create a bitmap index on the embedding vector column.

Answer

Create an inverted index on the document text column.

Answer

Create a B-tree index on the document text column.

Question 10

A team wants to use OCI Generative AI Agents to build a RAG system that answers questions from documents stored in OCI Object Storage. What must they create first?

Accepted Answer

A knowledge base that indexes the documents from Object Storage. The agent requires a knowledge base that is connected to the data source. Directly creating a session without a knowledge base and agent will fail.

Answer

A fine-tuned model on the documents

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A dedicated AI cluster for the agent

Answer

An OCI Functions endpoint to process documents

Question 11

A company's RAG application ingests news articles that are updated frequently. The vector store in OCI OpenSearch contains embeddings of the articles. The team notices that outdated information is still retrieved even after updating the source documents. What is the most effective way to ensure the vector store reflects the latest content?

Accepted Answer

Use the OCI OpenSearch document update API to replace embeddings for changed documents. The most effective way to ensure the vector store reflects the latest content is to update the embeddings for changed documents using the document update API. This replaces outdated vectors with new ones, ensuring retrieval returns current information.

Answer

Increase the TTL for vector indices

Answer

Rely on the LLM to ignore outdated information

Answer

Re-index the entire vector store daily

Question 12

A document processing pipeline uses OCI Document Understanding to extract text from PDFs, then creates embeddings with OCI Generative AI. Some documents exceed the embedding model's token limit. What is the best approach?

Accepted Answer

Split the document into chunks that fit the limit and embed each chunk separately. Splitting documents into chunks that fit within the embedding model's token limit ensures that no information is lost while still allowing each chunk to be embedded and indexed separately. This approach is standard in RAG pipelines, where documents are chunked to balance token limits and retrieval granularity, enabling the system to retrieve relevant chunks rather than entire documents.

Answer

Truncate the document to the token limit

Answer

Use a different embedding model with a higher token limit

Answer

Skip documents that exceed the limit

Question 13

Which LangChain memory type is best suited for a long-running conversation where token consumption must be minimized, and the gist of previous exchanges should be retained?

Accepted Answer

ConversationSummaryMemory. ConversationSummaryMemory (D) is best suited for long-running conversations where token consumption must be minimized because it periodically summarizes the conversation history, retaining the gist of previous exchanges in a compressed form. This avoids storing every raw message (as in BufferMemory) while still preserving context, making it ideal for cost-sensitive or token-limited LLM deployments.

Answer

ConversationBufferMemory

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VectorStoreMemory

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ConversationBufferWindowMemory

Question 14

A data scientist wants to generate a concise summary of a long legal document. The model should output a bullet list of key points. Which prompt component is LEAST important for this task?

Accepted Answer

Few-shot examples of summaries. Few-shot examples are the least important component here because the task — summarizing a legal document into bullet points — is a straightforward, well-understood instruction that modern LLMs can execute reliably from a clear task description alone. The context (the document itself) is essential input, the output format specification ensures the bullet-list structure, and the task instruction defines the objective. Few-shot examples add token overhead and complexity without materially improving output quality for such a simple, explicit task.

Answer

Context/background (the legal document text)

Answer

Output format specification ('Output as a bullet list')

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Task instruction ('Summarize the following legal document in bullet points')

Question 15

A company is deploying a generative AI model for a real-time inference API. To ensure high availability and cost efficiency under variable load, which two configurations should they implement? (Choose two.)

Accepted Answer

Set the number of model deployment replicas to at least 2. Option D is correct because setting the number of model deployment replicas to at least 2 eliminates a single point of failure and allows the inference API to keep serving traffic if one replica or its underlying host fails, which directly satisfies the high availability requirement. Option E is correct because enabling autoscaling based on average CPU utilization lets the deployment add replicas when inference load rises and remove them when load falls, providing cost efficiency under variable traffic while maintaining performance. Option A is incorrect because a single replica, even with a larger GPU, remains a single point of failure and cannot scale horizontally, so it fails the high availability goal. Option B is incorrect because confining the deployment to a single availability domain means an AD-level outage takes down the entire inference API. Option C is incorrect because disabling connection draining causes in-flight inference requests to be dropped abruptly during scaling or maintenance events, degrading availability and reliability.

Answer

Use a single replica with a larger GPU to handle all traffic

Answer

Deploy the model in a single availability domain to simplify management

Answer

Disable connection draining on the load balancer