1Z0-1127-25 Practice Test 38 — 15 Questions

Question 1

Which two metrics would you monitor to ensure a generative AI deployment on OCI is operating efficiently? (Choose two.)

Accepted Answer

Request throughput (requests per second). Request throughput (requests per second) is a critical metric for monitoring the operational efficiency of a generative AI deployment on OCI because it directly measures the system's capacity to handle incoming inference requests. If throughput drops below expected levels, it indicates a bottleneck in the compute resources (e.g., GPU utilization) or the serving infrastructure, which can lead to degraded user experience and potential timeouts.

Answer

Number of active users

Answer

Object Storage bucket size

Answer

Model accuracy on validation set

Question 2

A prompt engineer is designing a system prompt for a customer support agent. The goal is to ensure the agent always responds in a polite, professional tone and never provides medical advice. Which approach best achieves this?

Accepted Answer

Use a system prompt that includes both the desired tone and the constraint about medical advice. System prompt is designed to set persona, tone, and constraints. Including both tone and constraints in the system prompt is the intended practice.

Answer

Set the temperature to 0 to ensure consistent tone and hope it avoids medical topics

Answer

Specify tone in the system prompt and add the medical advice restriction in each user message

Answer

Rely on the model's inherent training to avoid medical advice, so no explicit instruction needed

Question 3

An organization wants to deploy a generative AI chatbot using OCI Generative AI service. The chatbot must comply with data residency requirements by ensuring that all data processing occurs within a specific geographic region. What is the best practice to achieve this?

Accepted Answer

Use a dedicated AI cluster in the required region. OCI Generative AI service allows you to provision a dedicated AI cluster within a specific region, ensuring all model inference and data processing remain within that geographic boundary. This dedicated cluster is isolated from other regions and complies with data residency requirements by design, as no data leaves the chosen region during processing.

Answer

Enable cross-region replication for disaster recovery

Answer

Configure a tenancy-wide policy to restrict region usage

Answer

Use IAM policies to block access from other regions

Question 4

A developer is using the OCI Generative AI service API and receives a '400 Bad Request' with error 'Model not found'. What is the most likely cause?

Accepted Answer

The model ID is misspelled or does not exist.. The '400 Bad Request' with 'Model not found' error indicates that the model ID provided in the API request does not match any available model in the OCI Generative AI service. This is most commonly caused by a typo, incorrect model name, or referencing a model that has been deprecated or removed from the service catalog.

Answer

The API request lacks authentication.

Answer

The input exceeds the maximum token limit.

Answer

The endpoint region is incorrect.

Question 5

A company wants to deploy a custom fine-tuned model for retrieval-augmented generation (RAG) using dedicated AI cluster. They need to ensure the model can handle concurrent requests from multiple applications with consistent latency. What should they configure?

Accepted Answer

Increase the number of replicas in the dedicated cluster.. Increasing the number of replicas in the dedicated cluster distributes the load across multiple model copies, improving concurrency and latency stability. This allows the model to handle concurrent requests from multiple applications with consistent latency.

Answer

Set a high temperature to keep responses concise.

Answer

Enable auto-scaling on the cluster.

Answer

Use the managed serving endpoint instead.

Question 6

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 7

Which TWO are valid methods to monitor the performance of a generative AI model deployed on OCI Data Science?

Accepted Answer

Use OCI Monitoring service to track custom metrics like latency and throughput. OCI Monitoring service allows you to define and track custom metrics such as inference latency (e.g., p50/p99 response times) and throughput (requests per second) for your generative AI model deployed on OCI Data Science. This enables real-time performance monitoring and alerting based on thresholds you set, which is essential for production AI workloads.

Answer

Use OCI Notifications to receive alerts on model drift

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Use OCI Events service to trigger retraining on low accuracy

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Use OCI Audit service to review API call logs

Question 8

In LangChain, which component is responsible for loading data from a specific file format, such as PDF or CSV, into a document object?

Accepted Answer

Document Loader. Document Loader is the correct component because it is specifically designed to ingest data from various file formats (e.g., PDF, CSV, HTML) and convert it into LangChain's standardized Document objects. This is the foundational step in any retrieval-augmented generation (RAG) pipeline, where raw data must first be loaded before any splitting, embedding, or retrieval occurs.

Answer

Text Splitter

Answer

Vector Store

Answer

Retriever

Question 9

Which of the following is a known limitation of large language models where the model generates plausible-sounding but factually incorrect information?

Accepted Answer

Hallucination. Hallucination is the term used when an LLM produces content that is not grounded in training data or provided context, appearing confident but being incorrect.

Answer

Bias

Answer

Context window limitation

Answer

Knowledge cutoff

Question 10

A company is using dedicated AI cluster for fine-tuning. Which TWO best practices help optimize cost?

Accepted Answer

Manually scale down the cluster when not in use.. Manually scaling down the dedicated AI cluster when not in use directly reduces compute costs by stopping idle GPU/CPU resources. In OCI Generative AI, dedicated AI clusters incur charges for provisioned capacity, so scaling down during inactivity avoids paying for unused infrastructure.

Answer

Use the largest replica count.

Answer

Use the managed serving endpoint instead.

Answer

Leave the cluster running continuously.

Question 11

A developer wants to call the OCI Generative AI service from a Python application running on an OCI Compute instance. Which method is the most secure for authenticating the API calls?

Accepted Answer

Use instance principals with a dynamic group and policy. Instance principals allow the Compute instance to authenticate to OCI services without storing any credentials on the instance. By assigning a dynamic group and policy, the instance obtains a temporary security token from the OCI metadata service, which is the most secure method for programmatic access from within OCI.

Answer

Use a resource principal

Answer

Use the OCI CLI with a config file containing credentials

Answer

Use an API signing key stored on the instance

Question 12

Which three factors most significantly affect the quality of an LLM's output? (Select THREE)

Accepted Answer

Clarity of the prompt. The clarity of the prompt directly determines how well the LLM interprets the user's intent. A well-structured, unambiguous prompt reduces ambiguity and guides the model toward generating relevant and coherent responses, while a vague or poorly worded prompt often leads to off-target or nonsensical output.

Answer

Model's context window size

Answer

Number of GPUs used during inference

Question 13

Which TWO are advantages of using retrieval-augmented generation (RAG) over fine-tuning for incorporating new knowledge?

Accepted Answer

Enables the model to access up-to-date information without retraining. RAG retrieves relevant, up-to-date information from an external knowledge base at inference time, allowing the model to answer questions about recent events or proprietary data without requiring any retraining. This is a key advantage over fine-tuning, which would need a new training cycle to incorporate the same new knowledge.

Answer

Better at capturing domain-specific writing style

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Eliminates the need for a vector database

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Reduces token usage and latency compared to fine-tuning

Question 14

Which TWO of the following are valid ways to reduce latency when using OCI Generative AI Service?

Accepted Answer

Use a dedicated AI cluster. A dedicated AI cluster provides isolated compute resources (GPU nodes) for inference, eliminating resource contention from other tenants or workloads. This ensures consistent low-latency responses because the model is always warm and available without queueing delays, which is critical for real-time applications.

Answer

Deploy the model in a different region

Answer

Use a larger model

Answer

Batch multiple requests

Question 15

A team is using OCI Generative AI Agents to build a customer support bot. The bot sometimes generates answers that contradict the knowledge base. What is the most likely cause?

Accepted Answer

The chunking strategy for the knowledge base does not capture enough context overlap.. When the chunking strategy lacks sufficient context overlap, the retrieved chunks may omit critical surrounding information, causing the generative AI model to infer missing details incorrectly and produce answers that contradict the knowledge base. In OCI Generative AI Agents, the chunking strategy determines how documents are split into smaller pieces for retrieval; without adequate overlap, the model loses the semantic continuity needed to stay faithful to the source material.

Answer

The max tokens value is too low, truncating the response.

Answer

The temperature parameter is set too high, causing the model to hallucinate.

Answer

The model's repetition penalty is too high.