1Z0-1127-25 Practice Test 9 — 10 Questions

Question 1

Which THREE are essential steps in the prompt engineering process for an LLM?

Accepted Answer

Test the prompt with a variety of input examples. Option A is correct because testing the prompt with a variety of input examples is essential to validate that the prompt generalizes across different cases and to surface edge cases or ambiguities before deployment. Option C is correct because defining the desired output format and constraints (for example, JSON schema, length limits, tone, or required fields) gives the model explicit structure and makes responses consistent and machine-parseable. Option E is correct because prompt engineering is inherently iterative: you evaluate model responses, adjust wording, add examples or constraints, and re-test until the output reliably meets the goal. Options B and D are not part of prompt engineering: fine-tuning on a domain corpus changes model weights through training, and INT8 quantization is a model-compression/deployment optimization, neither of which is an essential step in designing and refining a prompt.

Answer

Fine-tune the model on a domain corpus

Answer

Quantize the model to INT8

Question 2

An LLM is being used to answer customer queries about a product catalog. The answers are fluent but sometimes include plausible-sounding but incorrect product details. What is this phenomenon called, and which technique is most effective to mitigate it?

Accepted Answer

Hallucination; use Retrieval-Augmented Generation (RAG) with the catalog indexed. The phenomenon where an LLM generates fluent but incorrect information is called hallucination. The most effective mitigation for product catalog queries is Retrieval-Augmented Generation (RAG), where the model retrieves relevant information from a trusted knowledge base (the catalog) and uses it to generate answers. This grounds the model's responses in factual data, reducing hallucinations.

Answer

Knowledge cutoff; fine-tune the model on the catalog

Answer

Bias amplification; increase temperature

Answer

Overfitting; reduce the model size

Question 3

What is the primary purpose of chunking documents in a RAG pipeline?

Accepted Answer

To ensure each chunk fits within the model's context window. The primary purpose of chunking documents in a RAG pipeline is to divide large documents into smaller, manageable pieces that each fit within the model's context window. This ensures that the retrieval step can fetch relevant chunks without exceeding the token limit of the LLM, which would otherwise cause truncation or failure to process the input.

Answer

To improve embedding quality

Answer

To speed up training

Answer

To reduce storage costs

Question 4

You are deploying a generative AI solution on OCI for a healthcare client that requires strict data residency (data must remain in the EU) and low-latency inference. The solution uses a fine-tuned LLM model (7B parameters) stored in Object Storage in the Frankfurt region. You have set up an OCI Data Science model deployment endpoint with GPU shape VM.GPU.A10.1, using a single replica. During load testing with 50 concurrent users, you observe high latency (average 8 seconds per request) and occasional 504 gateway timeouts. The model deployment logs show no errors, and the model loads successfully. You have confirmed that the Object Storage bucket is in the same region and that the network latency between the client and the endpoint is minimal (under 5 ms). Which action should you take to reduce latency and eliminate timeouts?

Accepted Answer

Increase the number of replicas to 3 and enable autoscaling based on CPU utilization.. The high latency and 504 timeouts with 50 concurrent users indicate that a single GPU replica is overwhelmed by the request queue. Increasing replicas to 3 distributes the load across multiple endpoints, while enabling autoscaling based on CPU utilization ensures dynamic scaling to handle traffic spikes. This directly reduces per-request latency and eliminates timeouts without violating data residency requirements.

Answer

Increase the model deployment endpoint timeout setting from 60 seconds to 300 seconds in the OCI console.

Answer

Upgrade the model deployment shape to VM.GPU.A100.4 and keep a single replica.

Answer

Move the model deployment to the US East (Ashburn) region to leverage lower-cost GPU capacity and reduce latency.

Question 5

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) (C) is correct because it measures how many inference requests the generative AI endpoint is actually serving per unit time, directly reflecting utilization and capacity of the deployment; low or saturated throughput signals inefficiency or a bottleneck. Average inference latency (D) is correct because it captures the time taken to return a model response, and monitoring it (e.g., via OCI Monitoring metrics on the model deployment endpoint) reveals whether the deployment meets performance expectations or is degraded by resource contention. Together these two operational metrics indicate whether the generative AI service is processing workload efficiently. Number of active users (A) is a usage/adoption metric, not a direct efficiency measure of the deployment. Object Storage bucket size (B) reflects storage consumption, which is unrelated to inference efficiency. Model accuracy on validation set (E) is a model-quality metric measured during training/evaluation, not a runtime efficiency metric.

Answer

Number of active users

Answer

Object Storage bucket size

Answer

Model accuracy on validation set

Question 6

An organization is deploying a large language model on OCI using a dedicated AI cluster. They need to minimize inference latency. Which configuration step is most critical?

Accepted Answer

Configure the cluster to use high-bandwidth RDMA networking. RDMA (Remote Direct Memory Access) bypasses the CPU and kernel to transfer data directly between GPU memories, drastically reducing latency and CPU overhead. In a dedicated AI cluster on OCI, high-bandwidth RDMA networking (e.g., using RoCE v2 or InfiniBand) is the most critical step to minimize inference latency because model parallelism and tensor parallelism across nodes depend on fast, low-latency interconnects. Without RDMA, even with optimized model parallelism, the network becomes the bottleneck, increasing per-token latency.

Answer

Set up a load balancer across multiple regions

Answer

Use a single VM shape to reduce network hops

Answer

Disable model parallelism to simplify setup

Question 7

Refer to the exhibit. What is the primary reason the response is incomplete?

Accepted Answer

The max-tokens limit is too low.. The response is incomplete because the max-tokens limit is too low, causing the model to truncate its output before completing the full answer. When the token budget is exhausted, the generation stops mid-sentence or mid-thought, leaving the response unfinished regardless of prompt length or other parameters.

Answer

The temperature is not set.

Answer

The model-id is incorrect.

Answer

The prompt is too short.

Question 8

A developer observes that their LangChain RAG pipeline sometimes returns duplicate or near-duplicate documents in the retrieved set, reducing the diversity of information provided to the LLM. Which retrieval parameter should they adjust to improve diversity?

Accepted Answer

Use MMR (maximum marginal relevance) as the search type. MMR (Maximum Marginal Relevance) is a retrieval algorithm that balances relevance with diversity by re-ranking documents based on their similarity to the query and their dissimilarity to already-selected documents. By adjusting the lambda parameter, you control the trade-off between relevance and diversity, directly addressing the problem of duplicate or near-duplicate documents in the retrieved set.

Answer

Increase the similarity score threshold

Answer

Increase chunk_overlap in the text splitter

Answer

Reduce chunk_size in the text splitter

Question 9

A team is building a conversational chatbot using LangChain and OCI Generative AI. They want to maintain a summary of the conversation rather than storing the entire history, to keep within token limits. Which memory class should they use, and what additional step is required when initializing the memory?

Accepted Answer

ConversationSummaryMemory; provide an LLM to generate summaries. ConversationSummaryMemory is designed to maintain a running summary of the conversation instead of storing the full history, which directly addresses the requirement to stay within token limits. The additional step required is providing an LLM (e.g., via `llm=ChatOpenAI(...)`) because the memory class uses the LLM to generate and update the summary dynamically.

Answer

ConversationBufferWindowMemory; set a window size

Answer

ConversationTokenBufferMemory; set a token limit

Answer

ConversationBufferMemory; no additional step

Question 10

Refer to the exhibit. In this RAG pipeline, what is the role of the 'embedding_model' variable?

Accepted Answer

It converts text into vector representations for similarity search.. In a Retrieval-Augmented Generation (RAG) pipeline, the 'embedding_model' variable is responsible for converting input text (such as user queries or document chunks) into dense vector representations. These vectors are then used to perform similarity search against a vector database, enabling the retrieval of the most relevant context for the generative model. This is a core function of the embedding step in RAG, not a guardrail, fine-tuning, or text generation role.

Answer

It applies guardrails to filter content.

Answer

It fine-tunes the model on the provided texts.

Answer

It generates text completions based on prompts.