1Z0-1127-25 Practice Test 4 — 25 Questions

Question 1

A developer wants to use the OCI Generative AI service to generate text using a Cohere model. Which SDK class should be used for inference calls?

Accepted Answer

GenerativeAiInferenceClient. The correct SDK class for inference calls with Cohere models in OCI Generative AI is `GenerativeAiInferenceClient`. This class is specifically designed to handle inference operations such as text generation, summarization, and chat completions, and it supports both Cohere and Meta Llama models. It provides methods like `generate_text`, `summarize_text`, and `chat` that directly interact with the inference endpoints.

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CustomModelClient

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AiInferenceClient

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GenerativeAiClient

Question 2

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. Option B is correct because RAG decouples knowledge from model weights: at inference time the system retrieves relevant documents from an external index and injects them into the prompt, so the model can answer with current information without any retraining or weight updates. Option E is correct because RAG only requires updating the underlying document store/vector index when the corpus changes, avoiding the expensive GPU compute and repeated training runs that fine-tuning would demand for large, frequently changing corpora. Option A is not correct because domain-specific writing style is typically learned through fine-tuning on in-domain text, not through retrieval of facts. Option C is not correct because RAG actually depends on a vector database (or similar retrieval index) to embed and search documents. Option D is not correct because RAG adds a retrieval step and inserts retrieved passages into the context, which generally increases token usage and latency rather than reducing them.

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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 3

When invoking the OCI Generative AI service from a RAG application, the developer receives a 401 Unauthorized error. The application uses resource principal authentication from an OCI Data Science notebook session. What is the most likely fix?

Accepted Answer

Ensure the dynamic group includes the data science notebook session and has the correct policy. The 401 Unauthorized error indicates that the OCI Generative AI service cannot authenticate the request. In a RAG application using resource principal authentication from an OCI Data Science notebook session, the notebook session must be a member of a dynamic group, and that dynamic group must have an IAM policy granting it the necessary permissions (e.g., `allow dynamic-group to use generative-ai-family in compartment `). Option C correctly identifies that the dynamic group configuration or policy is missing or incorrect, which is the most likely cause of the authentication failure.

Answer

Add the Generative AI service to the subnet's security list

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Use an API key instead of resource principal

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Restart the notebook session

Question 4

A company wants to build a sentiment analysis system for customer reviews. They have a labeled dataset of 10,000 reviews. Which approach is most cost-effective and likely to yield good performance?

Accepted Answer

Fine-tune a pre-trained BERT model on the labeled dataset. Fine-tuning a pre-trained encoder-only model like BERT on the labeled dataset is a standard approach for classification tasks, offering good performance with relatively modest data and compute.

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Use GPT-4 with a prompt and no fine-tuning

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Use a simple bag-of-words model with logistic regression

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Train a Transformer model from scratch on the reviews

Question 5

A team has set up a RAG pipeline using OCI Data Science with OCI OpenSearch as the vector store. The embedding model is from the OCI Generative AI service. Users note that the vector search returns irrelevant documents for many queries. Which of the following is the most likely cause?

Accepted Answer

The query is not being converted to an embedding before search. In a RAG pipeline, the user's query must be converted into an embedding vector using the same embedding model that was used to embed the documents. If the query is sent as raw text to OpenSearch, it will perform a lexical (keyword) search instead of a vector similarity search, returning irrelevant results. OCI Data Science and OpenSearch require explicit embedding of the query before invoking the k-NN search endpoint.

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The chunk size is too large, causing overlapping context

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The OpenSearch cluster is too small to handle the load

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The embedding model dimension does not match OpenSearch index dimension

Question 6

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

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Quantize the model to INT8

Question 7

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.

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Knowledge cutoff; fine-tune the model on the catalog

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Bias amplification; increase temperature

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Overfitting; reduce the model size

Question 8

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.

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To improve embedding quality

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To speed up training

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To reduce storage costs

Question 9

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.

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

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

Question 10

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

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Object Storage bucket size

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Model accuracy on validation set

Question 11

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.

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Set up a load balancer across multiple regions

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Use a single VM shape to reduce network hops

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Disable model parallelism to simplify setup

Question 12

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.

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The temperature is not set.

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The model-id is incorrect.

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The prompt is too short.

Question 13

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

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Increase chunk_overlap in the text splitter

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Reduce chunk_size in the text splitter

Question 14

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

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ConversationTokenBufferMemory; set a token limit

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ConversationBufferMemory; no additional step

Question 15

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.

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It fine-tunes the model on the provided texts.

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It generates text completions based on prompts.

Question 16

A developer creates a prompt for a code generation task but the output often contains syntax errors. Which adjustment to the prompt is MOST likely to improve correctness?

Accepted Answer

Add a few examples of correct code for similar tasks in the prompt. Adding few-shot examples of correct code directly in the prompt provides the model with explicit patterns to follow, reducing syntactic drift. This technique, known as few-shot prompting, anchors the model's output to the demonstrated structure and syntax, which is far more effective than relying on the model's internal knowledge alone for code generation tasks.

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Remove the output format specification to give the model freedom

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Increase the temperature to 0.9 for more creative solutions

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Set the max tokens to a very high value

Question 17

Which of the following best describes the role of the self-attention mechanism in a Transformer model?

Accepted Answer

It computes a weighted sum of all input token representations, where weights depend on pairwise compatibility between tokens. The self-attention mechanism computes a weighted sum of all input token representations, where the weights are determined by the pairwise compatibility (attention scores) between tokens. This allows each token to dynamically attend to every other token in the sequence, capturing global dependencies without the limitations of fixed local windows or recurrence.

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It encodes the order of tokens in the sequence

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It applies a convolutional filter over local windows of tokens

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It replaces the need for positional encoding by using recurrence

Question 18

A developer wants to use LangChain to create an agent that can perform calculations and look up information from a database. Which tools should be provided to the agent?

Accepted Answer

Calculator tool and a custom tool for database queries. The agent needs to perform calculations and look up information from a database, so the correct toolset is a Calculator tool (for arithmetic) and a custom tool that wraps database queries (for the lookup). LangChain agents select tools based on the task, so providing exactly these two matches the stated capabilities.

Answer

Custom tool for database queries and a vector store tool

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Calculator tool and a retriever tool

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Web search tool and an LLM tool

Question 19

In LangChain, which component is responsible for connecting a language model to a retriever and a prompt template to answer questions based on retrieved documents?

Accepted Answer

RetrievalQA chain. RetrievalQA is the LangChain chain purpose-built to wire together a retriever, a prompt template, and an LLM so the model answers questions grounded in retrieved documents. It handles the retrieve-then-generate flow automatically: it fetches relevant chunks from the vector store, injects them into the prompt, and passes the augmented prompt to the LLM. This is the classic RAG pattern in LangChain.

Answer

LLMChain

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SequentialChain

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AgentExecutor

Question 20

A developer needs to generate embeddings for text data using the OCI Generative AI service. Which API should they call to get vector representations of text?

Accepted Answer

cohere.embed. The cohere.embed API is specifically designed to convert text into vector embeddings, which are numerical representations that capture semantic meaning. This is the correct choice for generating embeddings for RAG or vector search workflows in OCI Generative AI.

Answer

cohere.generate

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cohere.summarize

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cohere.classify