1Z0-1127-25 Practice Test 40 — 15 Questions

Question 1

Which LangChain document loader would you use to load data from a webpage given its URL?

Accepted Answer

WebBaseLoader. A is correct because WebBaseLoader is specifically designed in LangChain to load and parse HTML content from a given URL, extracting text from web pages. It uses libraries like BeautifulSoup or Selenium under the hood to handle the HTTP request and parse the DOM, making it the appropriate choice for loading data from a webpage.

Answer

PDFLoader

Answer

CSVLoader

Answer

TextLoader

Question 2

A data scientist observes that their fine-tuned LLM performs well on training data but generates repetitive and dull responses in production. What is the most likely cause and best solution?

Accepted Answer

The temperature is set too low; increase temperature during inference. The model's repetitive and dull responses indicate that the temperature parameter is too low, causing the model to always select the most probable tokens, leading to deterministic and monotonous outputs. Increasing temperature during inference introduces randomness into token sampling, allowing for more diverse and creative responses. This is a common issue in production LLMs where low temperature settings optimized for training metrics fail to produce engaging real-world outputs.

Answer

The model is overfitted; apply stronger regularization

Answer

The training data lacks diversity; add more varied examples

Answer

The model has too many layers; reduce model size

Question 3

A developer needs to authenticate API calls to OCI Generative AI from a compute instance. Which TWO methods can be used?

Accepted Answer

Configure an API key in OCI IAM for the user. Option A is correct because OCI IAM API keys (a public/private RSA key pair uploaded to the user's IAM profile) are a standard way for a developer to sign OCI API requests, including calls to the Generative AI inference endpoints, using the OCI SDK/CLI with the user's credentials. Option D is correct because resource principal with instance principals lets code running on a compute instance authenticate to OCI services—including Generative AI—without embedding user credentials, by using the instance's dynamic group membership and IAM policies to obtain a short-lived security token. Option B is not an authentication method; a customer-managed key (CMK) in OCI Vault is used for encryption at rest, not for authenticating API calls. Option C is not an authentication mechanism; a service connector moves data between services via a connector hub and does not authenticate a developer's direct API calls to Generative AI. Option E is not appropriate here; an auth token is a user credential used for OCI registry (OCIR) and other token-based flows, not for signing Generative AI API requests from a compute instance.

Answer

Configure a customer-managed key (CMK) for encryption

Answer

Set up a service connector to forward requests

Answer

Use an auth token from OCI Identity

Question 4

A team uses Cohere's `rerank` endpoint after initial retrieval to improve result quality. What is the main benefit of reranking?

Accepted Answer

It reorders chunks by relevance to the query. The rerank endpoint takes the initial set of retrieved chunks and re-scores them based on their semantic relevance to the query, producing a reordered list where the most contextually appropriate chunks appear first. This improves the quality of the input fed to the LLM, leading to more accurate and coherent generated responses without altering the embeddings or the retrieval count.

Answer

It generates new embeddings for chunks

Answer

It combines multiple queries

Answer

It reduces the number of retrieved chunks

Question 5

A team is designing a RAG system for legal document review. They want to ensure that the retrieved chunks are contextually coherent and not truncated mid-sentence. Which chunking strategy should they use?

Accepted Answer

Recursive chunking based on sentence boundaries.. Recursive chunking based on sentence boundaries ensures that each chunk ends at a natural sentence boundary, preserving contextual coherence and avoiding mid-sentence truncation. This is critical for legal document review where incomplete sentences can lead to misinterpretation of legal clauses. The recursive approach allows for variable-length chunks that respect linguistic structure, unlike fixed-size or token-level methods.

Answer

Token-level chunking.

Answer

Semantic chunking using document section headers.

Answer

Fixed-size character chunking with overlap.

Question 6

What is the primary goal of chain-of-thought prompting?

Accepted Answer

Encourage step-by-step reasoning to improve accuracy on multi-step problems. Chain-of-thought prompting explicitly instructs or demonstrates to the model that it should reason through intermediate steps before producing a final answer. This decomposition of multi-step problems into sequential reasoning steps significantly improves accuracy on tasks like math word problems, logical deduction, and multi-hop question answering.

Answer

Prevent the model from hallucinating

Answer

Make the output more creative

Answer

Reduce output token count

Question 7

Which TWO are advantages of using LoRA for fine-tuning?

Accepted Answer

Requires less GPU memory. LoRA (Low-Rank Adaptation) is an advantage for fine-tuning because it requires less GPU memory (option A): instead of updating all weights, it freezes the base model and trains only small low-rank adapter matrices, so optimizer states and gradients consume far less VRAM. It also reduces the number of trainable parameters (option C), since each adapted weight matrix is decomposed into two small rank-r matrices, typically training well under 1% of the original parameters. Option B is not guaranteed — LoRA can match full fine-tuning quality but does not inherently guarantee higher accuracy. Option D is wrong because LoRA adds only tiny adapter weights and does not increase the base model size meaningfully. Option E is wrong because LoRA targets training efficiency, not inference speed; adapters can even add slight inference overhead unless merged.

Answer

Guarantees higher accuracy

Answer

Increases model size

Answer

Improves inference speed

Question 8

A data scientist wants to deploy a fine-tuned LLM on OCI for inference with low latency. Which OCI service should they use?

Accepted Answer

OCI Generative AI Service (Dedicated AI Cluster). B is correct because OCI Generative AI Service with a Dedicated AI Cluster provides a managed, high-throughput, low-latency inference endpoint for fine-tuned LLMs. It leverages GPU-accelerated infrastructure and optimized serving stacks (e.g., vLLM, TensorRT-LLM) to minimize response times, making it ideal for production inference workloads.

Answer

OCI Data Science Notebook Session

Answer

OCI Data Flow

Answer

OCI Functions

Question 9

A prompt engineer is testing two versions of a prompt for a content generation task. They want to measure which version produces more factual and concise outputs. Which evaluation approach is BEST?

Accepted Answer

Run A/B tests on a diverse set of inputs and score outputs based on predefined criteria. Running A/B tests on a diverse set of inputs and scoring outputs based on predefined criteria (e.g., factuality, conciseness) provides a systematic and objective comparison. This approach reduces bias and ensures that the evaluation is representative of the task. It is the best practice for prompt evaluation.

Answer

Use only the first output from each prompt and manually compare

Answer

Ask the model to self-evaluate its outputs

Answer

Increase the temperature to see which prompt handles randomness better

Question 10

A developer is building a conversational AI application using LangChain and needs to persist chat history across sessions. Which TWO approaches can they use? (Choose TWO.)

Accepted Answer

Use ConversationSummaryMemory and store the summary in a file. ConversationSummaryMemory can be persisted by storing its summary in a file, which allows chat history to survive across sessions. Option E is correct because ConversationBufferMemory can be explicitly saved to a database, providing durable storage for the conversation buffer. Both approaches decouple memory from the in-memory lifecycle, enabling cross-session persistence.

Answer

Use the agent's memory parameter with a default in-memory store

Answer

Enable streaming responses to automatically save history

Answer

Use ChatMessageHistory without a backing store

Question 11

A developer is building a RAG application using Oracle Cloud Infrastructure (OCI) Document Understanding and OCI Generative AI. After chunking documents and generating embeddings, the developer observes that the retrieval step often returns chunks that are semantically unrelated to the query. Which action is MOST likely to improve retrieval relevance?

Accepted Answer

Adjust the chunk size and chunk overlap to better capture coherent passages.. Adjusting chunk size and overlap helps create coherent chunks that align with query intent, improving retrieval relevance. Option A is wrong because the embedding model type (dense vs. sparse) affects retrieval method but does not directly fix chunk coherence issues. Option C is wrong because increasing chunk size may introduce noise and irrelevant context. Option D is wrong because reducing the number of retrieved chunks (k) only limits results, not improves relevance of individual chunks.

Answer

Switch from a dense embedding model to a sparse embedding model.

Answer

Increase the chunk size to capture more context.

Answer

Reduce the number of retrieved chunks (k) in the vector search.

Question 12

Which of the following best describes the role of attention in transformer models?

Accepted Answer

It allows the model to focus on relevant parts of the input sequence when generating output.. The attention mechanism in transformer models dynamically computes a weighted sum of all input tokens, allowing the model to focus on the most relevant parts of the input sequence when generating each output token. This is achieved through scaled dot-product attention, which assigns higher weights to tokens that are more contextually important, enabling the model to capture long-range dependencies effectively.

Answer

It assigns equal weight to all words in the input.

Answer

It is used only during training, not inference.

Answer

It replaces the need for positional encoding.

Question 13

A team is building a multilingual semantic search application. They need to index documents in English, Spanish, and French, and later search using queries in any of these languages. Which embedding model should they use?

Accepted Answer

Cohere embed-multilingual-v3.0. Cohere embed-multilingual-v3.0 supports multiple languages in a single model, enabling cross-lingual semantic search. embed-english-v3.0 is English-only. Command R and Llama 3 are not embedding models.

Answer

Meta Llama 3 70B

Answer

Cohere Command R

Answer

Cohere embed-english-v3.0

Question 14

An enterprise RAG system must ensure that retrieved data comes only from authorized sources. Which OCI feature should be used to enforce this?

Accepted Answer

OCI IAM policies for the vector database. OCI IAM policies allow you to define granular access controls on the vector database, ensuring that only authorized principals (users, groups, or service principals) can read or write data. This directly enforces that retrieved data comes only from authorized sources, which is a core requirement for enterprise RAG systems.

Answer

Data encryption at rest

Answer

Network security groups

Answer

Resource quotas

Question 15

A company uses OCI Generative AI to generate legal document summaries. They have a custom model deployed on a dedicated AI cluster. They want to ensure that the model is not used by unauthorized users. They also need to log all inference requests for auditing. Which combination of OCI services should they use?

Accepted Answer

OCI Identity and Access Management (IAM) policies and OCI Logging.. OCI IAM policies are the primary mechanism for controlling access to OCI resources, including custom models on dedicated AI clusters, by defining which users or groups can invoke the model. OCI Logging captures detailed logs of all inference requests, including metadata such as timestamps, source IPs, and request payloads, which satisfies the auditing requirement. Together, they provide both authorization enforcement and audit trail without additional services.

Answer

OCI Vault for encryption and OCI Audit for logging.

Answer

OCI Data Safe and OCI Monitoring.

Answer

OCI API Gateway with authentication and OCI Audit.