1Z0-1127-25 Practice Test 37 — 15 Questions

Question 1

A team wants to use a prompt pattern that combines reasoning with acting, where the model alternates between thinking and taking actions (e.g., tool calls). Which pattern fits this requirement?

Accepted Answer

ReAct pattern. The ReAct pattern (Reason + Act) prompts the model to output reasoning steps and then perform actions (like API calls), enabling interactive problem-solving.

Answer

Template pattern

Answer

Role prompting

Answer

Recipe pattern

Question 2

Refer to the exhibit. A user in GenAI-Users group tries to run a text generation inference but gets permission denied. What is the most likely issue?

Accepted Answer

The operation condition is too restrictive.. The policy attached to the GenAI-Users group includes a condition that restricts the operation to a specific compartment or resource, but the user is attempting to run inference in a different compartment or without meeting the condition. Since the condition is too restrictive, the IAM policy denies the action even though the user is in the correct group and the resource type is valid.

Answer

The policy resource type is wrong.

Answer

The group name mismatch.

Answer

The user is not in the compartment.

Question 3

A company uses a RAG pipeline with OCI Data Science and Cohere embeddings. They notice that retrieval recall is low for domain-specific acronyms. What is the best practice to improve this?

Accepted Answer

Expand acronyms to their full forms during document preprocessing and indexing.. Expanding acronyms to their full forms during document preprocessing and indexing ensures that the embedding model can map the acronym to its semantic meaning, improving retrieval recall for domain-specific terms. Cohere embeddings are trained on general text, so without expansion, acronyms like 'NLP' may not match queries for 'Natural Language Processing' in vector space. This preprocessing step directly addresses the root cause of low recall for acronyms.

Answer

Reduce the cosine similarity threshold in the vector search.

Answer

Fine-tune the embedding model with domain-specific acronyms.

Answer

Increase the chunk size to include more context around acronyms.

Question 4

An application needs to generate embeddings for text in multiple languages (English, Spanish, French). Which OCI Generative AI embedding model should be used?

Accepted Answer

embed-multilingual-v3.0. The embed-multilingual-v3.0 model is designed for multilingual text. The English-only model would not handle other languages well.

Answer

embed-english-v3.0

Answer

Cohere Command R

Answer

Meta Llama 3

Question 5

A startup is using OCI Generative AI serverless inference for a text generation application. They notice that the latency is high during peak hours. They have a budget to increase costs moderately. Which action would most effectively reduce latency?

Accepted Answer

Switch to dedicated AI cluster.. Switching to a dedicated AI cluster provides consistent low latency compared to serverless inference. Option B is wrong because enabling content filtering does not affect latency; it is used to filter harmful content. Option C is wrong because increasing the number of concurrent requests may increase the load on the serverless infrastructure and could actually worsen latency. Option D is wrong because although using a smaller model might reduce latency, it can degrade the quality of generated text, and the dedicated cluster is a more effective solution given the budget to increase costs moderately.

Answer

Enable content filtering.

Answer

Increase the number of concurrent requests.

Answer

Use a smaller model.

Question 6

A company wants to use OCI Generative AI service to automatically generate product descriptions for an e-commerce catalog. They have 10,000 products. What is the best approach to ensure high-quality, consistent descriptions?

Accepted Answer

Fine-tune a base model on a dataset of existing product descriptions.. Fine-tuning a base model on a dataset of existing product descriptions is the best approach because it adapts the model to the specific domain, style, and vocabulary of the e-commerce catalog. This ensures high-quality, consistent outputs across 10,000 products by learning the patterns and terminology from the company's own data, rather than relying on generic or template-based methods.

Answer

Use a pre-trained summarization model.

Answer

Use a template-based generation with keyword insertion.

Answer

Use the built-in chat model with few-shot examples in the prompt.

Question 7

A developer is using ChatPromptTemplate with MessagesPlaceholder to handle conversation history. What is the purpose of MessagesPlaceholder in the prompt template?

Accepted Answer

To provide a slot where conversation history messages can be injected dynamically. MessagesPlaceholder in LangChain's ChatPromptTemplate is specifically designed to inject conversation history messages dynamically at a designated position in the prompt. It acts as a slot that can hold a list of messages (e.g., from a chat memory buffer) and is replaced at runtime with the actual history, allowing the LLM to maintain context across turns.

Answer

To insert a single user message at a specific position

Answer

To add a system instruction that is always present

Answer

To convert the chat history into a plain string for the LLM

Question 8

Which THREE factors should be considered when designing a vector search index for a RAG application that supports multiple languages?

Accepted Answer

Implement language identification as a preprocessing step.. Language identification as a preprocessing step ensures that documents are correctly tagged before indexing, which allows the system to apply appropriate language-specific tokenization, stop-word removal, and stemming. This prevents cross-language contamination in the vector index and improves retrieval accuracy for a multilingual RAG application.

Answer

Create separate vector indexes for each language.

Answer

Use larger chunk sizes for languages with complex morphology.

Question 9

A data scientist needs to fine-tune a Llama 3 model for a legal document classification task. They have a dataset of 10,000 labeled examples. Which fine-tuning technique available in OCI Generative AI is most suitable for efficiently adapting the model with limited computational overhead?

Accepted Answer

T-Few fine-tuning. T-Few fine-tuning is a parameter-efficient technique that updates only a small number of weights, making it suitable for fine-tuning large models with limited compute. It is the technique offered by OCI GenAI for fine-tuning.

Answer

Full fine-tuning all model parameters

Answer

LoRA (Low-Rank Adaptation)

Answer

Prefix tuning

Question 10

A developer is troubleshooting low recall in a vector search. Which THREE factors should be checked? (Choose three.)

Accepted Answer

Embedding model quality and relevance to domain. The embedding model's quality and domain relevance directly determine how well semantic relationships are captured. If the model is not fine-tuned on domain-specific data, it may fail to map similar concepts close together in the vector space, leading to low recall. For example, a general-purpose model may not distinguish between 'bank' as a financial institution versus a river bank in a legal document search.

Answer

The number of results returned (k) in the search

Answer

The LLM's temperature setting

Question 11

A data scientist is fine-tuning a Llama 2 model on a custom dataset for a summarization task. After fine-tuning, the model produces summaries that are too similar to the input text, often copying sentences verbatim. Which adjustment is MOST likely to reduce copying and improve abstractive summarization?

Accepted Answer

Increase the temperature to 0.8 and use top-p sampling. Increasing temperature (e.g., to 0.8) adds randomness to token selection, making the model less likely to choose the most probable tokens that lead to verbatim copying from the input. Top-p sampling further restricts the sampling pool to a cumulative probability mass, balancing diversity and coherence. In contrast, greedy decoding and beam search always select high-probability tokens, which encourages copying. Reducing context window may lose important information, not fix copying. Thus, higher temperature with top-p sampling is the best adjustment to promote abstractive summarization.

Answer

Switch to greedy decoding

Answer

Increase the beam search width

Answer

Reduce the context window size

Question 12

An OCI user observes that their embedding model returns vectors that are not normalized, and they want to compute cosine similarity between two text embeddings. What should they do?

Accepted Answer

Normalize the vectors to unit length, then compute the dot product. Cosine similarity measures the cosine of the angle between two vectors, which is equivalent to the dot product of the vectors after they have been normalized to unit length (L2 norm = 1). Option C correctly describes this process: first normalize each embedding vector to unit length, then compute the dot product. This is the standard approach because raw embedding vectors from models like OCI's AI services may not be unit vectors, and the dot product alone does not account for magnitude differences.

Answer

Compute the Euclidean distance between the vectors

Answer

Compute the L1 norm of the difference

Answer

Compute the dot product directly

Question 13

A company is fine-tuning a Llama model on OCI with dedicated AI cluster. They want to use their own training data stored in Oracle Object Storage. What must they do to ensure the fine-tuning job can access the data?

Accepted Answer

Configure a resource principal for the cluster to read the bucket.. OCI Generative AI dedicated AI clusters require a resource principal (an IAM principal) to authenticate and authorize access to data stored in Oracle Object Storage. By configuring the cluster with a resource principal that has read permissions on the bucket, the fine-tuning job can securely read the training data without exposing credentials or using public URLs.

Answer

Upload the data to the dedicated cluster's local storage.

Answer

Use OCI Data Flow to transfer data.

Answer

Create a pre-authenticated request for the bucket.

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

Answer

ConversationTokenBufferMemory; set a token limit

Answer

ConversationBufferMemory; no additional step

Question 15

An application uses LangChain's ConversationalRetrievalChain with memory. Users report that the chatbot occasionally repeats information from earlier in the conversation even when the new question is unrelated. What is the most likely cause?

Accepted Answer

The retriever is returning irrelevant documents from the conversation history. The chain retrieves documents based on the conversation history plus the current question; if the retriever returns irrelevant chunks from past context, the LLM may repeat old information.

Answer

The memory type is set to ConversationSummaryMemory

Answer

The chunk_size is too small, causing loss of context

Answer

The LLM temperature is set too high