1Z0-1127-25 Practice Test 8 — 10 Questions

Question 1

A team is building a system to detect duplicate customer support tickets. They have a dataset of 10,000 resolved tickets and want to find pairs with similar intent. Which approach would be MOST efficient and effective?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Embed each ticket using an embedding model and compute cosine similarity between all pairs. Embedding each ticket into a dense vector space and computing cosine similarity between all pairs is both efficient and effective for detecting duplicate intents. This approach leverages pre-trained embedding models (e.g., sentence-transformers) that capture semantic similarity, and pairwise cosine similarity scales well for 10,000 tickets (approximately 50 million comparisons) using optimized matrix operations. It avoids the quadratic cost of LLM inference per pair while preserving high accuracy for intent matching.

Answer

Use a long-context LLM to process all tickets in a single prompt

Answer

Use a generation model like Cohere Command to compare tickets one pair at a time

Answer

Fine-tune a generation model on a classification task to predict duplicates

Question 2

Which component of the transformer architecture allows the model to weigh the importance of different words in a sentence when processing input?

Accepted Answer

Self-attention mechanism. The self-attention mechanism is the core component of the transformer architecture that enables the model to dynamically assign weights to each word in a sentence relative to every other word. This allows the model to capture contextual relationships and dependencies, such as determining which words are most relevant to the current word being processed, regardless of their distance in the sequence.

Answer

Layer normalization

Answer

Positional encoding

Answer

Feed-forward neural network

Question 3

A company wants to build a multi-modal RAG system that can retrieve both text and images based on a user query. Which approach is most aligned with OCI GenAI capabilities?

Accepted Answer

Utilize a multi-modal embedding model from OCI GenAI to embed both text and images into a common vector space. OCI GenAI supports multi-modal models like Cohere's multimodal embedding model, which can embed text and images into a shared vector space, enabling retrieval across modalities. Separate text and image models would not align the vectors. OCR-based text-only approach loses image semantics. Using multiple vector stores complicates retrieval.

Answer

Use OCI Document Understanding to convert images to text, then index text

Answer

Use separate vector stores for text and image embeddings

Answer

Use image captioning to generate text descriptions and index those

Question 4

A data scientist is using OCI Data Science to build a RAG system for medical literature. They have a large corpus of PDFs. They used the default OCI Generative AI embedding model and chunked each PDF into 512-character segments with 10% overlap. However, queries about specific drug doses often return incorrect information, even though the correct dose is present in the corpus. Upon inspection, they find that the retrieved chunks often contain partial dose information or miss the context units (e.g., mg vs. mcg). What improvement should they prioritize?

Accepted Answer

Use a semantic chunking strategy that respects document structure (e.g., paragraphs, sections).. The core issue is that fixed-size character chunking (512 characters) breaks medical documents mid-sentence or mid-section, causing critical context like units (mg vs. mcg) to be separated from the numeric dose. Semantic chunking that respects document structure (paragraphs, sections) ensures that dose information and its units remain in the same chunk, preserving the semantic integrity needed for accurate retrieval. This directly addresses the observed problem of partial dose information and missing context units.

Answer

Implement a secondary verification step using a rule-based pattern matcher.

Answer

Increase the chunk overlap to 50% to ensure more context.

Answer

Fine-tune the embedding model on medical text.

Question 5

A developer is using the OCI Generative AI service and notices that the cost per API call is higher than expected. Which factor contributes MOST to the cost of an LLM inference call?

Accepted Answer

Number of input and output tokens. Most LLM APIs charge based on the number of input and output tokens. The token count directly affects cost. Model size, context window, and temperature are related but the direct billing metric is token count.

Answer

Model size (number of parameters)

Answer

Context window size

Answer

Temperature setting

Question 6

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.

Answer

CustomModelClient

Answer

AiInferenceClient

Answer

GenerativeAiClient

Question 7

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.

Answer

Better at capturing domain-specific writing style

Answer

Eliminates the need for a vector database

Answer

Reduces token usage and latency compared to fine-tuning

Question 8

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

Answer

Use an API key instead of resource principal

Answer

Restart the notebook session

Question 9

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.

Answer

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

Answer

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

Answer

Train a Transformer model from scratch on the reviews

Question 10

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.

Answer

The chunk size is too large, causing overlapping context

Answer

The OpenSearch cluster is too small to handle the load

Answer

The embedding model dimension does not match OpenSearch index dimension