1Z0-1127-25 Practice Test 12 — 15 Questions

Question 1

A team is deploying a chatbot that must never output harmful or biased statements. They plan to use a pre-trained LLM with in-context learning. Which additional measure is MOST effective at reducing harmful outputs without retraining?

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

Accepted Answer

Include few-shot examples in the system prompt that demonstrate appropriate responses. Few-shot examples embedded in the system prompt provide the model with concrete demonstrations of desired safe behavior at inference time, which is the essence of in-context learning. This steers the model's output distribution toward the demonstrated patterns without any weight updates, making it the most effective no-retraining mitigation. It also allows rapid iteration — safety examples can be swapped or expanded without redeploying the model.

Answer

Apply a larger context window to include more safety instructions

Answer

Fine-tune the model on a curated dataset of safe dialogues

Answer

Use beam search with a high beam width

Question 2

A data scientist is comparing BLEU, ROUGE, and BERTScore to evaluate a summarization model. The client cares most about whether the summary captures all key facts from the source document. Which metric is most aligned with this requirement?

Accepted Answer

ROUGE. ROUGE is recall-oriented, measuring how much of the reference content appears in the generated summary, which aligns with capturing key facts. BLEU is precision-oriented, and BERTScore measures semantic similarity but is not specifically recall-focused.

Answer

Perplexity

Answer

BERTScore

Answer

BLEU

Question 3

Which LangChain abstraction is used to wrap OCI Generative AI's chat models (e.g., Cohere Command R) for use in a LangChain chain?

Accepted Answer

ChatOCIGenAI. LangChain provides ChatOCIGenAI as a wrapper for OCI chat models, similar to ChatOpenAI for OpenAI. This allows seamless integration with LangChain's chat model interface.

Answer

OCIGenAI

Answer

OCIGenAIEmbeddings

Answer

OracleAIChain

Question 4

In LangChain, which component is responsible for loading data from a specific file format, such as PDF or CSV, into a document object?

Accepted Answer

Document Loader. Document Loader is the correct component because it is specifically designed to ingest data from various file formats (e.g., PDF, CSV, HTML) and convert it into LangChain's standardized Document objects. This is the foundational step in any retrieval-augmented generation (RAG) pipeline, where raw data must first be loaded before any splitting, embedding, or retrieval occurs.

Answer

Text Splitter

Answer

Vector Store

Answer

Retriever

Question 5

An organization uses Oracle AI Vector Search in Oracle Database 23ai to store embeddings for a LangChain RAG application. They need to perform similarity search with high recall and low latency for a large dataset (10M vectors). Which index configuration should they choose?

Accepted Answer

HNSW index with appropriate parameters (e.g., efConstruction=200, M=32). HNSW (Hierarchical Navigable Small World) indexes provide high recall and low latency for approximate nearest neighbor search, especially on large datasets. IVF (Inverted File) is also an option but typically requires more tuning and may have lower recall at high speed. HNSW is generally preferred for production workloads.

Answer

Binary quantization without an index

Answer

No index, use brute-force distance computation

Answer

IVF index with 1000 centroids

Question 6

In a LangChain RetrievalQA chain, which method on a vector store is used to create the retriever object?

Accepted Answer

as_retriever(). In LangChain, the `as_retriever()` method on a vector store object converts the vector store into a retriever object that can be used in a RetrievalQA chain. This method is part of the base `VectorStore` class and returns a `VectorStoreRetriever` instance, which wraps the vector store's similarity search functionality into a retriever interface compatible with LangChain's retrieval chains.

Answer

build_retriever()

Answer

to_retriever()

Answer

get_retriever()

Question 7

A company wants to use OCI Generative AI service to generate email summaries for customer support. They need to ensure low latency and data residency in Frankfurt. What should they use?

Accepted Answer

Use the managed serving endpoint in Frankfurt region.. Managed serving endpoints in OCI Generative AI are pre-deployed, fully managed endpoints that provide low-latency inference and are available in specific regions, including Frankfurt. This ensures data residency in Frankfurt while meeting the latency requirements for real-time email summarization.

Answer

Use the playground in US region.

Answer

Use OCI AI Quick Actions.

Answer

Create a dedicated AI cluster in Frankfurt.

Question 8

An OCI CLI command above returns embeddings for the phrase 'Hello world'. The developer notices that the embedding vector length is 384 dimensions. However, they expected 768 dimensions. What is the most likely cause?

Accepted Answer

The model 'cohere.embed-multilingual-light-v3.0' outputs 384-dimensional vectors.. The Cohere model 'cohere.embed-multilingual-light-v3.0' is specifically designed to output 384-dimensional embeddings. The developer's expectation of 768 dimensions likely stems from familiarity with larger models like 'cohere.embed-english-v3.0', which outputs 1024 dimensions, or other models that produce 768-dimensional vectors. The embedding dimension is a fixed property of the model, not influenced by input length or CLI display settings.

Answer

The input text 'Hello world' is too short, causing dimension reduction.

Answer

The CLI result is truncated in the display.

Answer

The --truncate END flag reduces the dimension.

Question 9

A developer is using Oracle AI Vector Search with LangChain to build a RAG system on top of Oracle Database 23ai. They have created a VECTOR column and built an HNSW index. To improve recall at the cost of some accuracy, which index parameter should they adjust?

Accepted Answer

Increase the 'neighbors' parameter. Increasing the 'neighbors' parameter (M) in an HNSW index expands the number of bidirectional links per node during graph construction. This denser graph provides more alternative paths during search, improving recall because the search is less likely to miss relevant vectors, but it also increases memory usage and can slightly degrade search speed due to more edges to traverse.

Answer

Increase the chunk_overlap in the text splitter

Answer

Decrease the 'efConstruction' parameter

Answer

Switch from HNSW to IVF index type

Question 10

When evaluating a summarization model, the team notices that the ROUGE-L score is high but human evaluators rate the summaries poorly for coherence. What does this discrepancy MOST likely indicate?

Accepted Answer

ROUGE-L is not sensitive to coherence and fluency, only to n-gram overlap. ROUGE-L measures longest common subsequence overlap, focusing on word order recall. It does not capture coherence, factual consistency, or fluency. High ROUGE-L with low human scores suggests the summaries are lexically similar but not well-formed.

Answer

The model is suffering from hallucination

Answer

The reference summaries are too short

Answer

The human evaluators are biased

Question 11

A startup needs to deploy an LLM for a simple FAQ chatbot on OCI with low latency. Which model choice is most appropriate?

Accepted Answer

Use a smaller, task-specific fine-tuned model.. For a simple FAQ chatbot requiring low latency, a smaller, task-specific fine-tuned model is most appropriate because it reduces inference time and computational overhead while maintaining high accuracy on the narrow domain of FAQs. Fine-tuning adapts the model to the specific question-answer patterns, avoiding the unnecessary complexity and latency of larger general-purpose models.

Answer

Use a medium-sized model with high precision.

Answer

Use an ensemble of models.

Answer

Use the largest available model for best quality.

Question 12

In LangChain, which class should be used to wrap Oracle Cloud Infrastructure's Generative AI service as a chat model?

Accepted Answer

ChatOCIGenAI. ChatOCIGenAI is the LangChain class specifically designed to wrap Oracle Cloud Infrastructure's Generative AI service as a chat model. It implements the BaseChatModel interface, allowing seamless integration with LangChain's chat-based workflows. This class handles authentication and API calls to OCI's generative AI endpoints.

Answer

OracleVS

Answer

OCIGenAIEmbeddings

Answer

OCIGenAI

Question 13

Which TWO techniques are commonly used to reduce the memory footprint of LLM inference?

Accepted Answer

Quantization. Quantization (A) is correct because it stores model weights and/or activations in lower-precision formats such as INT8, INT4, or FP8 instead of FP16/FP32, directly shrinking the memory needed to hold the model and reducing bandwidth during inference. KV cache optimization (C) is correct because the key/value cache grows with sequence length and batch size and can dominate memory during long-context generation; techniques like PagedAttention, cache quantization, or sliding-window/eviction policies reduce that footprint. Increasing batch size (B) is wrong because it enlarges activation and KV-cache memory rather than reducing it. Gradient checkpointing (D) is a training-time memory-saving technique that trades extra compute for storing fewer activations, and it does not apply to standard inference. Using full precision FP32 (E) is wrong because it increases, not decreases, the memory footprint compared with FP16 or quantized formats.

Answer

Increasing batch size

Answer

Gradient checkpointing

Answer

Using full precision (FP32)

Question 14

In OCI Generative AI, when using the Cohere Command model, which parameter is used to discourage the model from repeating the same phrases?

Accepted Answer

frequency_penalty. In OCI Generative AI, the frequency_penalty parameter is used to discourage the model from repeating the same phrases by penalizing tokens based on how often they have already appeared in the generated text. A higher frequency_penalty reduces repetition.

Answer

presence_penalty

Answer

temperature

Answer

top-k

Question 15

An OCI user wants to generate embeddings for a large corpus of technical documents to enable semantic search. Which type of model should they use?

Accepted Answer

An embedding model like Cohere Embed. Embedding models are specifically designed to produce dense vector representations that capture semantic meaning. They are distinct from generation models. For semantic search, embeddings from an embedding model are compared using cosine similarity.

Answer

A summarization model

Answer

A classification model

Answer

A generation model like Cohere Command