1Z0-1127-25 Practice Test 11 — 15 Questions

Question 1

Which of the following sampling strategies is most likely to produce the most diverse and creative text?

Accepted Answer

Temperature sampling with temperature=1.2. Temperature sampling with a higher temperature (>1) increases the probability of less likely tokens, promoting creativity and diversity.

Answer

Beam search with width 5

Answer

Greedy decoding

Answer

Top-p sampling with p=0.1

Question 2

An OCI user is comparing two embedding models: one with 768 dimensions and another with 1024 dimensions. Which of the following trade-offs is most relevant?

Accepted Answer

Higher dimensions may capture more fine-grained semantic information but require more storage and slower similarity search. Higher dimensions can capture more nuanced semantics but require more storage and may increase retrieval latency.

Answer

The 1024-dimensional model always yields better accuracy with no additional cost

Answer

The 768-dimensional model is always faster and more storage-efficient

Answer

Dimension size has no impact on performance or storage

Question 3

Which of the following is a decoder-only model architecture?

Accepted Answer

GPT-3. GPT is a decoder-only model. BERT is encoder-only. T5 is encoder-decoder.

Answer

T5

Answer

BART

Answer

BERT

Question 4

In the transformer architecture, what is the primary purpose of positional encoding?

Accepted Answer

To provide the model with information about the order of tokens. Since self-attention processes tokens in parallel without inherent order, positional encoding adds information about the position of each token in the sequence.

Answer

To normalize the input embeddings

Answer

To reduce the number of parameters in the model

Answer

To enable multi-head attention

Question 5

A data scientist is fine-tuning a Llama 2 7B model on a custom dataset using OCI Data Science. After training, the model generates fluent but factually incorrect statements about the new domain. Which post-training technique would BEST address this issue without retraining?

Accepted Answer

Use a retrieval-augmented generation (RAG) pipeline. RAG retrieves factual information from an external knowledge base to ground the generation, reducing hallucinations. The other options do not address factual accuracy.

Answer

Decrease the temperature to 0.1

Answer

Switch to a larger model like Llama 2 70B

Answer

Apply top-p sampling with p=0.9

Question 6

Which OCI Generative AI API should be used to convert a user's query into a vector representation for semantic search?

Accepted Answer

Embedding API. The OCI Generative AI Embedding API is specifically designed to convert text inputs, such as user queries, into dense vector representations (embeddings). These vectors capture semantic meaning and are essential for similarity search in vector databases or retrieval-augmented generation (RAG) pipelines. The other APIs serve different purposes: Chat handles multi-turn conversations, Generate produces text completions, and Rerank reorders documents based on relevance.

Answer

Chat API

Answer

Generate API

Answer

Rerank API

Question 7

A data scientist is using self-consistency decoding to improve the accuracy of a reasoning task. Which of the following best describes the process?

Accepted Answer

Generate multiple independent reasoning paths and aggregate the final answers by majority voting. Self-consistency decoding works by sampling multiple diverse chain-of-thought reasoning paths from the model (typically using a non-zero temperature) and then taking a majority vote over the final answers. This marginalizes over reasoning paths and reduces variance from any single flawed chain, improving accuracy on arithmetic, commonsense, and symbolic reasoning tasks. It is a sampling-and-aggregation technique, not a single-pass or tree-search method.

Answer

Generate one chain-of-thought reasoning path and use it as the final answer

Answer

Use a single forward pass with a low temperature setting

Answer

Branch into multiple reasoning trees and prune less promising branches

Question 8

Which THREE techniques are commonly used to improve the quality of text generation?

Accepted Answer

Temperature scaling. Temperature scaling (A) is correct because it adjusts the softmax logits before sampling, sharpening the distribution at low temperatures to make high-probability tokens more likely and improving coherence. Top-k sampling (B) is correct because it restricts sampling to the k most probable tokens at each step, cutting off the long tail of unlikely tokens that would otherwise degrade fluency. Beam search (E) is correct because it maintains multiple candidate sequences and selects the highest-scoring one, reducing the risk of locally optimal but globally poor outputs. Greedy decoding (C) is not marked correct because, although it is deterministic and can be adequate, it often produces repetitive or suboptimal text and is not typically framed as a quality-improvement technique. Random sampling (D) is not marked correct because sampling uniformly from the full distribution introduces high variance and frequently yields incoherent output.

Answer

Greedy decoding

Answer

Random sampling

Question 9

A research team is comparing two LLMs for a translation task. Model A uses greedy decoding, Model B uses beam search with width=5. Both models are otherwise identical. Which statement about their outputs is MOST likely true?

Accepted Answer

Model B will generally produce more fluent and accurate translations. Beam search considers multiple candidate sequences and selects the one with the highest overall probability, which often results in more fluent and accurate translations than greedy decoding, but at higher computational cost.

Answer

Model A will have higher BLEU scores than Model B

Answer

Model A will produce more diverse translations

Answer

Model B will have lower latency than Model A

Question 10

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) decouples knowledge from the model weights: documents are chunked, embedded, and stored in a vector database, and at query time the most relevant chunks are retrieved and injected into the prompt for the LLM to synthesize an answer. Because the knowledge lives in the index rather than the model, monthly policy updates only require re-embedding the changed documents — no retraining or fine-tuning is needed. This makes RAG the most cost-effective and operationally appropriate choice for frequently changing, domain-specific content.

Answer

Train a custom model from scratch on the policy documents each month

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Fine-tune a base LLM on the policy documents monthly

Question 11

A developer is using OCI Generative AI for a question-answering system. The model frequently provides outdated information because the training data cutoff is over a year old. Which approach would most effectively address this issue?

Accepted Answer

Implement a Retrieval-Augmented Generation (RAG) pipeline that retrieves up-to-date documents from an external knowledge base. Retrieval-Augmented Generation (RAG) directly addresses the problem of stale training data by dynamically retrieving current documents from an external knowledge base at inference time. This allows the model to generate answers grounded in up-to-date information without requiring retraining or a larger model, making it the most effective and practical solution for a question-answering system.

Answer

Increase the context window to include more of the user's prompt

Answer

Fine-tune the model on a dataset that includes recent information up to today

Answer

Switch to a larger model that has a more recent knowledge cutoff

Question 12

Which LangChain component is responsible for storing and retrieving message history across multiple turns in a conversation?

Accepted Answer

Memory. Memory is the correct component because it is specifically designed to store and retrieve conversation history across multiple turns, enabling the model to maintain context. Unlike stateless components, Memory persists past interactions (e.g., via buffer or summary mechanisms) and feeds them into the prompt for subsequent LLM calls.

Answer

PromptTemplate

Answer

Chain

Answer

Tool

Question 13

Which API should a developer use to send a multi-turn conversation history to an LLM, including a system message and previous user/assistant exchanges, using OCI Generative AI?

Accepted Answer

Chat API. The OCI Generative AI Chat API is purpose-built for multi-turn conversations: it accepts a list of messages with roles (system, user, assistant) so the model retains conversational context across turns. The Generate API handles single-prompt text generation without native conversation history semantics.

Answer

Summarisation API

Answer

Generate API

Answer

Embedding API

Question 14

Refer to the exhibit. A user deployed a custom model via OCI Data Science and registered it in the Model Catalog. They use the correct OCID but get this error. What is the most likely issue?

Accepted Answer

The model is not deployed to an endpoint. The error indicates that the user is trying to invoke a custom model via the OCI Data Science Model Catalog, but the model has not been deployed to an endpoint. In OCI, a model registered in the Model Catalog is just a metadata entry; it must be deployed to a managed endpoint (using OCI Data Science Model Deployment) to generate a serving URL that can accept inference requests. Without this deployment, any attempt to call the model using its OCID will fail because there is no active endpoint to route the request to.

Answer

The model is not fine-tuned

Answer

The compartment OCID is missing

Answer

The model is in a different region

Question 15

A company has fine-tuned a large language model using OCI Generative AI service. When attempting to deploy the model to a dedicated endpoint, the deployment fails with an error indicating insufficient capacity. Which action should be taken to resolve this issue?

Accepted Answer

Request a service limit increase for dedicated endpoints. The error indicates that the OCI tenancy has reached its quota for dedicated endpoints. Requesting a service limit increase is the correct action because OCI enforces service limits on dedicated endpoints to manage resource allocation, and increasing this limit allows the deployment to proceed.

Answer

Delete existing endpoints to free capacity

Answer

Deploy the model to a different OCI region

Answer

Use a pre-built model instead of the fine-tuned model