1Z0-1127-25 Practice Test 46 — 15 Questions

Question 1

Which Oracle AI Vector Search index type is designed for approximate nearest neighbor search and uses a navigable small world graph?

Accepted Answer

HNSW. HNSW (Hierarchical Navigable Small World) is a graph-based index for ANN search.

Answer

VECTOR

Answer

IVF

Answer

BTREE

Question 2

Which prompting technique involves providing the model with a small set of input-output examples within the prompt to guide its behavior?

Accepted Answer

Few-shot prompting. Few-shot prompting includes examples of desired input-output pairs, helping the model infer the task without fine-tuning.

Answer

Tree-of-thought prompting

Answer

Chain-of-thought prompting

Answer

Zero-shot prompting

Question 3

Which three techniques are commonly used to reduce the risk of prompt injection in LLM applications? (Choose three.)

Accepted Answer

Output filtering.. Output filtering (B) is correct because it acts as a post-processing defense that scans the LLM's generated output for malicious content, such as leaked system prompts or injected commands, before it reaches the user. This technique helps mitigate the impact of successful prompt injections by catching and neutralizing harmful outputs that bypass input controls.

Answer

Enabling prompt validation against regex patterns.

Answer

Increasing temperature.

Question 4

A team is fine-tuning a foundation model on a large dataset stored in OCI Object Storage. They want to minimize data transfer costs. What is the best practice for locating the storage?

Accepted Answer

Place the bucket in the same region as the fine-tuning job. Placing the Object Storage bucket in the same OCI region as the fine-tuning job eliminates cross-region data transfer charges. OCI charges egress fees when data moves between regions, but intra-region data transfer between services in the same region is free. This minimizes costs while keeping the data accessible for the fine-tuning workload.

Answer

Place the bucket in the same region and availability domain as the fine-tuning job

Answer

Use OCI File Storage instead of Object Storage

Answer

Use a cross-region bucket to leverage geographically distributed data

Question 5

A company is deploying a multi-language chatbot using OCI Generative AI Service. The chatbot must support English, Spanish, and French. The team finds that responses in Spanish are less accurate than in English. They have a small bilingual dataset. What is the best approach?

Accepted Answer

Use a multilingual base model (e.g., mT5) and fine-tune on the bilingual dataset (English and Spanish) using cross-lingual transfer learning.. Fine-tuning a multilingual base model like mT5 on a small bilingual dataset leverages cross-lingual transfer learning, where knowledge from high-resource languages (English) improves performance on low-resource languages (Spanish). This approach is specifically designed for scenarios with limited data and directly addresses the accuracy gap without requiring separate models or translation pipelines.

Answer

Use prompt engineering with language-specific instructions in the system prompt.

Answer

Translate all user queries to English, process them, then translate responses back.

Answer

Train separate fine-tuned models for each language.

Question 6

A financial firm deploys a RAG application using OCI OpenSearch. They observe that the LLM sometimes generates incorrect answers that are not supported by the retrieved documents. Which technique directly addresses this issue?

Accepted Answer

Implement a post-generation verification step that checks if the answer is grounded in the retrieved chunks.. It directly addresses the problem of hallucination by verifying that the LLM's output is factually supported by the retrieved documents. In a RAG pipeline, the LLM may still generate unsupported content even with good retrieval; a post-generation grounding check explicitly validates each claim against the source chunks, ensuring answer fidelity.

Answer

Use a more detailed system prompt instructing the model to not make up information.

Answer

Increase the temperature parameter of the LLM to reduce creativity.

Answer

Increase the number of retrieved documents to provide more context.

Question 7

A developer is using OCI Generative AI with a Cohere Command model for text generation. They want the output to be more creative and diverse, but still relevant. Which sampling strategy should they use?

Accepted Answer

Top-p (nucleus) sampling. Top-p (nucleus) sampling selects from the smallest set of tokens whose cumulative probability exceeds p. It adapts to the model's confidence, allowing diversity while maintaining relevance. Greedy decoding is deterministic, temperature scales all probabilities, top-k fixes the number of candidates, and beam search explores multiple sequences but tends to produce safe outputs.

Answer

Temperature sampling (temperature > 1)

Answer

Top-k sampling

Answer

Greedy decoding

Question 8

Refer to the exhibit. A data scientist received this output after submitting a fine-tuning job. What is the most effective change to resolve the out-of-memory error?

Accepted Answer

Increase the number of nodes in the cluster.. The out-of-memory error during fine-tuning indicates that the model's memory requirements exceed the available resources on the current node. Increasing the number of nodes in the cluster distributes the model parameters, gradients, and optimizer states across multiple GPUs or nodes, effectively increasing the total memory capacity and resolving the OOM error. This is a standard approach in distributed training frameworks like PyTorch DDP or FSDP, which OCI Data Science supports.

Answer

Increase the sequence length.

Answer

Reduce the learning rate.

Answer

Decrease the number of fine-tuning epochs.

Question 9

A developer is evaluating OCI GenAI model families. Which three are correct characteristics of the available models? (Choose three.)

Accepted Answer

Llama models are open-source and available for fine-tuning. Llama models, such as Llama 2 and Llama 3, are open-source large language models originally developed by Meta. OCI GenAI provides them as pre-built models that developers can fine-tune using their own datasets, enabling customization for domain-specific tasks without training from scratch.

Answer

All models support real-time streaming of tokens

Answer

Cohere Command models are optimized for multilingual tasks

Question 10

A company fine-tunes an LLM on internal support tickets. After deployment, the model hallucinates company-specific product names. What is the most effective mitigation?

Accepted Answer

Implement RAG with a verified product database. RAG (Retrieval-Augmented Generation) grounds the LLM's output in a verified product database, providing factual context that prevents hallucination of company-specific product names. Unlike fine-tuning, which only adjusts model weights and can still produce plausible but incorrect names, RAG retrieves exact records at inference time, ensuring accuracy for proprietary terminology.

Answer

Switch to a smaller model to reduce hallucination risk

Answer

Use prompt engineering to remind the model to be accurate

Answer

Fine-tune further with more ticket data

Question 11

A healthcare startup is building an AI assistant to help doctors draft clinical notes from patient-physician conversations. They have a large language model that is fine-tuned on medical data. During testing, they notice the model occasionally generates plausible-sounding but incorrect medical recommendations. The startup wants to deploy the assistant to assist doctors, not replace them. They have the following options: (A) Deploy the model as-is and rely on doctors to catch errors, (B) Add a disclaimer that the model may make mistakes, (C) Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base before presenting to doctors, (D) Reduce the model's temperature to 0 to ensure deterministic outputs. Which option best balances safety and utility?

Accepted Answer

Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base.. Implementing a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base directly mitigates the risk of hallucinated medical recommendations while preserving the assistant's utility. This approach leverages retrieval-augmented generation (RAG) principles to ground the model's outputs in verified facts, ensuring safety without sacrificing the flexibility needed for drafting clinical notes.

Answer

Add a disclaimer that the model may make mistakes.

Answer

Deploy the model as-is and rely on doctors to catch errors.

Answer

Reduce the model's temperature to 0 to ensure deterministic outputs.

Question 12

A healthcare startup is building a chatbot to answer patient inquiries using a large language model (LLM) deployed on OCI Data Science AI Quick Actions. The chatbot must comply with HIPAA regulations, so all patient data must remain within the OCI tenancy and never be sent to third-party APIs. The team has fine-tuned a Llama 2 7B model on de-identified medical records using OCI Data Science notebooks. The model is deployed as a managed endpoint via AI Quick Actions. Early testing shows that the chatbot sometimes generates responses containing specific patient names or dates of birth that were present in the fine-tuning dataset. Moreover, the model occasionally hallucinates medication dosages that are not medically accurate. Which course of action should the team take to address both issues while maintaining HIPAA compliance?

Accepted Answer

Re-fine-tune the model using differential privacy to limit memorization of training data, and implement retrieval-augmented generation (RAG) with a curated medical knowledge base to ground medication-related responses.. It addresses both memorization of PII and hallucination of medication dosages while maintaining HIPAA compliance. Differential privacy during fine-tuning limits the model's ability to memorize specific patient data, and retrieval-augmented generation (RAG) grounds responses in a curated medical knowledge base, reducing hallucinations without sending data outside the OCI tenancy.

Answer

Deploy a rule-based post-processing script that checks each response against a list of known patient names and medication dosages, and rejects any response containing them.

Answer

Switch to a larger model (e.g., Llama 2 70B) to improve accuracy and reduce hallucinations, and apply output filtering to remove any detected PII from responses.

Answer

Increase the fine-tuning dataset size with more varied de-identified records to reduce overfitting, and apply a temperature setting of 0 to make outputs deterministic.

Question 13

You deployed a generative AI model on OCI Model Deployment with autoscaling configured based on average CPU utilization. The model is a large language model that heavily utilizes the GPU. During peak hours, the scaling is too slow to keep up with demand, resulting in high latency for users. You want to improve the responsiveness of autoscaling. Which change should you make?

Accepted Answer

Use GPU utilization as the scaling metric instead of CPU utilization. The model heavily utilizes GPU, not CPU. Autoscaling based on CPU utilization is irrelevant for GPU-bound workloads, leading to delayed scale-out. Using GPU utilization as the scaling metric directly reflects the actual resource bottleneck, enabling faster and more accurate scaling decisions.

Answer

Decrease the target CPU utilization threshold for scale-out

Answer

Increase the maximum number of replicas in the autoscaling configuration

Answer

Increase the cooldown period between scale-out events

Question 14

A RAG system returns irrelevant chunks even though the embedding model and vector index are correctly configured. After reviewing, the chunks are too large and contain extraneous information. Which combination of adjustments should be made to improve relevance?

Accepted Answer

Reduce chunk size, increase overlap, and adjust topK.. Reducing chunk size removes extraneous information, increasing overlap ensures context continuity across smaller chunks, and adjusting topK limits the number of retrieved chunks to the most relevant ones. This combination directly addresses the problem of large chunks containing irrelevant data while maintaining retrieval precision.

Answer

Increase chunk overlap only.

Answer

Decrease chunk size and increase chunk overlap.

Answer

Use semantic chunking and adjust topK.

Question 15

A legal firm needs an AI assistant that can answer questions based on a large corpus of internal regulations that change quarterly. The firm also requires high accuracy and the ability to cite sources. Which approach should the firm choose?

Accepted Answer

Build a RAG application with vector search and citation generation. Retrieval-Augmented Generation (RAG) with vector search allows the legal firm to index its quarterly-changing regulations into a vector database, retrieve the most relevant chunks for each query, and generate answers with source citations. This approach ensures high accuracy by grounding the LLM's output in the current, authoritative documents without requiring retraining, and citation generation provides the necessary source traceability for legal compliance.

Answer

Use a pre-trained model without customization

Answer

Implement a rule-based search engine

Answer

Fine-tune a pre-trained model on the current regulations