1Z0-1127 Practice Test 4 — 25 Questions

Question 1

A company wants to deploy a custom fine-tuned model for retrieval-augmented generation (RAG) using dedicated AI cluster. They need to ensure the model can handle concurrent requests from multiple applications with consistent latency. What should they configure?

Accepted Answer

Increase the number of replicas in the dedicated cluster.. Option A is correct because increasing the number of replicas in the dedicated cluster distributes the load across multiple model copies, improving concurrency and latency stability.

Answer

Set a high temperature to keep responses concise.

Answer

Enable auto-scaling on the cluster.

Answer

Use the managed serving endpoint instead.

Question 2

Refer to the exhibit. A user runs 'oci generative-ai model list' and sees this output. They then try to use 'cohere.command-light' but get an error. What is the most likely reason?

Accepted Answer

The model is in INACTIVE state. Option B is correct because the model 'cohere.command-light' has lifecycle-state 'INACTIVE', meaning it cannot be used. Option A is false because it is listed; C and D would produce different errors.

Answer

The API key does not have access

Answer

The model is not listed

Answer

The region is wrong

Question 3

Which TWO actions are required to use a custom fine-tuned model via OCI Generative AI? (Choose two.)

Accepted Answer

Deploy the model to an endpoint. Options B and D are required. B: Fine-tuning must be complete. D: Model endpoint must be deployed. A is optional (private endpoint). C is not needed if within same region. E is not required unless cross-tenant.

Answer

Provision a private endpoint for the model

Answer

Enable cross-region replication

Answer

Grant access to other tenancies

Question 4

A data scientist is fine-tuning a model on OCI Generative AI with a custom dataset. They receive a "QuotaExceeded" error during training. What is the most likely cause?

Accepted Answer

Exceeded the training compute unit quota. The 'QuotaExceeded' error during fine-tuning on OCI Generative AI specifically indicates that the training job has consumed more compute units than allocated in the service limit. Fine-tuning requires dedicated training compute units (TCUs) which are a separate quota from inference or API calls. When this quota is exhausted, the service rejects new training jobs with this error.

Answer

Exceeded the API call rate limit

Answer

Exceeded the model storage limit

Answer

Exceeded the data transfer out limit

Question 5

Which OCI Generative AI parameter controls the diversity of generated text by increasing the probability of less likely tokens?

Accepted Answer

temperature. The temperature parameter in OCI Generative AI controls the randomness of token selection by scaling the logits before applying the softmax function. A higher temperature (e.g., > 1.0) increases the probability of less likely tokens, making the output more diverse and creative, while a lower temperature (e.g., < 1.0) sharpens the distribution toward the most likely tokens, producing more deterministic and conservative text.

Answer

frequency_penalty

Answer

top_k

Answer

top_p

Question 6

Which THREE parameters can be adjusted to reduce repetition in generated text? (Choose three.)

Accepted Answer

presence_penalty. A is correct because presence_penalty directly penalizes tokens that have already appeared in the generated text, reducing the likelihood of repeating the same concepts or phrases. This parameter works by subtracting a fixed penalty from the logits of tokens that are already present in the sequence, making the model less likely to reuse them.

Answer

max_tokens

Answer

temperature

Question 7

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 8

A developer is using the OCI Generative AI service API and receives a '400 Bad Request' with error 'Model not found'. What is the most likely cause?

Accepted Answer

The model ID is misspelled or does not exist.. The '400 Bad Request' with 'Model not found' error indicates that the model ID provided in the API request does not match any available model in the OCI Generative AI service. This is most commonly caused by a typo, incorrect model name, or referencing a model that has been deprecated or removed from the service catalog.

Answer

The API request lacks authentication.

Answer

The input exceeds the maximum token limit.

Answer

The endpoint region is incorrect.

Question 9

A developer makes an API call to generate text with top_p=1.5. What is the correct way to fix this error?

Accepted Answer

Set top_p to a value between 0 and 1, e.g., 0.9. The `top_p` parameter, also known as nucleus sampling, must be a probability value between 0 and 1. Setting it to 1.5 is invalid because it exceeds the allowed range, which would cause the API to reject the request. The correct fix is to set `top_p` to a valid value such as 0.9, which restricts token selection to the smallest set whose cumulative probability exceeds that threshold.

Answer

Remove the top_p parameter from the request

Answer

Increase the temperature parameter to compensate

Answer

Use the top_k parameter instead

Question 10

Which THREE are valid ways to interact with OCI Generative AI?

Accepted Answer

OCI REST API.. Option C is correct because OCI Generative AI exposes a comprehensive REST API that allows developers to programmatically interact with the service for tasks such as text generation, summarization, and chat. The REST API uses standard HTTP methods and JSON payloads, making it accessible from any language or tool that can send HTTP requests.

Answer

OCI Mobile App.

Answer

OCI Data Science Notebooks.

Question 11

An enterprise RAG system must ensure that retrieved data comes only from authorized sources. Which OCI feature should be used to enforce this?

Accepted Answer

OCI IAM policies for the vector database. Option B is correct because OCI IAM policies allow you to define granular access controls on the vector database, ensuring that only authorized principals (users, groups, or service principals) can read or write data. This directly enforces that retrieved data comes only from authorized sources, which is a core requirement for enterprise RAG systems.

Answer

Data encryption at rest

Answer

Network security groups

Answer

Resource quotas

Question 12

A team fine-tunes an embedding model for a legal document RAG system but observes low retrieval recall. Which technique is most likely to improve recall?

Accepted Answer

Use hard negative mining during training. Hard negative mining exposes the model to challenging negatives during training, which sharpens the embedding space and improves recall.

Answer

Use a smaller batch size

Answer

Reduce the learning rate

Answer

Increase the number of fine-tuning epochs

Question 13

Which TWO of the following are best practices for building a RAG pipeline in OCI?

Accepted Answer

Use overlapping chunks. Overlapping chunks ensure that context is not lost at chunk boundaries, which is critical for retrieval accuracy in RAG pipelines. By including overlapping text segments, the embedding model can capture semantic continuity, reducing the risk of missing relevant information when a query spans chunk edges.

Answer

Always use exact vector search for accuracy

Answer

Avoid storing metadata alongside vectors

Answer

Use a single large chunk for each document

Question 14

An enterprise is using OCI Generative AI with a RAG architecture. They observe that the LLM sometimes produces hallucinated answers that are not supported by the retrieved documents. Which strategy is most effective in reducing these hallucinations?

Accepted Answer

Provide clear instructions in the system prompt to answer only based on the provided context.. Option B is correct because explicitly instructing the LLM to answer only based on the provided context directly addresses the root cause of hallucinations in a RAG pipeline: the model's tendency to rely on its parametric knowledge rather than the retrieved documents. This system prompt acts as a behavioral constraint, forcing the model to ground its responses in the supplied context, which is the most effective and widely recommended mitigation strategy.

Answer

Increase the temperature parameter to make outputs more focused.

Answer

Use a smaller LLM to reduce model capacity.

Answer

Retrieve more chunks (increase top-k) to provide more context.

Question 15

A developer wants to build a RAG application that processes highly sensitive medical records. The documents are already stored in OCI Object Storage. Which vector storage strategy best balances security and performance?

Accepted Answer

Use OCI OpenSearch with a private subnet and VCN security lists. Option C is correct because it uses OCI OpenSearch deployed within a private subnet, which ensures that vector data never traverses the public internet, while VCN security lists provide granular traffic control. This architecture balances security (data isolation and access control) with performance (low-latency access within the same VCN or via FastConnect/IPSEC VPN) for sensitive medical records.

Answer

Store vectors in-memory within the application server

Answer

Use OCI OpenSearch with a public endpoint for low latency

Answer

Use a third-party vector database outside OCI

Question 16

A developer notices that the RAG application returns irrelevant chunks for user queries. The embedding model used is `cohere.embed-english-light-v3.0`. Which action is MOST likely to improve relevance?

Accepted Answer

Switch to a larger embedding model (e.g., cohere.embed-english-v3.0). The `cohere.embed-english-light-v3.0` model is a smaller, faster embedding model that may lack the semantic richness needed to capture nuanced query-document relationships. Switching to the larger `cohere.embed-english-v3.0` model provides higher-dimensional embeddings with better representational capacity, which directly improves the relevance of retrieved chunks in a RAG pipeline.

Answer

Reduce the number of retrieved chunks (k)

Answer

Increase the chunk size

Answer

Use a different similarity metric (e.g., Euclidean instead of cosine)

Question 17

Refer to the exhibit. A developer runs the command and immediately tries to use the endpoint. The application fails with an error indicating the endpoint is not active. What is the most likely reason?

Accepted Answer

The endpoint is in provisioning state and not yet ready. Option C is correct because when a developer creates a new model deployment endpoint in OCI, the endpoint enters a 'provisioning' state and can take several minutes to become active. Attempting to use the endpoint immediately after creation will result in an error indicating the endpoint is not ready, as the underlying infrastructure and model loading must complete before inference requests are accepted.

Answer

The model ID is not available in us-ashburn-1

Answer

The purpose parameter is misspelled

Answer

The compartment ID is incorrect

Question 18

Refer to the exhibit. A developer has set this policy to allow an OCI Data Science session to generate embeddings. However, the API call returns a 403 Forbidden. Which of the following is likely missing?

Accepted Answer

The dynamic group does not include the Data Science session. The 403 Forbidden error indicates that the Data Science session does not have the necessary permissions to call the API. In OCI, policies grant permissions to dynamic groups, not directly to resources. The dynamic group must include the Data Science session (e.g., by matching the session's OCID or resource type) for the policy to apply. Without this, the session is not recognized as a principal, and the API call is denied.

Answer

The policy needs a 'where request.region != ...' condition

Answer

The policy should include 'in tenancy' instead of compartment

Answer

The service requires 'manage' permission instead of 'use'

Question 19

An organization wants to combine keyword search and vector search to improve retrieval accuracy in their RAG pipeline. Which OCI service provides built-in hybrid search capabilities?

Accepted Answer

OCI OpenSearch. OCI OpenSearch is the correct answer because it natively supports hybrid search, which combines keyword-based (BM25) and vector-based (k-NN) queries in a single search request. This allows the RAG pipeline to retrieve documents that match both exact terms and semantic meaning, improving overall accuracy without requiring separate search systems.

Answer

OCI Search with AI

Answer

Autonomous Database with AI Vector Search

Answer

OCI Logging

Question 20

A company's RAG application ingests news articles that are updated frequently. The vector store in OCI OpenSearch contains embeddings of the articles. The team notices that outdated information is still retrieved even after updating the source documents. What is the most effective way to ensure the vector store reflects the latest content?

Accepted Answer

Use the OCI OpenSearch document update API to replace embeddings for changed documents. Using the OCI OpenSearch document update API to replace embeddings for changed documents is efficient and targeted, ensuring immediate consistency.

Answer

Increase the TTL for vector indices

Answer

Rely on the LLM to ignore outdated information

Answer

Re-index the entire vector store daily