1Z0-1127-25 Practice Exam 53 — 20 Questions

Question 1

A company wants to use OCI Generative AI to build a multilingual customer support chatbot. They need to understand customer queries in multiple languages and generate responses in the same language. Which TWO actions should they take? (Choose two.)

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

Accepted Answer

Select Cohere Command R as the base model for chat. Cohere Command R (or R+) supports multilingual input and output natively, so fine-tuning is unnecessary. Using the embed-multilingual-v3.0 model for embedding customer queries enables multilingual semantic search if RAG is used. Embed-english-v3.0 only supports English, and Llama 3 is primarily English-focused.

Answer

Use the embed-english-v3.0 model for embedding queries

Answer

Fine-tune Meta Llama 3 on multilingual data

Answer

Use the Summarisation API for each language

Question 2

A developer is using the OCI Generative AI Generate API (not Chat API) to create a single-turn text completion. They need to include a system-level instruction that guides the model's behavior for that request. Which parameter should they use?

Accepted Answer

'preamble_override' parameter. The Generate API uses 'preamble_override' to set a system instruction for the completion. The 'system' parameter is for the Chat API. 'max_tokens' and 'temperature' are not system instructions.

Answer

'temperature' parameter

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'system' parameter

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'max_tokens' parameter

Question 3

A company deploys a fine-tuned model on an OCI Generative AI dedicated AI cluster. After deployment, they observe high latency during peak hours. The cluster has only one replica. Which action would most effectively reduce latency without increasing cost unnecessarily?

Accepted Answer

Enable auto-scaling with a maximum of 3 replicas.. Enabling auto-scaling with a maximum of 3 replicas (Option B) is the most effective action because it dynamically adds replicas during peak hours to handle increased load, reducing latency, while limiting the maximum to 3 prevents unnecessary cost overruns. This balances performance and cost, unlike a fixed large replica count or switching models, which either wastes resources or fails to address the root cause of insufficient compute capacity.

Answer

Increase the number of replicas to 10.

Answer

Switch to a larger base model.

Answer

Move to a serverless deployment model.

Question 4

A developer is using OCI Generative AI Service to generate code snippets. They want to ensure the output is as deterministic as possible for testing. Which combination of parameters should they use?

Accepted Answer

Temperature = 0, Top-p = 1. Setting Temperature=0 makes the model deterministic by always selecting the highest-probability token, while Top-p=1 includes all tokens in the sampling pool, ensuring no additional randomness is introduced. This combination eliminates stochastic variation, making outputs repeatable for testing.

Answer

Temperature = 0.5, Top-p = 0.5

Answer

Temperature = 0, Top-p = 0

Answer

Temperature = 1, Top-p = 1

Question 5

What is the primary benefit of using a Dedicated AI Cluster over On-Demand serving for deploying generative AI models on OCI?

Accepted Answer

Higher throughput and lower latency due to reserved capacity. A Dedicated AI Cluster provides reserved compute capacity on OCI, ensuring consistent high throughput and low latency for generative AI inference workloads. Unlike On-Demand serving, which shares resources and can suffer from contention or cold starts, a Dedicated AI Cluster guarantees that GPU resources are always available for your model, eliminating variability in response times.

Answer

No need to manage model versions

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Automatic scaling to zero when not in use

Answer

Lower cost for variable workloads

Question 6

Your organization has deployed a generative AI model for a multilingual translation service on OCI Model Deployment. The model is a 13B parameter transformer hosted on a single VM.GPU.A100.1 shape with 2 replicas. Recently, the service experiences intermittent timeouts when a burst of requests arrives. You have enabled autoscaling based on CPU utilization, but the scaling is too slow. After investigation, you find that the model inference time is highly variable due to different sequence lengths. You need to ensure the service can handle sudden spikes without timeouts. Which solution should you implement?

Accepted Answer

Implement a request queue (e.g., OCI Queue) to buffer requests and process them asynchronously. Implementing a request queue (e.g., OCI Queue) decouples request ingestion from processing, allowing the service to buffer bursts of requests and process them asynchronously. This prevents timeouts by smoothing out the variable inference times caused by differing sequence lengths, as the queue absorbs spikes and the model processes at its own pace. Autoscaling based on CPU utilization is too slow for sudden spikes, but a queue provides immediate relief by not dropping requests.

Answer

Increase the maximum number of replicas and prewarm additional replicas before expected traffic

Answer

Reduce the model size to a 7B parameter model to decrease inference time

Answer

Use autoscaling based on the number of messages in the request queue

Question 7

A developer is using OCI Generative AI to build a question-answering system over a large corpus of technical manuals. The developer uses the Cohere Embed model to generate embeddings and stores them in an OCI OpenSearch cluster. Queries are slow and the team needs to reduce latency. Which approach is BEST for improving search speed while maintaining acceptable accuracy?

Accepted Answer

Reduce the k value in the nearest neighbor search.. Reducing the k value in the nearest neighbor search directly decreases the number of vectors that must be compared during query time, which lowers latency. In approximate nearest neighbor (ANN) search, a smaller k means fewer candidates are evaluated, speeding up retrieval while still maintaining acceptable accuracy if the original k was unnecessarily high. This is the most effective tuning knob for latency in vector search systems like OCI OpenSearch with Cohere embeddings.

Answer

Increase the embedding dimension for better representation.

Answer

Use exact nearest neighbor search instead of approximate.

Answer

Increase the index refresh interval to reduce write overhead.

Question 8

Refer to the exhibit. A user in group GenAIUsers reports that they cannot call the OCI Generative AI API. What is the most likely issue?

Accepted Answer

The compartment ID in the policy does not match the user's compartment.. The policy is scoped to a specific compartment ID, but the user's compartment does not match that ID. For OCI IAM policies to grant access to resources like the Generative AI API, the policy must be written for the compartment where the resource resides or where the user operates. Since the user is in a different compartment, the policy does not apply, causing the API call to fail.

Answer

The policy statement is missing the 'inspect' verb.

Answer

The policy is in INACTIVE state.

Answer

The user is not in the group GenAIUsers.

Question 9

A company has multiple teams sharing an OCI Generative AI Dedicated AI Cluster. They need to ensure that each team can only access their own fine-tuned models and cannot see or invoke models from other teams. What is the best approach?

Accepted Answer

Use OCI compartments and IAM policies with resource-level permissions for models. OCI compartments and IAM policies with resource-level permissions allow you to grant granular access to specific models within a Dedicated AI Cluster. By placing each team's fine-tuned models in separate compartments and writing policies that restrict access to those compartments, you ensure teams can only see and invoke their own models. This approach leverages OCI's native identity and access management without requiring separate clusters or network-level isolation.

Answer

Train separate models for each team

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Encrypt model artifacts with different keys for each team

Answer

Use network security lists to isolate traffic

Question 10

A data scientist wants to improve the accuracy of a summarization model on medical texts. Which OCI service feature is most suitable?

Accepted Answer

OCI Generative AI fine-tuning. C is correct because OCI Generative AI fine-tuning allows a data scientist to adapt a pre-trained large language model (LLM) specifically for medical text summarization by training it on domain-specific data. This improves accuracy by aligning the model's outputs with the terminology, context, and nuances of medical literature, which generic models may not capture well.

Answer

OCI Data Flow

Answer

OCI Language service

Answer

OCI Anomaly Detection

Question 11

A company is building a RAG application using OCI Generative AI and OCI Search with OpenSearch. Users report that the responses from the LLM are not relevant to the queries, even though the document chunks seem appropriate. What is the most likely cause?

Accepted Answer

Reranking is not enabled in the OpenSearch query.. Enabling reranking improves the relevance of retrieved documents by reordering them based on semantic match with the query. Without reranking, the initial vector search results may not be optimally ordered.

Answer

The embedding model is not suited for the domain.

Answer

The top K value is set too high.

Answer

The chunk size is too small, causing loss of context.

Question 12

An organization needs to extract text from PDF documents and convert them into embeddings for a RAG pipeline using OCI. Which OCI service is best suited for extracting text from PDFs?

Accepted Answer

OCI Document Understanding. OCI Document Understanding is purpose-built for extracting text, tables, and key-value pairs from PDFs and images using pre-trained AI models. It directly supports the text extraction step required to prepare documents for embedding generation in a RAG pipeline, unlike the other services which focus on different modalities or lack native PDF text extraction capabilities.

Answer

OCI Language

Answer

OCI Speech

Answer

OCI Vision

Question 13

A team is fine-tuning an LLM on OCI Generative AI for a domain-specific task. They have a dataset of 10,000 labeled examples. What is a best practice to avoid catastrophic forgetting during fine-tuning?

Accepted Answer

Include a small percentage of general-domain data in the training mix.. Catastrophic forgetting occurs when a fine-tuned model loses previously learned general knowledge. By including a small percentage (e.g., 5–10%) of general-domain data in the training mix, the model retains its broad capabilities while adapting to the new domain-specific task. This technique, often called 'replay' or 'experience replay,' is a standard practice in continual learning for LLMs.

Answer

Increase the learning rate to speed up adaptation.

Answer

Use only the new domain-specific data for fine-tuning.

Answer

Reduce the number of training epochs to the minimum.

Question 14

A developer wants to compare two sentences for semantic similarity using embeddings. Which distance or similarity metric is most commonly used for dense vector representations?

Accepted Answer

Cosine similarity. Cosine similarity measures the cosine of the angle between two vectors, is commonly used for comparing embedding vectors, and ranges from -1 to 1, where 1 indicates identical direction.

Answer

Jaccard similarity

Answer

Manhattan distance

Answer

Euclidean distance

Question 15

Which TWO actions should be taken to monitor model drift in a deployed generative AI model? (Select TWO)

Accepted Answer

Compare inference statistics over time. Comparing inference statistics over time (Option A) is correct because model drift in generative AI is detected by monitoring changes in output distributions, token probabilities, or response patterns relative to baseline metrics. This allows you to identify when the model's behavior deviates from expected performance due to shifts in input data or underlying patterns.

Answer

Retrain the model weekly

Answer

Use OCI Data Labeling for new data

Answer

Deploy multiple model versions

Question 16

A company is deploying a LangChain application on OCI and needs to implement error handling and rate limit management. Which THREE strategies should they consider? (Choose THREE.)

Accepted Answer

Implement retry logic with exponential backoff when receiving 429 (Too Many Requests) responses. A is correct because HTTP 429 (Too Many Requests) responses indicate rate limiting by the API provider. Implementing retry logic with exponential backoff is a standard resilience pattern that progressively increases wait times between retries, preventing further rate limit violations and allowing the system to recover gracefully without overwhelming the endpoint.

Answer

Increase the chunk_size parameter in the text splitter

Answer

Resubscribe to the model endpoint if errors occur

Question 17

An organization is concerned about the safety of generated content. Which OCI feature allows them to define custom policies to block inappropriate outputs?

Accepted Answer

Content filtering and safety controls in Generative AI. OCI Generative AI includes built-in content filtering and safety controls that allow organizations to define custom policies to block inappropriate or harmful outputs. These controls operate at the model inference layer, enabling fine-grained filtering based on categories such as toxicity, hate speech, or personally identifiable information (PII). This directly addresses the concern about generated content safety.

Answer

OCI IAM policies

Answer

OCI Audit logs

Answer

OCI Vault

Question 18

Which THREE factors should be considered when choosing between a fine-tuning and a prompt engineering approach?

Accepted Answer

Need for model personalization. Model personalization is a key driver for choosing fine-tuning over prompt engineering. Fine-tuning modifies the model's weights to adapt it to a specific domain or task, enabling deeper customization that prompt engineering alone cannot achieve, especially when the desired behavior requires learning new patterns or knowledge not present in the base model.

Answer

Latency requirements

Answer

Availability of foundation model in OCI

Question 19

A data scientist needs to generate embeddings for a collection of documents to be used for both clustering and semantic search. They want to use appropriate input types for each task. Which TWO input types should they use from the Cohere Embed API? (Choose two.)

Accepted Answer

search_document. For clustering, the 'clustering' input type is appropriate. For semantic search, 'search_document' (for documents to be searched) and 'search_query' (for queries) are used. The question asks for two options that cover both tasks; 'clustering' and 'search_document' are correct. 'search_query' is for queries, not documents, and 'classification' is for classification tasks.

Answer

search_query

Answer

embedding

Answer

classification

Question 20

Given the CLI output from `oci generative-ai model list`, what can be determined about the model 'my-fine-tuned-model'?

Accepted Answer

It was created by fine-tuning an existing base model. The CLI output from `oci generative-ai model list` includes a model named 'my-fine-tuned-model'. In OCI Generative AI, models listed with custom names that are not part of the base model catalog (e.g., cohere.command, meta.llama) indicate they were created by fine-tuning a base model using your own dataset. The presence of a custom name without a base model prefix confirms it is a fine-tuned model, not a pre-built one.

Answer

It is a pre-built model provided by OCI

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It has been deployed to an endpoint

Answer

It is currently being trained