1Z0-1127-25 Practice Test 48 — 15 Questions

Question 1

A company needs to integrate OCI Generative AI Service with an existing application that uses OCI IAM for authentication. They want to use resource principal to allow the application to call the service without storing API keys. Which step is REQUIRED?

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

Accepted Answer

Create a dynamic group and a policy granting access to the Generative AI Service. Resource principal authentication in OCI requires the application to be represented by a dynamic group, which matches instances or resources based on defined rules. A policy must then grant that dynamic group access to the Generative AI Service. This avoids storing API keys by using OCI IAM's built-in resource principal token exchange.

Answer

Create an OCI API key for the application

Answer

Enable the Generative AI Service for resource principal in the tenancy

Answer

Assign the application to a group with admin privileges

Question 2

An OCI administrator is configuring access control for OCI Generative AI. Which three IAM components are required to allow a group of data scientists to call the GenerateText API? (Choose three.)

Accepted Answer

An IAM group for the data scientists. An IAM group is required to organize the data scientists into a logical set of principals. IAM policies are then attached to this group to grant permissions, ensuring only members of the group can call the GenerateText API. Without a group, you cannot apply a policy to a collection of users.

Answer

A local peering gateway

Answer

A dynamic group

Question 3

A team wants to use the Embedding API to convert product descriptions into vectors for a semantic search application. They have descriptions in English and Spanish. Which embedding model should they use?

Accepted Answer

embed-multilingual-v3.0. embed-multilingual-v3.0 supports multiple languages including English and Spanish.

Answer

embed-english-v3.0

Answer

Cohere Command R

Answer

Cohere Rerank

Question 4

A data scientist wants to fine-tune a Cohere Command R model using the T-Few technique. They have prepared a dataset in JSONL format with prompt/completion pairs. Which step is REQUIRED before creating the fine-tuning job?

Accepted Answer

Upload the dataset to an OCI Object Storage bucket. The dataset must be uploaded to an OCI Object Storage bucket so the fine-tuning job can access it. The other options are either optional or not required.

Answer

Register the dataset in OCI Data Labeling

Answer

Deploy a dedicated AI cluster to host the base model

Answer

Create an OCI Functions endpoint for dataset preprocessing

Question 5

During a fine-tuning job for a text generation model, the loss curve shows that the training loss decreases steadily, but the evaluation loss increases after a few epochs. Which action is most likely to improve the model's generalization?

Accepted Answer

Implement early stopping based on evaluation loss. Increasing evaluation loss while training loss decreases indicates overfitting. Early stopping prevents further training once evaluation metrics plateau or degrade, improving generalization. Reducing dataset size or increasing epochs would worsen overfitting; increasing temperature does not affect training.

Answer

Increase the number of training epochs

Answer

Reduce the size of the training dataset

Answer

Increase the temperature parameter during generation

Question 6

During iterative prompt refinement, a team evaluates two prompt variants on 100 test queries. Variant A scores 85% accuracy but occasionally generates offensive content. Variant B scores 80% accuracy with no safety issues. Which evaluation criterion should take priority for a customer-facing application?

Accepted Answer

Safety — offensive content is unacceptable in a customer-facing system. For customer-facing applications, safety is paramount. Even if accuracy is slightly lower, ensuring no offensive content is critical to avoid reputational and legal risks. The team should prioritize safety and then work to improve accuracy.

Answer

Accuracy — because it is highest and the offensive content can be filtered post-hoc

Answer

Cost — the variant with higher accuracy uses fewer tokens

Answer

Latency — because the variant with higher accuracy also has lower latency

Question 7

A financial services company deployed a fine-tuned model using OCI Generative AI Service to generate investment advice based on quarterly reports. The model was trained on 10,000 labeled examples and achieved high accuracy in testing. However, after three months in production, the model's outputs have become inconsistent and sometimes recommend investments based on outdated market conditions. The team has received multiple complaints from users about inaccurate advice. The model is deployed on a dedicated AI cluster with auto-scaling disabled. The OCI audit logs show no configuration changes. The team suspects data drift and wants to mitigate it without incurring high costs. They have a pipeline that can collect new labeled data monthly, but it takes two weeks to process. What should the team do?

Accepted Answer

Set up a monthly retraining schedule using the new labeled data as soon as it is available, and use a champion/challenger deployment to validate the new model before full rollout.. It directly addresses data drift by establishing a regular retraining cycle with the new labeled data, which is the standard mitigation strategy for model degradation over time. The champion/challenger deployment pattern allows the team to validate the updated model's performance against the current production model before full rollout, ensuring no regression in accuracy. This approach balances cost efficiency (monthly retraining) with the operational constraint of a two-week data processing pipeline.

Answer

Decrease the temperature parameter to 0.1 to make outputs more deterministic.

Answer

Revert to the base model (Cohere Command) and use few-shot prompting with recent reports.

Answer

Enable auto-scaling on the dedicated AI cluster to handle increased load.

Question 8

In a LangChain RAG pipeline using Oracle AI Vector Search, the developer wants to retrieve chunks that are both relevant and diverse to cover multiple aspects of a query. Which retrieval method should they configure on the retriever?

Accepted Answer

Maximal Marginal Relevance (MMR). Maximal Marginal Relevance (MMR) is the correct retrieval method because it explicitly balances relevance to the query with diversity among the retrieved chunks. In a LangChain RAG pipeline using Oracle AI Vector Search, MMR re-ranks the initial similarity results to minimize redundancy, ensuring the final set covers multiple aspects of the query rather than returning near-duplicate chunks.

Answer

Threshold-based search

Answer

Random sampling of the top-k results

Answer

Similarity search with a high k value

Question 9

A developer is building a RAG pipeline using OCI Data Science and wants to store vector embeddings. Which OCI service is optimized for vector search and can be used as a vector store?

Accepted Answer

OCI OpenSearch. B is correct because OCI OpenSearch is a fully managed, search and analytics engine that natively supports k-nearest neighbor (k-NN) search on dense vector embeddings. It provides optimized indexing and querying for high-dimensional vectors, making it the ideal vector store for a RAG pipeline in OCI Data Science.

Answer

OCI Autonomous Database

Answer

OCI Object Storage

Answer

OCI Streaming

Question 10

A developer wants to deploy a custom generative AI model that was trained using OCI Data Science. Which service should they use to expose the model as an API endpoint?

Accepted Answer

OCI Data Science Model Deployment. B is correct because OCI Data Science Model Deployment is specifically designed to host and serve machine learning models as REST API endpoints. It directly deploys models trained in OCI Data Science, managing the underlying infrastructure, scaling, and providing a secure HTTPS endpoint for inference requests.

Answer

OCI API Gateway

Answer

OCI Functions

Answer

OCI Generative AI service

Question 11

In a Transformer model, what is the role of positional encoding?

Accepted Answer

To provide information about the position of each token in the sequence. Positional encoding is essential in Transformer models because the self-attention mechanism processes all tokens in parallel and has no inherent notion of sequence order. By adding positional encodings (often sinusoidal or learned) to the input embeddings, the model can distinguish between tokens at different positions, enabling it to capture word order and relative positions. Without this, the model would treat the sequence as a bag of tokens, losing all sequential context.

Answer

To reduce the number of parameters in the model

Answer

To enable the model to process tokens in parallel

Answer

To encode the semantic meaning of each token

Question 12

A company has deployed a model on a Dedicated AI Cluster and needs to monitor inference performance metrics such as request latency, throughput, and error rates. Which OCI service provides built-in monitoring dashboards for these metrics?

Accepted Answer

OCI Monitoring. OCI Monitoring is the correct service because it provides built-in dashboards and metrics for inference performance, including request latency, throughput, and error rates, specifically for Dedicated AI Cluster deployments. These metrics are automatically collected and visualized in the OCI Monitoring console, allowing real-time tracking of model inference health without additional configuration.

Answer

OCI Logging

Answer

OCI Notifications

Answer

OCI Events

Question 13

An organization is deploying an LLM for document question answering. They want to reduce hallucinations and ensure answers are grounded in provided documents. Which THREE techniques should they implement? (Choose three.)

Accepted Answer

Fine-tune the model on a corpus of in-domain documents. RAG retrieves relevant document chunks and conditions the generation on them, reducing hallucination. Fine-tuning on the document domain can improve grounding. Using a lower temperature (closer to 0) makes the model more deterministic and less likely to fabricate. Higher temperature increases hallucination risk, and longer context window alone does not guarantee grounding.

Answer

Use a longer context window to include more document text

Answer

Set a high temperature (e.g., 1.5) for sampling

Question 14

A prompt engineer is designing a system that generates step-by-step recipes for users. Which TWO prompt patterns are MOST relevant for this task?

Accepted Answer

Recipe patterns. Recipe patterns are step-by-step instructions by definition. Template patterns allow reusability with placeholders for ingredients or steps. Role prompting could set persona but is not specific to recipes.

Answer

Role prompting

Answer

Zero-shot prompting

Answer

ReAct pattern

Question 15

Which THREE of the following are best practices when deploying a generative AI model on OCI?

Accepted Answer

Set up autoscaling for the endpoint.. Autoscaling ensures that the generative AI endpoint can dynamically adjust compute resources based on real-time inference traffic, maintaining low latency and high availability while optimizing cost. On OCI, autoscaling policies can be configured for dedicated AI clusters to scale the number of model serving replicas in response to metrics like CPU utilization or request queue depth.

Answer

Store API keys in the model endpoint configuration.

Answer

Disable logging to save costs.