1Z0-1127-25 Practice Test 34 — 15 Questions

Question 1

An e-commerce company uses OCI Generative AI to generate product descriptions. They have fine-tuned the model on their product catalog. They notice that the descriptions are accurate but lack creativity and are repetitive. They want to maintain accuracy while adding variety. What change should they make?

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

Accepted Answer

Increase the temperature from 0.2 to 0.5.. Slightly increasing temperature (e.g., from 0.2 to 0.5) introduces controlled variability without significantly compromising accuracy. Option A is wrong because top_p=1.0 samples from the full distribution, which can add noise. Option C is wrong because adding more training data requires effort and time, and may not immediately add variety. Option D is wrong because changing the base model could hurt accuracy and requires retraining.

Answer

Increase the top_p sampling from 0.9 to 1.0.

Answer

Use a different base model.

Answer

Add more training examples with diverse descriptions.

Question 2

A company wants to create a chatbot that answers questions based on a large internal document set that is updated weekly. They have limited ML expertise. Which approach is recommended?

Accepted Answer

Use retrieval-augmented generation (RAG) with a vector database.. Retrieval-Augmented Generation (RAG) with a vector database is the recommended approach because it allows the chatbot to answer questions based on a large, frequently updated document set without requiring model retraining. RAG retrieves relevant document chunks at query time using vector similarity search, then passes them as context to the LLM, ensuring up-to-date answers with minimal ML expertise.

Answer

Fine-tune a model on the entire document set.

Answer

Train a custom model from scratch.

Answer

Include all documents in the system prompt.

Question 3

A multinational corporation plans to deploy OCI Generative AI in multiple OCI regions for disaster recovery. They have fine-tuned a custom model in the primary region. What is the recommended approach to make the fine-tuned model available in the secondary region with minimal manual effort?

Accepted Answer

Copy the model artifact to the secondary region's object storage bucket and create a new dedicated endpoint there.. The recommended approach to make a fine-tuned custom model available in a secondary OCI region with minimal manual effort is to copy the model artifact (the trained weights and configuration files) to the secondary region's object storage bucket and then create a new dedicated endpoint there. This avoids re-running the expensive fine-tuning job and leverages OCI's object storage cross-region copy capabilities, while the dedicated AI cluster in the secondary region can serve the model directly from the copied artifact.

Answer

Create an IAM policy to allow cross-region access to the model from the secondary region.

Answer

Use OCI Cross-Region Replication for the model's underlying object storage bucket and the dedicated AI cluster.

Answer

Redeploy the fine-tuning job in the secondary region using the same training data.

Question 4

An OCI GenAI practitioner wants to deploy a model that can generate code from natural language descriptions. Which type of model is most suitable?

Accepted Answer

GPT. GPT (Generative Pre-trained Transformer) is the most suitable model for code generation from natural language because it is an autoregressive language model optimized for text generation tasks. Unlike encoder-only models, GPT generates coherent, contextually relevant sequences of tokens, making it ideal for producing code based on descriptive prompts.

Answer

T5

Answer

ResNet

Answer

BERT

Question 5

Which TWO deployment options are available for using fine-tuned models with OCI Generative AI service?

Accepted Answer

On-Demand Endpoint. The OCI Generative AI service provides two deployment options for fine-tuned models: On-Demand Endpoint and Managed Dedicated Endpoint. The On-Demand Endpoint (Option C) is a serverless, pay-per-token option that automatically scales, suitable for variable workloads. The Managed Dedicated Endpoint (Option E) provides a dedicated, single-tenant endpoint with guaranteed throughput and lower latency for production workloads.

Answer

Bring Your Own Container (BYOC)

Answer

Serverless Endpoint

Answer

Edge Deployment

Question 6

An AI specialist is troubleshooting why a fine-tuned model produces inconsistent results across different inference calls. What is the most likely cause?

Accepted Answer

The temperature is set too high. Temperature controls the randomness of token sampling during inference. A high temperature (e.g., >1.0) increases the probability of selecting less likely tokens, causing the model to produce varied outputs for the same input across different calls. This is the most direct cause of inconsistent results when the base model and fine-tuning are otherwise sound.

Answer

The base model is not suitable

Answer

The model is overfitted

Answer

The fine-tuning dataset is too small

Question 7

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 8

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 9

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. To use a custom fine-tuned model via OCI Generative AI, you must first successfully complete the fine-tuning job (E) to create the model, and then deploy it to an endpoint (A) to enable inference. Options B (private endpoint) is optional for networking control, not required. C (cross-region replication) is not needed. D (granting access to other tenancies) is only required if you want to share the model across tenancies, not for your own usage.

Answer

Provision a private endpoint for the model

Answer

Enable cross-region replication

Answer

Grant access to other tenancies

Question 10

A prompt engineer is troubleshooting a chatbot that consistently fails to follow instructions when the user includes adversarial input. Which two strategies can mitigate prompt injection attacks? (Choose two.)

Accepted Answer

Use instruction shielding: clearly separate system instructions from user input. Instruction shielding (clear separation of instruction and input) and input validation/sanitization are effective defenses. Adding more examples or adjusting temperature do not address injection.

Answer

Increase temperature to make model less predictable

Answer

Use a smaller model to reduce capability

Answer

Add more few-shot examples with safe outputs

Question 11

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. 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 12

When invoking the OCI Generative AI service from a RAG application, the developer receives a 401 Unauthorized error. The application uses resource principal authentication from an OCI Data Science notebook session. What is the most likely fix?

Accepted Answer

Ensure the dynamic group includes the data science notebook session and has the correct policy. The 401 Unauthorized error indicates that the OCI Generative AI service cannot authenticate the request. In a RAG application using resource principal authentication from an OCI Data Science notebook session, the notebook session must be a member of a dynamic group, and that dynamic group must have an IAM policy granting it the necessary permissions (e.g., `allow dynamic-group to use generative-ai-family in compartment `). Option C correctly identifies that the dynamic group configuration or policy is missing or incorrect, which is the most likely cause of the authentication failure.

Answer

Add the Generative AI service to the subnet's security list

Answer

Use an API key instead of resource principal

Answer

Restart the notebook session

Question 13

A company is deploying OCI Generative AI for a chatbot that must answer customer queries within 500ms. They choose a dedicated AI cluster but observe 2-second latency. What is the most likely cause?

Accepted Answer

The cluster is configured for batch inference. A dedicated AI cluster in OCI Generative AI is designed for real-time inference with low latency. When the cluster is configured for batch inference, it processes requests in batches rather than individually, which introduces queuing and processing delays that can easily exceed the 500ms target. This explains the observed 2-second latency, as batch mode prioritizes throughput over per-request response time.

Answer

The endpoint is not cached

Answer

The request includes too many tokens

Answer

The model is too large for the cluster

Question 14

Which TWO of the following are valid approaches to serve a RAG application in OCI with low latency?

Accepted Answer

Implement a caching layer for frequently asked questions.. Implementing a caching layer for frequently asked questions reduces redundant LLM invocations and vector store queries, directly lowering latency for repeated queries. This approach leverages in-memory caches like Redis or Memcached to serve precomputed responses, bypassing the retrieval and generation pipeline for cached items.

Answer

Pre-compute embeddings and answers for all possible questions.

Answer

Deploy the vector store on multiple regions to reduce network latency.

Answer

Increase the chunk size to reduce the number of retrievals.

Question 15

An organization wants to fine-tune a large language model on OCI using their proprietary data. They are concerned about data privacy and want to ensure that fine-tuning data does not leave the OCI region. Which OCI service should they use to securely store and manage their training data?

Accepted Answer

OCI Object Storage. C is correct because OCI Object Storage is a regional service that stores data within a specific OCI region, ensuring that fine-tuning data does not leave that region. It provides secure, durable, and scalable storage for large datasets, such as training data for LLMs, with encryption at rest and in transit, and supports direct integration with OCI Data Science and Generative AI services for fine-tuning workflows.

Answer

OCI Block Volume

Answer

OCI File Storage

Answer

Oracle Autonomous Database