1Z0-1127-25 Practice Test 3 — 10 Questions

Question 1

A team wants to use OCI Generative AI to generate synthetic data for training a model. They are concerned about the cost of API calls. Which pricing model would be most cost-effective for high-volume batch processing?

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

Accepted Answer

Reserved capacity with a fixed monthly fee. Reserved capacity with a fixed monthly fee provides the lowest per-request cost for high-volume batch processing. OCI Generative AI offers dedicated capacity pricing, which is ideal for predictable, large-scale workloads where you commit to a certain throughput, avoiding per-request charges that would accumulate significantly with high volume.

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OCI Universal Credits with per-request charges

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Monthly subscription with limited requests

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Pay-as-you-go per request

Question 2

A user wants to use OCI Generative AI to generate marketing copy. They want the output to be more creative and varied. Which parameter should they adjust?

Accepted Answer

Increase the temperature parameter.. Increasing the temperature parameter makes the model's output more random and diverse, which is ideal for creative tasks like generating marketing copy. A higher temperature (e.g., 0.7–1.0) increases the probability of sampling less likely tokens, leading to more varied and imaginative text. Setting temperature to 0 would make the output deterministic and repetitive, which is the opposite of what the user wants.

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Set temperature to 0.

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Decrease the temperature parameter.

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Increase the max_tokens parameter.

Question 3

A company is using OCI Generative AI service to generate product descriptions. They notice that the model sometimes generates biased content. Which approach should they take to mitigate bias while maintaining performance?

Accepted Answer

Fine-tune the model with a balanced, curated dataset that reduces bias. Fine-tuning with a balanced, curated dataset directly addresses the root cause of bias by adjusting the model's internal weights to reduce reliance on biased patterns in the original training data. This approach preserves the model's generative performance for product descriptions because it retrains only on domain-specific, unbiased examples, unlike post-processing which merely filters outputs without correcting the underlying model behavior.

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Use a larger model without fine-tuning

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Post-process outputs to remove biased phrases

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Switch to a different pre-built model from OCI

Question 4

Which TWO of the following are valid ways to consume OCI Generative AI models?

Accepted Answer

Using the OCI Console chat interface. A is correct because the OCI Console provides a built-in chat interface that allows users to interact directly with Generative AI models without writing any code. This interface is part of the OCI Generative AI service's web-based console, enabling prompt testing and model evaluation through a graphical user interface.

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Using the OCI CLI

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Using OCI SDK for Python

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Using OCI Data Flow

Question 5

A multi-turn chatbot needs to maintain context across user queries. The context window is limited. What design should be used?

Accepted Answer

Use a summary of previous turns and add new input.. Summarizing previous turns and appending the new input efficiently manages the limited context window of large language models (LLMs). This approach preserves essential conversational context without exceeding token limits, ensuring coherent multi-turn interactions.

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Store context in a separate database and retrieve each time.

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Reset context after each turn.

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Keep the entire conversation history in each request.

Question 6

A company deploys a large language model on a dedicated AI cluster with 4 nodes. The model requires 128 GB of memory per instance, but the nodes have only 64 GB each. During inference, the nodes experience out-of-memory errors. What is the best solution?

Accepted Answer

Enable model parallelism across nodes. Model parallelism splits the model's layers or parameters across multiple nodes, allowing the 128 GB model to be distributed across the 4 nodes (each with 64 GB) so that no single node exceeds its memory capacity. This is the best solution because it directly addresses the memory constraint without requiring additional hardware or sacrificing inference throughput, and it is a standard technique for deploying large language models on distributed AI clusters.

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Increase the number of nodes to 8

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Upgrade to higher memory node shapes

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Reduce the batch size in inference requests

Question 7

A healthcare company is using OCI GenAI to generate patient summaries from clinical notes. The model output sometimes includes hallucinated medical facts, such as incorrect dosages or diagnoses, which could be dangerous. The team needs to improve factual accuracy while maintaining data privacy. They have a large collection of internal medical knowledge bases (clinical guidelines, drug databases) that are stored in OCI Object Storage. The current implementation uses a zero-shot prompt with the base Cohere Command model. The data science team has limited GPU resources and wants to avoid building a complex pipeline. Which course of action best addresses the hallucination problem?

Accepted Answer

Implement a RAG pipeline that retrieves relevant documents from the internal knowledge bases and includes them in the prompt.. A Retrieval-Augmented Generation (RAG) pipeline directly addresses hallucination by grounding the model's output in verified, internal medical knowledge bases stored in OCI Object Storage. This approach retrieves relevant clinical guidelines or drug database entries and includes them in the prompt, providing factual context without requiring fine-tuning or complex GPU-intensive pipelines. It also preserves data privacy by keeping sensitive medical data within OCI and avoids exposing it to external model training.

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Increase the temperature parameter to 0.9 to encourage more deterministic outputs.

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Use prompt engineering to add 'Only provide facts that are absolutely certain.'

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Fine-tune the Cohere model on a publicly available medical dataset like PubMed.

Question 8

A team is deploying a generative AI model using OCI Functions for serverless inference. They are experiencing cold start latency of over 10 seconds for the first invocation after idle periods. What is the best strategy to reduce cold start latency?

Accepted Answer

Use provisioned concurrency to keep a set number of function instances warm.. OCI Functions supports provisioned concurrency, which keeps a specified number of instances warm. Option A (increasing memory) can reduce cold start but not as effectively. Option C (reducing timeout) might cause failures. Option D (using OCI Data Flow) is for data processing, not inference.

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Migrate the inference to OCI Data Flow for better performance.

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Reduce the function timeout to force faster execution.

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Increase the memory allocation for the function.

Question 9

Refer to the exhibit. An administrator receives the error shown when attempting to deploy a custom model. What is the most likely cause?

Accepted Answer

The user or service does not have permission to read the model artifact from Object Storage. The error indicates that the deployment process cannot access the model artifact stored in Object Storage. In OCI Generative AI, the service must have read permission on the bucket and object to download the artifact. If the user or service principal lacks the necessary IAM policy (e.g., `allow service generative-ai to read objects in compartment X where target.bucket.name='Y'`), the deployment fails with this access-denied error.

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The compartment ID is incorrect

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The model artifact file is corrupted

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The dedicated AI cluster ID is invalid

Question 10

A user wants to access the OCI Generative AI service programmatically. Which credential method is recommended for use in a production application running on OCI Compute?

Accepted Answer

Instance principal. Instance principal authentication is the recommended method for production applications running on OCI Compute because it allows the application to authenticate with OCI services without managing or embedding any credentials. The OCI Compute instance assumes a dynamic group and IAM policy that grants it permissions, and the SDK automatically handles token exchange via the instance metadata service, eliminating the need for long-lived secrets.

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API signing keys

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User password and OCID

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Resource principal