1Z0-1127-25 Practice Exam 4 — 20 Questions

Question 1

A prompt engineer is testing two versions of a prompt for a content generation task. They want to measure which version produces more factual and concise outputs. Which evaluation approach is BEST?

Accepted Answer

Run A/B tests on a diverse set of inputs and score outputs based on predefined criteria. A/B testing with clear metrics (factuality, conciseness) is the standard method for comparing prompt variants. Manual inspection on a few cases is not statistically robust; other options are not comparative.

Answer

Use only the first output from each prompt and manually compare

Answer

Ask the model to self-evaluate its outputs

Answer

Increase the temperature to see which prompt handles randomness better

Question 2

An administrator created the above IAM policies. A member of the GenerativeAIAdmins group reports they cannot invoke the model endpoint. Which permission is missing?

Accepted Answer

Permission to use or manage generative-ai-endpoint. The error occurs because the IAM policy grants permissions for 'generative-ai-model' but not for 'generative-ai-endpoint'. Invoking a model endpoint requires the 'use' or 'manage' permission on the 'generative-ai-endpoint' resource type, as the endpoint is the runtime interface that handles inference requests. Without this permission, the API call to the endpoint is denied, even if the user has access to the underlying model.

Answer

Permission to access the compartment

Answer

Permission to manage generative-ai-model

Answer

Permission to read the model's training data

Question 3

Refer to the exhibit. A developer runs the OCI CLI command and receives the output. However, the text "Hello, how are you?" is actually a mix of English and French words. Why does the model assign only 0.03 to French?

Accepted Answer

The text is overwhelmingly English, so the model assigns a low probability to French.. The model's output shows a probability distribution over languages, and the text is predominantly English with only a few French words. The model assigns a low probability (0.03) to French because the overwhelming majority of tokens are English, making the text far more likely to be classified as English. This reflects how language identification models evaluate the overall composition of the input.

Answer

The model is limited to identifying a single language per query.

Answer

The model cannot detect multiple languages in a single text.

Answer

The model's scores are normalized to sum to 1, so a high English score forces low others.

Question 4

A company is deploying a generative AI service on OCI using the OCI Data Science service with a large language model (LLM) in a VCN. The model inference endpoint must be accessible only from a private subnet within the same VCN. Which networking component should be configured to enable this?

Accepted Answer

Service Gateway. A Service Gateway enables private subnet resources to access OCI services (including the OCI Data Science model deployment endpoint) without traversing the internet. Since the inference endpoint must be accessible only from a private subnet within the same VCN, the Service Gateway provides the necessary private connectivity by routing traffic over the OCI network fabric, not through a NAT or internet gateway.

Answer

NAT Gateway

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Dynamic Routing Gateway (DRG)

Answer

Internet Gateway

Question 5

An organization is deploying a large language model on OCI using a dedicated AI cluster. They need to minimize inference latency. Which configuration step is most critical?

Accepted Answer

Configure the cluster to use high-bandwidth RDMA networking. RDMA (Remote Direct Memory Access) bypasses the CPU and kernel to transfer data directly between GPU memories, drastically reducing latency and CPU overhead. In a dedicated AI cluster on OCI, high-bandwidth RDMA networking (e.g., using RoCE v2 or InfiniBand) is the most critical step to minimize inference latency because model parallelism and tensor parallelism across nodes depend on fast, low-latency interconnects. Without RDMA, even with optimized model parallelism, the network becomes the bottleneck, increasing per-token latency.

Answer

Set up a load balancer across multiple regions

Answer

Use a single VM shape to reduce network hops

Answer

Disable model parallelism to simplify setup

Question 6

Which THREE components are required to deploy a custom generative AI model on OCI Data Science model deployment?

Accepted Answer

An inference script (e.g., score.py) to handle prediction requests. OCI Data Science model deployment requires an inference script (typically score.py) to define how the model processes incoming prediction requests. This script is the entry point that loads the model artifact and executes inference logic, making it an essential component for serving predictions.

Answer

A load balancer to distribute traffic

Answer

An API signing key for authentication

Question 7

Which THREE steps are required to deploy a custom generative AI model using OCI Data Science Model Deployment?

Accepted Answer

Create a model artifact (e.g., pickle, ONNX) with inference code. Deploying a custom generative AI model via OCI Data Science Model Deployment requires packaging the model and its inference code into a standardized artifact format (e.g., pickle, ONNX). This artifact is the core input that the deployment runtime loads to serve predictions, making it an essential step in the workflow.

Answer

Fine-tune the model using OCI Generative AI service

Answer

Register the model in OCI Generative AI service

Question 8

A data scientist is using the OCI Generative AI service to generate text completions. The API calls are returning HTTP 400 errors with the message 'Invalid model parameters'. What is the most likely cause?

Accepted Answer

One or more model parameters (e.g., temperature, top_p) are outside the accepted range. The HTTP 400 error with 'Invalid model parameters' directly indicates that one or more of the parameters sent in the API request (such as temperature, top_p, max_tokens, or stop sequences) are outside the acceptable range defined by the OCI Generative AI service. For example, temperature must be between 0 and 1, and top_p between 0 and 1, and sending a value like 2.0 for temperature would trigger this error. The other options (expired key, rate limit, incorrect endpoint) would produce different HTTP status codes or error messages.

Answer

The API key is expired

Answer

The request exceeds the rate limit

Answer

The endpoint URL is incorrect

Question 9

During multi-turn conversation with an OCI GenAI model, the model repeats user messages from earlier turns. What is the most likely cause?

Accepted Answer

Low presence penalty. A low presence penalty reduces the model's incentive to avoid repeating previously mentioned content. In multi-turn conversations, this can cause the model to echo user messages from earlier turns because the penalty is too weak to discourage repetition of tokens that have already appeared in the context window.

Answer

Low top-p

Answer

High temperature

Answer

High frequency penalty

Question 10

A company wants to build a customer support chatbot using OCI Generative AI. They have a large number of historical support tickets. Which approach is most effective for leveraging this data to improve the chatbot's responses?

Accepted Answer

Fine-tune the Cohere Command model on the historical tickets using OCI Data Science.. Fine-tuning the Cohere Command model on the historical support tickets using OCI Data Science is the most effective approach because it adapts the model's weights to the specific domain language, terminology, and resolution patterns found in the company's data. This supervised learning process creates a specialized model that can generate accurate, context-aware responses for customer support queries, unlike generic prompting methods that lack deep domain adaptation.

Answer

Use a pre-loaded prompt template from the OCI console.

Answer

Increase the temperature parameter to 1.0 to encourage diverse responses.

Answer

Use zero-shot prompting with the base model and include few-shot examples in the prompt.

Question 11

A LangChain application using ChatOCIGenAI is hitting rate limits from the OCI Generative AI service. The developer wants to implement retry logic with exponential backoff. Which approach is most appropriate in LangChain?

Accepted Answer

Set the max_retries and request_timeout parameters in ChatOCIGenAI, and use a custom callback to handle rate limit errors with exponential backoff. LangChain's built-in retry mechanism for LLMs can be configured via the request_timeout and max_retries parameters on the model wrapper. Additionally, using a custom callback or middleware can implement exponential backoff. The other options either do not address rate limits or are not native to LangChain.

Answer

Reduce the number of concurrent requests by setting a semaphore in the application code

Answer

Use a middleware in the FastAPI application to queue requests and retry with exponential backoff

Answer

Wrap the ChatOCIGenAI instance with a RetryFromLLM callback that implements exponential backoff

Question 12

A retail company uses OCI Generative AI to generate product descriptions. They observe the model occasionally produces biased content. Which technique should be applied to reduce bias in model outputs?

Accepted Answer

Apply prompt engineering with explicit instructions to avoid bias.. Prompt engineering allows you to explicitly instruct the model to avoid biased content, such as by including directives like 'Ensure the description is neutral and unbiased' in the system or user prompt. This technique directly influences the model's output generation without altering its underlying parameters, making it a targeted and effective approach for reducing bias in OCI Generative AI models.

Answer

Increase the max_tokens parameter.

Answer

Reduce the model's inference temperature to 0.

Answer

Use a different random seed for each request.

Question 13

A data scientist wants to deploy a fine-tuned LLM on OCI for inference with low latency. Which OCI service should they use?

Accepted Answer

OCI Generative AI Service (Dedicated AI Cluster). B is correct because OCI Generative AI Service with a Dedicated AI Cluster provides a managed, high-throughput, low-latency inference endpoint for fine-tuned LLMs. It leverages GPU-accelerated infrastructure and optimized serving stacks (e.g., vLLM, TensorRT-LLM) to minimize response times, making it ideal for production inference workloads.

Answer

OCI Data Science Notebook Session

Answer

OCI Data Flow

Answer

OCI Functions

Question 14

Which component of the Transformer architecture allows the model to weigh the importance of different tokens in the input sequence when generating each output token?

Accepted Answer

Self-attention mechanism. The self-attention mechanism computes attention scores between all pairs of tokens, enabling the model to dynamically focus on relevant parts of the input. Positional encoding adds order information, multi-head attention runs multiple attention heads in parallel, and the feed-forward network processes each position independently.

Answer

Feed-forward neural network

Answer

Multi-head attention

Answer

Positional encoding

Question 15

A company is deploying a fine-tuned Cohere model on OCI Generative AI service for real-time inference. They need to ensure low latency even during demand spikes. Which configuration should they prioritize?

Accepted Answer

Use a dedicated AI cluster for the endpoint.. A dedicated AI cluster provides isolated compute resources (GPUs) that are not shared with other tenants or workloads, ensuring consistent low latency even under demand spikes. This is critical for real-time inference because shared endpoints can experience resource contention and throttling during high traffic, while a dedicated cluster guarantees predictable performance.

Answer

Enable model caching on the endpoint.

Answer

Use streaming responses.

Answer

Increase the max tokens parameter.

Question 16

An engineer configured the above index mapping for vector search. When performing a k-NN search, the results are unexpected. What is the most likely issue?

Accepted Answer

The space type at the index level and mapping level are mismatched.. OpenSearch requires the space type to be consistently defined at both the index-level settings (method.parameters.space_type) and the field-level mapping (space_type). A mismatch between these two causes the k-NN search to behave unexpectedly, as the engine uses the index-level setting for distance computation while the mapping-level setting may be used for validation or other purposes.

Answer

The space type 'cosinesimil' is not supported; it should be 'cosine'.

Answer

The dimension 768 does not match the embedding model's output dimension.

Answer

The mapping uses 'knn_vector' type with 'faiss' engine, which is incompatible.

Question 17

A data scientist is deploying a custom generative AI model using OCI Data Science. After deploying the model to an endpoint, they notice that inference requests are failing with a timeout error when the payload size exceeds 1 MB. What is the most likely cause and solution?

Accepted Answer

The model deployment has a default payload size limit of ~1 MB; increase the payload limit in the deployment configuration.. OCI Data Science model deployments have a default payload size limit of approximately 1 MB. When inference requests exceed this limit, the load balancer or gateway times out the request. The solution is to increase the payload limit in the deployment configuration, which can be adjusted via the OCI console or API by modifying the `maximumRequestPayloadSize` setting.

Answer

The load balancer is misconfigured; reconfigure the load balancer timeout settings.

Answer

The model server lacks sufficient memory; scale out to more instances.

Answer

The model is not optimized for large payloads; use AutoML to optimize the model.

Question 18

Which of the following metrics is most suitable for evaluating a translation model's output against multiple reference translations?

Accepted Answer

BLEU. BLEU (Bilingual Evaluation Understudy) is the most suitable metric for evaluating a translation model's output against multiple reference translations because it measures n-gram precision between the candidate translation and one or more reference translations. It directly quantifies how many words and phrases in the candidate match those in the references, making it the standard metric for machine translation tasks.

Answer

ROUGE

Answer

Perplexity

Answer

BERTScore

Question 19

A company notices that some inference requests to their deployed model on OCI Generative AI take longer than acceptable. They want to reduce per-request latency. What should they do?

Accepted Answer

Reduce the maximum number of tokens generated. Reducing the maximum number of tokens generated directly decreases the amount of computation required per inference request because the model stops generating output earlier. Since latency is proportional to the number of output tokens produced, this is the most effective single change to reduce per-request response time in OCI Generative AI deployments.

Answer

Enable request batching

Answer

Use a larger model to improve accuracy

Answer

Increase the number of replicas in the deployment

Question 20

Which TWO actions are recommended best practices for managing costs when using OCI Generative AI dedicated AI clusters?

Accepted Answer

Use preemptible instances for non-critical inference workloads. Preemptible instances in OCI are significantly cheaper than standard instances and are ideal for non-critical inference workloads that can tolerate interruptions. This aligns with cost optimization best practices by allowing you to use spare compute capacity at a reduced rate for tasks that do not require continuous availability.

Answer

Provision a fixed number of nodes to handle peak load

Answer

Stop the dedicated AI cluster when not in use

Answer

Use pay-as-you-go billing instead of preemptible instances