1Z0-1127-25 Practice Test 36 — 15 Questions

Question 1

A data scientist is using OCI Generative AI Service to generate product descriptions. They notice that the output often repeats phrases. Which parameter adjustment would MOST directly address this issue?

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

Accepted Answer

Increase the frequency penalty. The frequency penalty directly reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. In OCI Generative AI Service, this parameter subtracts a fixed value from the log-probability of each token each time it is generated, making repeated tokens less likely to be chosen again. This is the most direct mechanism to address repetitive output.

Answer

Increase the temperature

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Increase the max tokens

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Decrease the top-p value

Question 2

A data scientist is fine-tuning a Cohere model on OCI Generative AI service for a custom classification task. They have a dataset of 1000 labeled examples. What is the minimum recommended dataset size for fine-tuning?

Accepted Answer

1000. Cohere models on OCI Generative AI require a minimum of 1000 labeled examples for fine-tuning to ensure sufficient signal for learning task-specific patterns without overfitting. This threshold is documented in OCI's fine-tuning requirements and applies to custom classification tasks.

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500

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5000

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100

Question 3

A prompt library manager wants to implement version control for prompt templates used across multiple applications. Which THREE practices should they adopt?

Accepted Answer

Use semantic versioning (e.g., v1.2.3) for prompt templates. Storing templates in a version control system, using semantic versioning, and maintaining a changelog are standard practices for prompt version management. Automated testing is good but not version control per se.

Answer

Automatically test prompts on a fixed set of inputs after each change

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Store prompts only in the application's database without history

Question 4

A company is using OCI Generative AI Agents to build a customer support assistant. They have uploaded product manuals to OCI Object Storage. Which two components are required to create the agent? (Select TWO)

Accepted Answer

Create a Generative AI Agent using the knowledge base. A Generative AI Agent requires a knowledge base to provide domain-specific context for answering queries. The knowledge base is the source of truth that the agent uses to ground its responses, and it must be explicitly created and associated with the agent. Without a knowledge base, the agent would lack the product manual data needed for customer support.

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Create an endpoint via InferenceClient

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Upload data directly to the agent (not via knowledge base)

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Provision a Dedicated AI Cluster

Question 5

A company needs to ensure that only authorized users can invoke an endpoint for a generative AI model. Which OCI feature should be used to control access?

Accepted Answer

OCI Identity and Access Management (IAM) policies. OCI Identity and Access Management (IAM) policies are the correct choice because they define who (users, groups, or service principals) can invoke which OCI resources, including generative AI model endpoints. IAM policies use resource-type and verb-based statements (e.g., 'allow group A to manage ai-service-family in compartment X') to enforce authorization at the API level, ensuring only authorized principals can call the model's inference endpoint.

Answer

Network security groups (NSGs)

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VCN flow logs

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OCI Web Application Firewall (WAF)

Question 6

Which TWO factors most significantly influence the computational cost of fine-tuning a large language model?

Accepted Answer

Number of model parameters. The number of model parameters directly determines the size of the weight matrices that must be updated during backpropagation. Fine-tuning requires storing gradients and optimizer states for each parameter, so the memory and compute scale linearly with parameter count. This is the primary driver of FLOPs (floating-point operations) per training step.

Answer

Batch size

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Quantization bits

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Dataset size

Question 7

A startup is building a customer support chatbot using RAG with OCI Generative AI. They have a large corpus of FAQ documents stored as PDFs in OCI Object Storage. The developer uses OCI Language to embed the text and stores vectors in OCI OpenSearch. During testing, the chatbot often fails to answer questions because relevant FAQ entries are not retrieved. The team suspects the chunking size is too large, causing loss of specific details. After reducing chunk size, retrieval improves slightly but still misses many answers. What should the team do NEXT?

Accepted Answer

Use a sliding window chunking strategy with overlap. A sliding window chunking strategy with overlap ensures that context is preserved across chunk boundaries, preventing the loss of specific details that can occur when a relevant sentence or phrase is split between two chunks. This directly addresses the symptom where reducing chunk size alone still misses answers, as overlapping chunks increase the likelihood that the exact text needed for retrieval appears in at least one chunk.

Answer

Increase the number of retrieved chunks (k)

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Switch to a different embedding model

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Manually rephrase the queries

Question 8

A data scientist receives an error when calling the embed_text API: "InvalidRequest: input too long". What is the most likely cause and solution?

Accepted Answer

The input text exceeds the maximum token limit for the model; truncate the input.. The error 'InvalidRequest: input too long' indicates that the input text exceeds the maximum token limit for the embedding model. OCI Generative AI embedding models, like all transformer-based models, have a fixed context window (e.g., 512 or 1024 tokens). The solution is to truncate the input to fit within that limit, as the API will reject overly long inputs.

Answer

The model specified is not supported for embeddings; use a different model.

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The API request rate exceeds the tenancy limit; reduce the request rate.

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The API key is invalid or expired; regenerate the key.

Question 9

A team uses OCI Generative AI’s fine-tuning capability to adapt a base model. After fine-tuning, they evaluate the model but see degraded performance on certain edge cases. What is the most likely cause?

Accepted Answer

Overfitting on the training data. Fine-tuning adapts a base model to a specific dataset, but if the training data is too narrow or the model is trained for too many epochs, it can memorize the training examples rather than learning generalizable patterns. This overfitting causes the model to perform well on training-like inputs but poorly on edge cases that deviate from the training distribution. In OCI Generative AI, overfitting is a common pitfall when fine-tuning hyperparameters like the number of epochs or learning rate are not properly validated.

Answer

Validation data leakage

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Learning rate too high

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Insufficient training epochs

Question 10

A prompt engineer is designing a system that generates SQL queries from natural language. The model sometimes produces unsafe queries (e.g., DROP TABLE). Which constraint in the system prompt would BEST mitigate this risk?

Accepted Answer

Include a constraint: 'You may only generate SELECT statements. Do not generate DDL or DML statements like DROP, DELETE, INSERT, or UPDATE.'. It explicitly prohibits the model from generating DDL (Data Definition Language) and DML (Data Manipulation Language) statements, directly addressing the risk of unsafe queries like DROP TABLE. By constraining the output to only SELECT statements, the prompt enforces a strict policy that prevents the model from producing destructive or modifying SQL commands, which is the most effective mitigation among the options.

Answer

Use a few-shot prompt with only safe SELECT examples

Answer

Use role prompting: 'You are an expert SQL developer'

Answer

Set frequency penalty to 1.0 to avoid repetitive unsafe patterns

Question 11

A company is using OCI GenAI with a Dedicated AI Cluster to serve a large language model for real-time chat applications. They notice high inference latency (average 2 seconds per response) and want to reduce it to under 500 milliseconds without significantly degrading the quality of responses. The cluster is configured with NVIDIA A100 GPUs. The model is the base Cohere Command model (52B parameters). They have explored increasing batch size, but that increases latency for interactive use cases. Which action should they take?

Accepted Answer

Deploy the model with inference optimization frameworks like vLLM, TensorRT, or ONNX Runtime.. Inference optimization frameworks like vLLM, TensorRT, and ONNX Runtime are specifically designed to reduce latency for large language models on NVIDIA A100 GPUs. These frameworks use techniques such as PagedAttention (vLLM), kernel fusion, and graph optimization to significantly lower per-request latency without degrading output quality, making them ideal for real-time chat applications where sub-500ms responses are required.

Answer

Increase batch size to process multiple queries at once.

Answer

Swap the model to a smaller variant, such as Cohere Command Light (6B).

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Enable model quantization (e.g., int8) to reduce memory and computation.

Question 12

An engineer sets beam search width to 1 during inference on OCI Generative AI. What is the most likely effect on output?

Accepted Answer

Faster inference. Beam search width of 1 is equivalent to greedy decoding, where only the single most probable token is selected at each step. This eliminates the need to maintain and compare multiple candidate sequences, significantly reducing computational overhead and memory access, which directly speeds up inference.

Answer

More memory usage

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More diverse outputs

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Better quality

Question 13

A startup wants to quickly prototype a chatbot using OCI Generative AI service. They have no prior experience with OCI. They want to test different models and parameters without writing any code and within a few minutes. They also want to save prompts and compare results. Which approach should they use?

Accepted Answer

Use the OCI Generative AI Playground.. The OCI Generative AI Playground is a no-code, web-based interface that allows users to quickly test different models and parameters, save prompts, and compare results without writing any code or having prior OCI experience. It directly meets the startup's requirement for rapid prototyping within minutes.

Answer

Create a dedicated AI cluster and use the OCI SDK.

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Use OCI Data Science Notebooks with the GenAI SDK.

Answer

Use OCI Functions to invoke the GenAI API.

Question 14

An architect is designing a multi-tenant application using OCI Generative AI. Each tenant has custom instructions and data. To minimize cost while maintaining isolation, which deployment approach is recommended?

Accepted Answer

Shared base model with per-tenant system prompts and retrieval.. It leverages a shared base model with per-tenant system prompts and retrieval-augmented generation (RAG) to isolate custom instructions and data without the cost of dedicated endpoints. This approach minimizes compute overhead by reusing a single model instance while maintaining logical isolation through prompt engineering and vector-based retrieval, aligning with OCI's pay-as-you-go pricing model.

Answer

Dedicated fine-tuned endpoint per tenant.

Answer

On-premises deployment of open-source models.

Answer

Single large fine-tuned model with conditional logic.

Question 15

A data scientist is fine-tuning a model on OCI Generative AI to generate code comments. They use a dataset of 10,000 examples. After fine-tuning, the model generates comments that are too similar to the training data and lack generalization. What is the most likely cause?

Accepted Answer

Too many training epochs.. When a fine-tuned model generates outputs that are too similar to the training data and lack generalization, it is a classic sign of overfitting. Overfitting occurs when the model is trained for too many epochs, causing it to memorize the training examples rather than learning the underlying patterns. In OCI Generative AI, the fine-tuning process adjusts model weights iteratively, and excessive epochs lead to poor performance on unseen data.

Answer

Incorrect tokenizer.

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Insufficient training data.

Answer

Too high learning rate.