1Z0-1127-25 Practice Test 4 — 25 Questions

Question 1

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 2

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.

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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 3

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

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Perplexity

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BERTScore

Question 4

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

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Use a larger model to improve accuracy

Answer

Increase the number of replicas in the deployment

Question 5

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

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Stop the dedicated AI cluster when not in use

Answer

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

Question 6

Refer to the exhibit. Users in the group cannot create a new custom model deployment on a Dedicated AI Cluster. What is the most likely missing permission?

Accepted Answer

Use of virtual-network-family for cluster networking. Creating a custom model deployment on a Dedicated AI Cluster requires the user to have the 'Use of virtual-network-family for cluster networking' permission. This permission allows the user to specify and manage the virtual network (VCN) and subnet that the cluster uses for networking. Without it, the deployment fails because the cluster cannot be attached to the required network resources.

Answer

Manage ai-document-understanding

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Manage ai-agents

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Manage instance-configurations

Question 7

Which TWO factors are most important when deciding between on-demand and dedicated AI clusters for OCI GenAI?

Accepted Answer

Number of concurrent requests. The number of concurrent requests (D) is critical because dedicated AI clusters provide guaranteed throughput and predictable performance for high-volume workloads, while on-demand clusters may throttle or queue requests under heavy load. Latency requirements (E) are equally important because dedicated clusters offer consistent low-latency inference by avoiding resource contention, whereas on-demand clusters can introduce variable latency due to shared infrastructure. Together, these factors directly determine whether a workload needs the isolation and guaranteed resources of a dedicated cluster or can tolerate the elasticity and potential variability of on-demand provisioning.

Answer

Fine-tuning capability

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Model size

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Data residency

Question 8

A data scientist deployed a fine-tuned Llama 2 7B model on OCI Model Deployment with a single VM.GPU.A10.1 shape. Users report average latency of 3 seconds per request, which is too high for the intended real-time application. The model is used for short text generation (max 128 tokens). The data scientist wants to reduce per-request latency without significant accuracy loss. Which action would be most effective?

Accepted Answer

Apply 4-bit quantization using AWQ. 4-bit quantization using AWQ reduces the model's memory footprint and computational requirements by compressing weights to 4-bit integers, which directly decreases inference latency on the VM.GPU.A10.1 shape. This technique preserves most of the model's accuracy while enabling faster token generation, making it the most effective single action for reducing per-request latency in a real-time short text generation scenario.

Answer

Increase the number of workers per replica

Answer

Increase the max_tokens parameter for the model

Answer

Enable response streaming for the model endpoint

Question 9

A company is deploying a generative AI model on OCI for an internal application that must comply with strict security policies. The model will be accessed by a limited group of users. Which three actions should the administrator take to ensure security? (Choose three.)

Accepted Answer

Deploy the model in a private VCN subnet. Deploying the model in a private VCN subnet ensures that the model endpoint is not exposed to the internet, which is a fundamental security requirement for compliance with strict security policies. By placing the model in a private subnet, all traffic must traverse through a bastion host, VPN, or FastConnect, providing network isolation and reducing the attack surface. This aligns with OCI's shared responsibility model where the customer controls network security.

Answer

Expose the model endpoint to the internet for ease of access

Answer

Disable audit logging to minimize storage costs

Question 10

A machine learning engineer evaluates OCI Generative AI for a real-time content generation application. They need to meet a SLAs of 99.9% availability. Which deployment architecture satisfies the requirement with the lowest cost?

Accepted Answer

Two dedicated AI clusters in different availability domains.. Deploying two dedicated AI clusters in different availability domains within a single region provides high availability (HA) to meet the 99.9% SLA while minimizing cost. OCI's dedicated AI clusters are regional resources, and placing replicas across availability domains protects against domain-level failures without the cross-region data transfer and egress costs incurred by multi-region deployments.

Answer

Two dedicated AI clusters in different regions.

Answer

Two dedicated AI clusters in the same availability domain.

Answer

Single dedicated AI cluster with a single replica.

Question 11

Which TWO techniques can help reduce bias in LLM outputs?

Accepted Answer

Using diverse training data. Using diverse training data helps the model learn from a wide range of perspectives, reducing the risk of over-representing any single group or viewpoint. This directly mitigates bias by ensuring the training distribution is more representative of the real world, rather than skewed toward a dominant demographic or cultural norm.

Answer

Setting temperature to 0

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Using only English data

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Increasing model size

Question 12

An organization wants to use OCI Generative AI to build a summarization tool but must ensure that all inference requests are logged for audit purposes. Which approach should they take?

Accepted Answer

Enable OCI Logging on the generative AI endpoint. OCI Logging can be enabled directly on the Generative AI endpoint to capture all inference requests and responses as logs, which can then be used for audit purposes. This is the native, recommended approach for logging API calls without introducing additional infrastructure or complexity.

Answer

Implement a custom proxy with logging

Answer

Enable OCI Audit service

Answer

Use OCI Vault to store logs

Question 13

An OCI AI Language text classification request returns the output shown. Which conclusion is most accurate?

Accepted Answer

The text is classified as Positive with high confidence.. The output shows a sentiment label of 'Positive' with a confidence score of 0.98, indicating the model is highly confident in its classification. Option B correctly identifies this as a positive sentiment with high confidence, which is the most accurate conclusion based on the provided data.

Answer

The model is uncertain about the sentiment.

Answer

The API endpoint is misconfigured.

Answer

The --endpoint parameter is optional.

Question 14

A financial institution uses an LLM for generating investment advice. They are concerned about hallucinations. Which method is most effective?

Accepted Answer

Use RAG with a verified corpus of regulations and reports.. Retrieval-Augmented Generation (RAG) grounds the LLM's output in a verified, external knowledge base (e.g., regulations and reports). By retrieving relevant documents at inference time, RAG reduces the model's reliance on its parametric memory, directly mitigating hallucinations in high-stakes domains like financial advice.

Answer

Fine-tune on general financial data.

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Increase the temperature to get more creative responses.

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Use a larger model to improve accuracy.

Question 15

An LLM application generates product descriptions. The output is sometimes repetitive (e.g., 'innovative' appears multiple times). Which parameter adjustment is MOST likely to reduce this repetition without harming creativity?

Accepted Answer

Set frequency penalty to a positive value, e.g., 0.3. The frequency penalty reduces the likelihood of repeating tokens that have already appeared. A moderate penalty (e.g., 0.3) discourages repetition while allowing creative word choices. Temperature and top-k affect randomness, not repetition.

Answer

Increase temperature from 0.7 to 1.0

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Decrease max tokens from 200 to 100

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Increase top-k from 40 to 80

Question 16

A developer wants to generate text using the OCI Generative AI service via the API. Which endpoint should they use to send a text generation request?

Accepted Answer

/v1/completions. The OCI Generative AI service uses the /v1/completions endpoint for text generation requests, as documented in the OCI Generative AI API reference. This endpoint accepts a prompt and generates a continuation of the text, making it the appropriate choice for general text generation tasks.

Answer

/v1/chat/completions

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/v1/embeddings

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/v1/models

Question 17

A user repeatedly gets the same phrase output by the model. Which parameter adjustment is MOST likely to reduce such repetitive patterns?

Accepted Answer

Increase frequency penalty. Frequency penalty reduces the likelihood of repeating tokens that have already appeared, directly combating repetition.

Answer

Decrease max tokens

Answer

Increase temperature

Answer

Increase top-p

Question 18

What is the primary purpose of an embedding model in a RAG pipeline?

Accepted Answer

To convert text into numerical vectors.. The primary purpose of an embedding model in a RAG pipeline is to convert text into numerical vectors (embeddings) that capture semantic meaning. These vectors enable the retrieval component to efficiently find relevant documents by measuring similarity (e.g., cosine similarity) between the query and stored document embeddings. Without this conversion, the system cannot perform semantic search over unstructured text.

Answer

To generate human-like responses.

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To rank search results.

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To summarize long documents.

Question 19

A data scientist observes that their fine-tuned LLM performs well on training data but generates repetitive and dull responses in production. What is the most likely cause and best solution?

Accepted Answer

The temperature is set too low; increase temperature during inference. The model's repetitive and dull responses indicate that the temperature parameter is too low, causing the model to always select the most probable tokens, leading to deterministic and monotonous outputs. Increasing temperature during inference introduces randomness into token sampling, allowing for more diverse and creative responses. This is a common issue in production LLMs where low temperature settings optimized for training metrics fail to produce engaging real-world outputs.

Answer

The model is overfitted; apply stronger regularization

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The training data lacks diversity; add more varied examples

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The model has too many layers; reduce model size

Question 20

Which TWO OCI Generative AI features are available in the Playground for testing models?

Accepted Answer

Adjusting parameters like temperature and max tokens. The Playground allows interactive testing with parameter adjustment and system prompts. It does not allow fine-tuning or creating dedicated clusters.

Answer

Provisioning a dedicated AI cluster

Answer

Viewing model training metrics

Answer

Submitting a fine-tuning job