1Z0-1127-25 Multiple Select — 50 Questions

Question 1

Which THREE techniques effectively reduce query latency in a RAG system?

Accepted Answer

Pre-compute embeddings for all documents. Pre-computing embeddings for all documents eliminates the need to generate embeddings at query time, which is a computationally expensive step. By storing pre-computed vector representations, the system can directly perform similarity searches against the index, significantly reducing latency.

Answer

Use a larger generation model

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Increase the number of shards

Question 2

Which three characteristics of LLMs can lead to hallucinations? (Select THREE)

Accepted Answer

Ability to generate plausible-sounding text. LLMs are trained to generate text that is statistically plausible and coherent, but they lack mechanisms to verify factual accuracy. This means they can produce sentences that sound convincing and grammatically correct while being entirely false, which is a direct cause of hallucinations.

Answer

Overconfidence in predictions

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Large vocabulary size

Question 3

Which TWO of the following are sources of training data for fine-tuning a model in OCI Generative AI?

Accepted Answer

OCI Object Storage bucket. OCI Object Storage is a supported source for training data when fine-tuning models in OCI Generative AI. The service can directly access data stored in Object Storage buckets via service-level integrations, allowing you to reference large datasets without local uploads.

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OCI File Storage

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OCI Database

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OCI Streaming

Question 4

Which three statements about transformer architecture are correct? (Choose three.)

Accepted Answer

The softmax function is used in the attention mechanism to normalize attention scores.. The softmax function is applied to the raw attention scores (the dot products between queries and keys) to convert them into a probability distribution that sums to 1. This normalization allows the model to assign a relative weight to each token in the sequence, ensuring that the weighted sum of values is stable and interpretable.

Answer

The feed-forward network applies a different set of weights for each token position.

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The encoder-decoder structure is used in GPT models.

Question 5

Which two are essential components of the Transformer architecture? (Select TWO)

Accepted Answer

Self-attention mechanism. The self-attention mechanism is essential because it allows each token in the input sequence to attend to every other token, capturing long-range dependencies without the sequential bottleneck of RNNs. This mechanism computes attention scores using queries, keys, and values, enabling parallel processing and forming the core of the Transformer's ability to model context.

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Pooling layers

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Recurrent connections

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Convolutional layers

Question 6

Which THREE techniques are commonly used to improve the quality of text generation?

Accepted Answer

Temperature scaling. Temperature scaling is correct because it controls the randomness of token probability distributions by dividing logits before softmax; lower temperatures (e.g., 0.1) make the model more deterministic, while higher temperatures (e.g., 1.5) increase diversity. This directly influences the quality of generated text by balancing coherence and creativity.

Answer

Greedy decoding

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Random sampling

Question 7

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.

Answer

Using the OCI CLI

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Using OCI SDK for Python

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Using OCI Data Flow

Question 8

Which THREE components are essential for a production-grade generative AI deployment on OCI? (Select THREE)

Accepted Answer

OCI Logging for audit. A is correct because OCI Logging provides centralized audit logging for all API calls and resource changes in the generative AI deployment. This is essential for compliance, security monitoring, and troubleshooting in a production environment, as it captures detailed logs of model invocations, data access, and configuration changes.

Answer

OCI Vault for secrets

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OCI Data Flow for data processing

Question 9

Which TWO factors most directly impact the consistency of text generated by an LLM when the same prompt is used multiple times?

Accepted Answer

Top_p. Top_p (nucleus sampling) directly impacts consistency by controlling the cumulative probability threshold for token selection. A lower Top_p (e.g., 0.1) restricts the model to only the most probable tokens, reducing randomness and making outputs more deterministic across repeated prompts. This parameter, along with Temperature, is a primary lever for managing output variability in LLMs.

Answer

Batch size

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Max_tokens

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Seed

Question 10

Which THREE are essential steps in the prompt engineering process for an LLM?

Accepted Answer

Test the prompt with a variety of input examples. Testing the prompt with a variety of input examples is essential to evaluate the LLM's generalization, robustness, and sensitivity to different phrasing or contexts. This step helps identify edge cases, biases, or inconsistencies in the model's responses before deployment.

Answer

Fine-tune the model on a domain corpus

Answer

Quantize the model to INT8

Question 11

Which THREE factors should be considered when choosing between fine-tuning a model and using a pre-trained model with prompt engineering? (Select three.)

Accepted Answer

Size of available dataset. The size of the available dataset is a critical factor: fine-tuning requires a sufficiently large, labeled dataset (typically thousands of examples) to adjust model weights effectively, while prompt engineering can work with zero or few examples. If the dataset is too small, fine-tuning risks overfitting and poor generalization, making prompt engineering the safer choice.

Answer

Required response time

Answer

Internet connectivity

Question 12

Which THREE models are available as part of the OCI Generative AI service?

Accepted Answer

Llama 3. Llama 3 is one of the open-source large language models (LLMs) available through the OCI Generative AI service, alongside Cohere models. OCI Generative AI provides managed access to Llama 3 for text generation tasks, allowing users to deploy and fine-tune it within Oracle Cloud Infrastructure.

Answer

GPT-4

Answer

Stable Diffusion

Question 13

A developer is troubleshooting an OCI Generative AI inference request that returns a 400 Bad Request error. Which three common causes could result in this error? (Choose three.)

Accepted Answer

Incorrect endpoint URL. A 400 Bad Request error indicates the server cannot process the request due to client-side issues. An incorrect endpoint URL (A) is a common cause because the request is sent to the wrong OCI Generative AI service endpoint (e.g., using a chat endpoint for a text generation model), leading to a malformed request that the server rejects. Missing required parameters (C) in the request body, such as 'compartmentId' or 'modelId', also triggers a 400 error as the API cannot validate or process the inference without them. Exceeding the model's maximum token limit (D) results in a 400 error because the input or output exceeds the model's configured token capacity, which the API validates before processing.

Answer

Invalid API key in the request header

Answer

Network connectivity issues

Question 14

An enterprise is deploying a generative AI model that must comply with data residency regulations. Which two configurations should they implement? (Select TWO.)

Accepted Answer

Set up OCI IAM policies to prevent data egress from the region for the model's resources. OCI IAM policies can explicitly deny data egress from a specific region, ensuring that the generative AI model's resources (such as training data, model artifacts, and inference endpoints) remain within the region that satisfies data residency regulations. This is achieved by writing policy statements that restrict the movement of data across regional boundaries, which is a direct control for compliance.

Answer

Enable OCI Logging for all API calls

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Use OCI Object Storage with cross-region replication for redundancy

Answer

Store encryption keys in an OCI Vault in a different region

Question 15

A DevOps engineer is setting up monitoring and logging for a generative AI inference endpoint. Which three resources should they enable? (Select THREE.)

Accepted Answer

OCI Logging for inference requests and responses. OCI Logging captures detailed logs of inference requests and responses, which is essential for auditing, debugging, and analyzing the behavior of a generative AI endpoint. This service provides a centralized repository for log data, enabling DevOps engineers to track input prompts and model outputs for compliance and troubleshooting purposes.

Answer

OCI VCN flow logs for network traffic

Answer

OCI Audit logs for all API calls

Question 16

A developer is building a LangChain RAG pipeline with OCI Generative AI. Which TWO components are needed to create embeddings from documents and store them for retrieval?

Accepted Answer

OCIGenAIEmbeddings. OCIGenAIEmbeddings converts text into embeddings, and a vector store (e.g., FAISS, Chroma, OracleVS) stores those embeddings for similarity search. Document loaders and text splitters are used before embedding but are not part of the embedding/storage step itself.

Answer

DocumentLoader

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ChatOCIGenAI

Answer

TextSplitter

Question 17

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

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An API signing key for authentication

Question 18

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 19

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

Question 20

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

Answer

Data residency