1Z0-1127 Timed Hard Questions — 25 Difficult Questions

Question 1

A data scientist fine-tuned a model on OCI Gen AI using a dedicated AI cluster. After deployment, the model gives inaccurate results. Which troubleshooting step should they take first?

Accepted Answer

Check the training data for bias or quality issues. Option B is correct because inaccurate results often stem from training data issues such as bias, quality, or insufficient diversity. Other options may be considered later but data quality is the primary suspect.

Answer

Switch to a different base model

Answer

Increase the cluster size

Answer

Use a serverless endpoint

Question 2

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

Accepted Answer

Number of model parameters. Model size (parameters) directly determines FLOPs and memory. Training sequence length affects memory and compute per step. Option C is wrong because batch size affects throughput but not fundamental cost per token. Option D is wrong because quantization usually reduces cost. Option E is wrong because dataset size affects total steps but per-step cost is dominated by model size and sequence length.

Answer

Batch size

Answer

Quantization bits

Answer

Dataset size

Question 3

A team is fine-tuning a large language model for a domain-specific Q&A application. After fine-tuning, they observe that the model performs well on the training distribution but struggles with out-of-distribution (OOD) questions. Which approach would best improve OOD robustness?

Accepted Answer

Include a diverse set of examples from related domains in the fine-tuning dataset.. Option C is correct because incorporating diverse data during fine-tuning helps the model generalize to OOD inputs. Option A is wrong because increasing learning rate may cause catastrophic forgetting. Option B is wrong because reducing model size reduces capacity. Option D is wrong because early stopping on training loss may not help OOD.

Answer

Use early stopping based on training loss to avoid overfitting.

Answer

Reduce the model size to prevent overfitting to the training data.

Answer

Increase the learning rate during fine-tuning to adapt faster to new patterns.

Question 4

Refer to the exhibit. A user runs the command shown and receives the error: 'ServiceError: NotAuthorizedOrNotFound'. What is the MOST likely cause?

Accepted Answer

The user does not have the 'inspect' permission on the model. The error 'NotAuthorizedOrNotFound' typically indicates either the model ID does not exist or the user lacks permission to view it. Option D is correct because the error message is generic to avoid information leakage. Option A would give a different error (e.g., invalid model ID), but the generic error suggests authorization or existence issues.

Answer

The CLI is not configured with OCI credentials

Answer

The model ID is incorrectly formatted

Answer

The model is in a different region than iad

Question 5

You are a cloud architect at a healthcare company that uses OCI Generative AI Service to analyze patient records and generate clinical summaries. The service is deployed in the Frankfurt region with a dedicated AI cluster. Recently, the compliance team flagged that some generated summaries contain hallucinated diagnoses not present in the source records. They demand immediate mitigation. The current setup uses the default model (cohere.command-r-08-2024) with temperature=0.7, top_p=0.9, and max_tokens=2048. The application sends the entire patient record as a single prompt. You have access to OCI Logging, monitoring metrics (latency, request count, token count, safety filter rejections), and the AI service's model fine-tuning capability. You must reduce hallucinations while minimizing latency increase. What is the most effective course of action?

Accepted Answer

Reduce temperature to 0.2, top_p to 0.5, and fine-tune the model on a curated dataset of 5,000 clinical summaries with a learning rate of 0.00005 and batch size of 8.. Option D is correct because reducing temperature and top_p makes the model more deterministic, reducing randomness and thus hallucinations. Fine-tuning on curated clinical data with a lower learning rate and smaller batch size aligns the model to the domain without excessive training. Option A might reduce hallucinations but increases latency and token cost. Option B only adds a safety filter, which does not address factual accuracy. Option C may change style but not reduce hallucinations.

Answer

Switch to cohere.command-light model for faster inference and add a post-processing step using a BERT-based NER model to validate entities.

Answer

Increase max_tokens to 4096 and use chunked processing with overlapping context windows to provide more context.

Answer

Enable the safety filter with strict content moderation and set up OCI Logging to audit all generations.

Question 6

A company is deploying a RAG pipeline using OCI Data Science and OCI Generative AI. The pipeline uses a Cohere command model for generation and a Cohere embed model for retrieval. The team notices that the model occasionally produces hallucinated answers that are not supported by the retrieved context. Which strategy is MOST effective at reducing hallucinations?

Accepted Answer

Implement a faithfulness verification step that re-ranks retrieved passages based on alignment with the generated answer.. Option D is correct because incorporating a faithfulness check that re-ranks retrieval results can directly filter out unsupported claims. Option A is wrong because increasing temperature may increase randomness and hallucinations. Option B is wrong because more retrieved chunks can introduce conflicting information. Option C is wrong because a larger model does not guarantee faithfulness and increases cost.

Answer

Increase the temperature parameter of the generation model.

Answer

Increase the number of retrieved chunks (k) to provide more context.

Answer

Use a larger generative model with more parameters.

Question 7

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.. Option D is correct because 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 8

You are deploying a generative AI solution on OCI for a healthcare client that requires strict data residency (data must remain in the EU) and low-latency inference. The solution uses a fine-tuned LLM model (7B parameters) stored in Object Storage in the Frankfurt region. You have set up an OCI Data Science model deployment endpoint with GPU shape VM.GPU.A10.1, using a single replica. During load testing with 50 concurrent users, you observe high latency (average 8 seconds per request) and occasional 504 gateway timeouts. The model deployment logs show no errors, and the model loads successfully. You have confirmed that the Object Storage bucket is in the same region and that the network latency between the client and the endpoint is minimal (under 5 ms). Which action should you take to reduce latency and eliminate timeouts?

Accepted Answer

Increase the number of replicas to 3 and enable autoscaling based on CPU utilization.. Option C is correct because the high latency and 504 timeouts with 50 concurrent users indicate that a single GPU replica is overwhelmed by the request queue. Increasing replicas to 3 distributes the load across multiple endpoints, while enabling autoscaling based on CPU utilization ensures dynamic scaling to handle traffic spikes. This directly reduces per-request latency and eliminates timeouts without violating data residency requirements.

Answer

Increase the model deployment endpoint timeout setting from 60 seconds to 300 seconds in the OCI console.

Answer

Upgrade the model deployment shape to VM.GPU.A100.4 and keep a single replica.

Answer

Move the model deployment to the US East (Ashburn) region to leverage lower-cost GPU capacity and reduce latency.

Question 9

A company has multiple teams sharing an OCI Generative AI Dedicated AI Cluster. They need to ensure that each team can only access their own fine-tuned models and cannot see or invoke models from other teams. What is the best approach?

Accepted Answer

Use OCI compartments and IAM policies with resource-level permissions for models. OCI compartments and IAM policies with resource-level permissions allow you to grant granular access to specific models within a Dedicated AI Cluster. By placing each team's fine-tuned models in separate compartments and writing policies that restrict access to those compartments, you ensure teams can only see and invoke their own models. This approach leverages OCI's native identity and access management without requiring separate clusters or network-level isolation.

Answer

Train separate models for each team

Answer

Encrypt model artifacts with different keys for each team

Answer

Use network security lists to isolate traffic

Question 10

A company is designing a generative AI solution on OCI that must comply with data privacy regulations. Which three best practices should they follow? (Choose three.)

Accepted Answer

Enable audit logging for all inference requests. Option A is correct because enabling audit logging for all inference requests is a fundamental data privacy best practice. It provides an immutable record of who accessed the generative AI service, what data was sent, and when, which is essential for compliance audits and detecting unauthorized access. OCI Audit service captures these events automatically when configured, ensuring traceability without storing the actual inference payloads.

Answer

Allocate a dedicated compartment for generative AI resources to apply specific IAM policies

Answer

Store all inference inputs and outputs in a public bucket for transparency

Question 11

Refer to the exhibit. The dashboard shows latency grouped by modelId, but some points are missing for certain modelIds. Which of the following is the most likely reason?

Accepted Answer

The modelIds with missing data may have been deleted or are inactive. Option C is correct because in OCI's Generative AI service, model deployments are associated with specific modelIds. If a modelId is deleted or its deployment is deactivated, the corresponding telemetry data (e.g., latency metrics) will no longer be reported, causing gaps in the dashboard. The dashboard aggregates metrics only for active modelIds, so missing points indicate that those modelIds are no longer in service.

Answer

The metric name is misspelled

Answer

The aggregation interval is too short

Answer

The compartmentId is incorrect

Question 12

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. Option B is correct because 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 13

A company wants to deploy a custom generative AI model for generating synthetic data for training other models. The model requires approximately 20GB of memory and must be accessible via a REST API with authentication. Additionally, the team needs to monitor for data drift over time. Which combination of OCI services best meets these requirements with minimal operational overhead?

Accepted Answer

OCI Data Science Model Deployment with OCI Monitoring and OCI Logging. Option B is correct because OCI Data Science Model Deployment provides a managed environment for hosting custom generative AI models with REST API endpoints and built-in authentication via OCI IAM. It integrates natively with OCI Monitoring and OCI Logging to track data drift and operational metrics without requiring additional infrastructure setup, minimizing operational overhead.

Answer

OCI Compute with custom Docker container and Prometheus monitoring

Answer

OCI Functions with API Gateway for authentication

Answer

OCI Data Flow with OCI Data Catalog for model registry

Question 14

An OCI administrator is configuring access control for OCI Generative AI. Which three IAM components are required to allow a group of data scientists to call the GenerateText API? (Choose three.)

Accepted Answer

An IAM group for the data scientists. An IAM group is required to organize the data scientists into a logical set of principals. IAM policies are then attached to this group to grant permissions, ensuring only members of the group can call the GenerateText API. Without a group, you cannot apply a policy to a collection of users.

Answer

A local peering gateway

Answer

A dynamic group

Question 15

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 16

During fine-tuning of a large language model on OCI, you notice that the model's performance on the validation set is not improving after several epochs, but the training loss continues to decrease. What is the most likely cause?

Accepted Answer

The model is overfitting to the training data.. When training loss decreases but validation performance stagnates or worsens, the model is overfitting to the training data. It memorizes the training examples but fails to generalize. A high learning rate might cause divergence, not this pattern. Too small training data can contribute to overfitting but is not the direct symptom. An unrepresentative validation set could cause mismatch, but the described pattern is classic overfitting.

Answer

The learning rate is too high.

Answer

The validation set is not representative.

Answer

The training data is too small.

Question 17

During inference with OCI Generative AI, you notice that the model is generating repetitive phrases. Which combination of parameters can help reduce repetition?

Accepted Answer

Top_p = 0.9, frequency_penalty = 0.5. Option B is correct because a high Top_p value (0.9) allows the model to consider a diverse set of tokens, reducing the chance of getting stuck in repetitive loops, while a positive frequency_penalty (0.5) actively penalizes tokens that have already been generated, discouraging the model from repeating the same phrases. Together, these parameters balance creativity and repetition suppression.

Answer

Top_p = 0.1, frequency_penalty = 0.5

Answer

Top_p = 0.9, frequency_penalty = 0.0

Answer

Top_p = 1.0, frequency_penalty = 0.0

Question 18

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 19

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.. A shared base model with per-tenant customization via system prompts and retrieval (RAG) is cost-effective and provides isolation through prompt engineering and data segregation. Dedicated fine-tuned endpoints are expensive, a single large model with conditional logic risks prompt injection, and on-premises deployment may not be feasible or scalable.

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 20

A development team wants to generate code snippets from natural language. Which model strategy should they adopt?

Accepted Answer

Use a code-specific model like Code Llama.. Code Llama is a specialized variant of Llama 2 that has been fine-tuned on code datasets, enabling it to generate syntactically and semantically correct code from natural language prompts. This makes it the optimal choice for code generation tasks, as general-purpose LLMs lack the targeted training on code structures and programming languages.

Answer

Use a general-purpose LLM like Llama 2.

Answer

Use a multimodal model.

Answer

Use an embedding model for text.