Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 practice test

A developer wants to deploy a RAG application using OCI Generative AI for both embedding and text generation while minimizing costs. Which strategy is most effective?

Trap 1: Use a larger generation model

Larger generation models increase cost per generation.

Trap 2: Reduce chunk size to decrease embedding calls

Smaller chunks may increase the number of chunks and thus embedding calls.

Trap 3: Use a larger embedding model for better accuracy

Larger models cost more per API call.

A
Use a larger generation model
Why wrong: Larger generation models increase cost per generation.
B
Cache frequent queries and their embeddings
Caching reduces redundant embedding API calls, lowering costs.
C
Reduce chunk size to decrease embedding calls
Why wrong: Smaller chunks may increase the number of chunks and thus embedding calls.
D
Use a larger embedding model for better accuracy
Why wrong: Larger models cost more per API call.

Question 2hardmultiple choice

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?

Trap 1: Switch to a different base model

Base model may not be the root cause if fine-tuning data is flawed.

Trap 2: Increase the cluster size

Cluster size affects performance, not accuracy.

Trap 3: Use a serverless endpoint

Endpoint type does not fix accuracy issues.

A
Switch to a different base model
Why wrong: Base model may not be the root cause if fine-tuning data is flawed.
B
Increase the cluster size
Why wrong: Cluster size affects performance, not accuracy.
C
Use a serverless endpoint
Why wrong: Endpoint type does not fix accuracy issues.
D
Check the training data for bias or quality issues
Training data quality directly impacts model accuracy.

Question 3mediummultiple choice

Users report that inference requests to the OCI Generative AI service are taking longer than expected. The application uses the on-demand endpoint. What is the most likely cause of the increased latency?

Trap 1: The inference model is not fine-tuned for the use case.

Fine-tuning affects accuracy, not latency. The issue is performance, not model suitability.

Trap 2: The selected model is too large for the use case.

Model size affects inference speed, but the on-demand endpoint automatically scales; the more common cause is shared resource contention.

Trap 3: The API request timeout is set too low.

Timeout settings affect client-side waits, not server-side inference latency.

A
The inference model is not fine-tuned for the use case.
Why wrong: Fine-tuning affects accuracy, not latency. The issue is performance, not model suitability.
B
The on-demand endpoint experiences shared resource contention.
On-demand endpoints are multi-tenant; high concurrent usage can cause latency spikes.
C
The selected model is too large for the use case.
Why wrong: Model size affects inference speed, but the on-demand endpoint automatically scales; the more common cause is shared resource contention.
D
The API request timeout is set too low.
Why wrong: Timeout settings affect client-side waits, not server-side inference latency.

Question 4mediummultiple choice

Refer to the exhibit. A developer runs the command and receives the error. What is the issue?

Network Topology

Trap 1: The message is too short.

Message length is not the issue.

Trap 2: The chat-id is invalid.

The error does not mention chat-id.

Trap 3: The endpoint is incorrect.

The endpoint is valid; the error is about max-tokens.

A
The max-tokens value exceeds the allowed range.
The error explicitly states the valid range.
B
The message is too short.
Why wrong: Message length is not the issue.
C
The chat-id is invalid.
Why wrong: The error does not mention chat-id.
D
The endpoint is incorrect.
Why wrong: The endpoint is valid; the error is about max-tokens.

Question 5easymultiple choice

A developer wants to integrate OCI GenAI into a Java application. Which SDK should they use?

Trap 1: OCI JavaScript SDK.

JavaScript SDK is for Node.js or browser applications.

Trap 2: OCI Python SDK.

Python SDK is for Python applications, not Java.

Trap 3: OCI CLI.

CLI is a command-line tool, not an SDK for integration.

A
OCI JavaScript SDK.
Why wrong: JavaScript SDK is for Node.js or browser applications.
B
OCI Python SDK.
Why wrong: Python SDK is for Python applications, not Java.
C
OCI Java SDK.
The Java SDK is designed for Java applications.
D
OCI CLI.
Why wrong: CLI is a command-line tool, not an SDK for integration.

Question 6hardmulti select

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

Trap 1: Batch size

Batch size affects memory but not per-token compute cost.

Trap 2: Quantization bits

Quantization reduces cost, not increases.

Trap 3: Dataset size

Dataset size affects total training time but not per-step cost.

A
Batch size
Why wrong: Batch size affects memory but not per-token compute cost.
B
Number of model parameters
More parameters increase compute and memory requirements.
C
Maximum sequence length
Longer sequences increase attention computation and memory usage.
D
Quantization bits
Why wrong: Quantization reduces cost, not increases.
E
Dataset size
Why wrong: Dataset size affects total training time but not per-step cost.