Troubleshooting practice questions

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 2hardmulti select

A troubleshooting scenario: A RAG system returns no results for certain queries. The index exists and has documents. Which TWO are likely causes?

Trap 1: The search algorithm is set to exact (brute-force) and index is…

Exact search still finds matches if they exist.

Trap 2: The query is too long for the embedding model

Models typically truncate long inputs, but still produce an embedding.

Trap 3: The index has not been refreshed after adding new documents

New documents are immediately searchable unless refresh interval is disabled.

A
The search algorithm is set to exact (brute-force) and index is small
Why wrong: Exact search still finds matches if they exist.
B
The query embedding dimension does not match the index dimension
Dimension mismatch causes search errors or zero results.
C
The query is too long for the embedding model
Why wrong: Models typically truncate long inputs, but still produce an embedding.
D
The index has not been refreshed after adding new documents
Why wrong: New documents are immediately searchable unless refresh interval is disabled.
E
The embedding model used for indexing is different from the one used for query
Different models produce incompatible embeddings, so no results match.

Question 3hardmulti select

A developer is troubleshooting low recall in a vector search. Which THREE factors should be checked? (Choose three.)

Trap 1: The number of results returned (k) in the search

While k affects recall, the question is about underlying factors causing low recall, not tuning parameters.

Trap 2: The LLM's temperature setting

Temperature affects generation randomness, not retrieval recall.

A
Embedding model quality and relevance to domain
A model not trained on similar data may produce poor embeddings.
B
Chunk size and overlap strategy
Improper chunking can cause important information to be missed or poorly represented.
C
Quality of the query embedding generation
If the query is not embedded correctly, the search will not align with semantic intent.
D
The number of results returned (k) in the search
Why wrong: While k affects recall, the question is about underlying factors causing low recall, not tuning parameters.
E
The LLM's temperature setting
Why wrong: Temperature affects generation randomness, not retrieval recall.

Question 4hardmulti select

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

Trap 1: OCI Vault for secrets

While good practice, not strictly essential if secrets are managed elsewhere.

Trap 2: OCI Data Flow for data processing

Data Flow is for big data processing, not required for inference.

A
OCI Logging for audit
Logging is critical for monitoring and compliance.
B
OCI Vault for secrets
Why wrong: While good practice, not strictly essential if secrets are managed elsewhere.
C
OCI Data Flow for data processing
Why wrong: Data Flow is for big data processing, not required for inference.
D
Dedicated AI cluster
Essential for hosting the model with predictable performance.
E
OCI IAM policies for access control
IAM policies are fundamental for securing the deployment.

Question 5mediummultiple choice

An AI specialist is troubleshooting why a fine-tuned model produces inconsistent results across different inference calls. What is the most likely cause?

Trap 1: The base model is not suitable

Unsuitable base model leads to consistently poor quality, not inconsistency.

Trap 2: The model is overfitted

Overfitting leads to consistent but poor generalization, not inconsistency.

Trap 3: The fine-tuning dataset is too small

Small dataset may cause overfitting, not necessarily inconsistency.

A
The base model is not suitable
Why wrong: Unsuitable base model leads to consistently poor quality, not inconsistency.
B
The temperature is set too high
High temperature increases randomness, causing variable outputs.
C
The model is overfitted
Why wrong: Overfitting leads to consistent but poor generalization, not inconsistency.
D
The fine-tuning dataset is too small
Why wrong: Small dataset may cause overfitting, not necessarily inconsistency.

Question 6easymulti select

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.)

Trap 1: Invalid API key in the request header

An invalid API key results in a 401 Unauthorized error.

Trap 2: Network connectivity issues

Connectivity issues lead to timeouts or 5xx errors, not 400.

A
Incorrect endpoint URL
A wrong URL may cause the request to be malformed or routed incorrectly, resulting in a 400.
B
Invalid API key in the request header
Why wrong: An invalid API key results in a 401 Unauthorized error.
C
Missing required parameters in the request body
Required fields like 'modelId' or 'prompt' are omitted.
D
Exceeding the model's maximum token limit
The input or output exceeds the model's token limit, causing a bad request.
E
Network connectivity issues
Why wrong: Connectivity issues lead to timeouts or 5xx errors, not 400.

Question 7mediummulti select

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

Trap 1: OCI VCN flow logs for network traffic

Incorrect: Flow logs are for network security analysis, not application monitoring.

Trap 2: OCI Audit logs for all API calls

Incorrect: Audit logs capture control plane operations, not inference data.

A
OCI VCN flow logs for network traffic
Why wrong: Incorrect: Flow logs are for network security analysis, not application monitoring.
B
OCI Logging for inference requests and responses
Correct: Logging allows auditing and debugging of inference calls.
C
OCI Monitoring metrics for endpoint latency and error rates
Correct: Metrics drive performance dashboards and alerts.
D
OCI Application Performance Monitoring (APM) for tracing inference requests
Correct: APM provides distributed tracing to diagnose performance bottlenecks.
E
OCI Audit logs for all API calls
Why wrong: Incorrect: Audit logs capture control plane operations, not inference data.