1Z0-1127 Timed Practice Test 2 — 10 Questions

Question 1

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 2

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 3

An enterprise deployed a custom fine-tuned model for generating financial reports. After the first month, the model's outputs began to include outdated information and occasional factual errors. The team suspects data drift. What is the best course of action?

Accepted Answer

Retrain the model on the latest financial data and monitor for drift.. Option D is correct because retraining with up-to-date data addresses the root cause of data drift. Option A is wrong because adjusting temperature may reduce creativity but not fix factual accuracy. Option B is wrong because increasing max tokens does not improve accuracy. Option C is wrong because switching to a different base model without retraining does not address drift.

Answer

Switch to a newer base model like Llama 3.1 without retraining.

Answer

Decrease the temperature parameter to 0.1 to reduce model creativity.

Answer

Increase the max tokens value to allow longer responses.

Question 4

A developer is building a RAG application using Oracle Cloud Infrastructure (OCI) Document Understanding and OCI Generative AI. After chunking documents and generating embeddings, the developer observes that the retrieval step often returns chunks that are semantically unrelated to the query. Which action is MOST likely to improve retrieval relevance?

Accepted Answer

Adjust the chunk size and chunk overlap to better capture coherent passages.. Option C is correct because adjusting the chunk size and overlap can significantly impact the quality of retrieved passages. Option A is wrong because increasing the chunk size may introduce more noise. Option B is wrong because reducing the number of retrieved chunks could miss relevant information. Option D is wrong because the embedding model is already chosen; changing it may not fix the chunking issue.

Answer

Switch from a dense embedding model to a sparse embedding model.

Answer

Increase the chunk size to capture more context.

Answer

Reduce the number of retrieved chunks (k) in the vector search.

Question 5

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 6

A developer is using OCI Generative AI to build a question-answering system over a large corpus of technical manuals. The developer uses the Cohere Embed model to generate embeddings and stores them in an OCI OpenSearch cluster. Queries are slow and the team needs to reduce latency. Which approach is BEST for improving search speed while maintaining acceptable accuracy?

Accepted Answer

Reduce the k value in the nearest neighbor search.. Reducing the k value in the nearest neighbor search directly decreases the number of vectors that must be compared during query time, which lowers latency. In approximate nearest neighbor (ANN) search, a smaller k means fewer candidates are evaluated, speeding up retrieval while still maintaining acceptable accuracy if the original k was unnecessarily high. This is the most effective tuning knob for latency in vector search systems like OCI OpenSearch with Cohere embeddings.

Answer

Increase the embedding dimension for better representation.

Answer

Use exact nearest neighbor search instead of approximate.

Answer

Increase the index refresh interval to reduce write overhead.

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

An administrator runs the above CLI command to check the status of a dedicated AI cluster. The cluster is ACTIVE with capacity 10. However, a user reports that inference requests to this cluster are failing with a '429 Too Many Requests' error. What is the most likely cause?

Accepted Answer

The cluster is hitting the maximum inference requests per minute limit. The '429 Too Many Requests' error is an HTTP status code indicating rate limiting has been exceeded. In OCI Generative AI, dedicated AI clusters have a configurable 'maximum inference requests per minute' limit. Even if the cluster is ACTIVE and has capacity (e.g., 10 nodes), hitting this per-minute request cap will cause the API gateway to reject further requests with a 429 error. The administrator must increase the rate limit or implement client-side throttling to resolve this.

Answer

The cluster does not have enough nodes to handle the load

Answer

The user is not in the same compartment as the cluster

Answer

The cluster is not in ACTIVE state

Question 9

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 10

A team is deploying a generative AI model using OCI Functions for serverless inference. They are experiencing cold start latency of over 10 seconds for the first invocation after idle periods. What is the best strategy to reduce cold start latency?

Accepted Answer

Use provisioned concurrency to keep a set number of function instances warm.. Option B is correct because OCI Functions supports provisioned concurrency, which keeps a specified number of instances warm. Option A (increasing memory) can reduce cold start but not as effectively. Option C (reducing timeout) might cause failures. Option D (using OCI Data Flow) is for data processing, not inference.

Answer

Migrate the inference to OCI Data Flow for better performance.

Answer

Reduce the function timeout to force faster execution.

Answer

Increase the memory allocation for the function.