1Z0-1127 Practice Test 35 — 15 Questions

Question 1

A developer is using the OCI Generative AI SDK in Python to call the cohere.command model. They are getting a 401 Unauthorized error. They have configured the SDK with their tenancy OCID and user OCID. What is the most likely missing piece?

Accepted Answer

API key or token.. Option A is correct. A 401 error indicates authentication failure, typically due to a missing or invalid API key or token. Option B is wrong because an incorrect region endpoint would result in a different error (e.g., 404 or timeout). Option C is wrong because the model OCID is not required for authentication. Option D is wrong because the compartment OCID is used for resource placement, not authentication.

Answer

Correct region endpoint.

Answer

Model OCID.

Answer

Compartment OCID.

Question 2

A healthcare company is deploying an OCI Generative AI service to summarize patient notes. They have recently moved from a managed serving endpoint to a dedicated AI cluster to ensure data privacy. The fine-tuned model is deployed on a dedicated cluster in the US West region. Users report that the summarization responses are now slower and occasionally timeout. The IT team checks the metrics: the cluster has 1 replica and CPU utilization is at 90%. The Object Storage bucket containing the model artifacts is in the same region. They have increased the timeout in their client configuration to 120 seconds, but still get timeouts. What should they do first to address the issue?

Accepted Answer

Increase the number of replicas in the dedicated cluster.. Option B is correct because the cluster is overloaded with a single replica, causing high CPU and timeouts. Increasing replicas distributes the load and reduces latency. Other options are either counterproductive or not feasible.

Answer

Move the Object Storage bucket to a local NVMe cache in the cluster.

Answer

Move the model back to a managed serving endpoint in a different region.

Answer

Increase the max tokens parameter in the API call.

Question 3

A healthcare organization is using OCI Generative AI to analyze medical records. They must comply with HIPAA. They have set up a dedicated AI cluster with private endpoints. However, they are concerned about model hallucinations that could lead to incorrect medical advice. They want to minimize hallucinations while maintaining usefulness. Which approach is most effective?

Accepted Answer

Implement a retrieval-augmented generation (RAG) pipeline with a verified medical knowledge base.. Option B is correct. Implementing a retrieval-augmented generation (RAG) pipeline grounds the model in a verified medical knowledge base, significantly reducing hallucinations. Option A is wrong because smaller models may still hallucinate and may be less capable. Option C is wrong because increasing temperature increases randomness, making hallucinations worse. Option D is wrong because reducing max tokens truncates output but does not address factual accuracy.

Answer

Use a smaller model to reduce complexity.

Answer

Increase the temperature to encourage diverse outputs.

Answer

Reduce max tokens to force shorter responses.

Question 4

A financial services company has deployed a custom fine-tuned model using OCI Generative AI service on a dedicated AI cluster for automated report generation. They use a Python application that sends prompts via the OCI SDK. Recently, they started seeing 429 Too Many Requests errors intermittently. The dedicated cluster has 2 replicas and the application is making about 100 requests per second. The cluster's documented throughput is 50 requests per second per replica. The company has not set up any throttling limits. What is the most likely cause of the 429 errors?

Accepted Answer

The application is exceeding the cluster's replica capacity.. Option B is correct because the application's request rate (100 requests/second) matches the cluster's total capacity (2 replicas x 50 = 100). Any burst can exceed capacity, causing 429 errors. Other options would produce different error codes.

Answer

The OCI SDK version is outdated.

Answer

The API calls are not authenticated.

Answer

The model's context window is too small for the prompts.

Question 5

A startup wants to quickly prototype a chatbot using OCI Generative AI service. They have no prior experience with OCI. They want to test different models and parameters without writing any code and within a few minutes. They also want to save prompts and compare results. Which approach should they use?

Accepted Answer

Use the OCI Generative AI Playground.. Option D is correct because the OCI Generative AI Playground provides a no-code interface for testing models, adjusting parameters, and saving prompt history. The other options require setup or code.

Answer

Create a dedicated AI cluster and use the OCI SDK.

Answer

Use OCI Data Science Notebooks with the GenAI SDK.

Answer

Use OCI Functions to invoke the GenAI API.

Question 6

A company deployed OCI Generative AI for a customer service chatbot. They are using the Cohere command model. The chatbot is generating responses that are too brief and often cut off mid-sentence. They have limited budget. What should they do?

Accepted Answer

Increase max tokens to 1024.. Option C is correct. Increasing the max tokens allows the model to generate longer, complete responses. Option A is wrong because increasing temperature would make responses more random, not longer. Option B is wrong because decreasing temperature reduces variability but does not increase length. Option D is wrong because switching models is costly and may not address the length issue directly.

Answer

Decrease the temperature to 0.2.

Answer

Increase the temperature to 0.9.

Answer

Use a different base model like Llama.

Question 7

A company uses OCI Generative AI to generate legal document summaries. They have a custom model deployed on a dedicated AI cluster. They want to ensure that the model is not used by unauthorized users. They also need to log all inference requests for auditing. Which combination of OCI services should they use?

Accepted Answer

OCI Identity and Access Management (IAM) policies and OCI Logging.. Option B is correct. OCI Identity and Access Management (IAM) policies control access to the model endpoint, and OCI Logging captures inference request logs for auditing. Option A is wrong because OCI Vault is for managing secrets, and OCI Audit logs administrative actions, not inference requests. Option C is wrong because OCI Data Safe is for database security, and OCI Monitoring tracks metrics, not logs. Option D is wrong because API Gateway with authentication and OCI Audit may not capture detailed model inference logs.

Answer

OCI Vault for encryption and OCI Audit for logging.

Answer

OCI Data Safe and OCI Monitoring.

Answer

OCI API Gateway with authentication and OCI Audit.

Question 8

An e-commerce company uses OCI Generative AI to generate product descriptions. They have fine-tuned the model on their product catalog. They notice that the descriptions are accurate but lack creativity and are repetitive. They want to maintain accuracy while adding variety. What change should they make?

Accepted Answer

Increase the temperature from 0.2 to 0.5.. Option B is correct. Slightly increasing temperature (e.g., from 0.2 to 0.5) introduces controlled variability without significantly compromising accuracy. Option A is wrong because top_p=1.0 samples from the full distribution, which can add noise. Option C is wrong because adding more training data requires effort and time, and may not immediately add variety. Option D is wrong because changing the base model could hurt accuracy and requires retraining.

Answer

Increase the top_p sampling from 0.9 to 1.0.

Answer

Use a different base model.

Answer

Add more training examples with diverse descriptions.

Question 9

A startup is using OCI Generative AI serverless inference for a text generation application. They notice that the latency is high during peak hours. They have a budget to increase costs moderately. Which action would most effectively reduce latency?

Accepted Answer

Switch to dedicated AI cluster.. Option A is correct. Switching to a dedicated AI cluster provides consistent low latency compared to serverless inference. Option B is wrong because using a smaller model might reduce latency but could degrade quality. Option C is wrong because enabling content filtering does not affect latency. Option D is wrong because increasing concurrent requests may increase load without improving latency.

Answer

Enable content filtering.

Answer

Increase the number of concurrent requests.

Answer

Use a smaller model.

Question 10

A data scientist is fine-tuning a model on OCI Generative AI to generate code comments. They use a dataset of 10,000 examples. After fine-tuning, the model generates comments that are too similar to the training data and lack generalization. What is the most likely cause?

Accepted Answer

Too many training epochs.. Option C is correct. Too many training epochs lead to overfitting, where the model memorizes the training data rather than learning general patterns. Option A is wrong because 10,000 examples is not insufficient for fine-tuning; overfitting is more likely. Option B is wrong because a high learning rate causes divergence, not memorization. Option D is wrong because an incorrect tokenizer would cause garbled output, not just over-similarity.

Answer

Incorrect tokenizer.

Answer

Insufficient training data.

Answer

Too high learning rate.

Question 11

A startup is building a customer support chatbot using RAG with OCI Generative AI. They have a large corpus of FAQ documents stored as PDFs in OCI Object Storage. The developer uses OCI Language to embed the text and stores vectors in OCI OpenSearch. During testing, the chatbot often fails to answer questions because relevant FAQ entries are not retrieved. The team suspects the chunking size is too large, causing loss of specific details. After reducing chunk size, retrieval improves slightly but still misses many answers. What should the team do NEXT?

Accepted Answer

Use a sliding window chunking strategy with overlap. The problem is likely chunk boundaries cutting off context. Using a sliding window with overlap ensures continuity, so relevant information is not lost at chunk edges. Increasing k adds noise; switching model is expensive; manual rephrasing is not scalable.

Answer

Increase the number of retrieved chunks (k)

Answer

Switch to a different embedding model

Answer

Manually rephrase the queries

Question 12

A small business is building an internal Q&A bot using OCI Generative AI with RAG. They have indexed their product manuals into OCI OpenSearch using a precomputed embedding model. When they test queries, the bot often returns answers that are only partially relevant, and sometimes it cannot find answers for questions that are clearly present in the manuals. The developers suspect the chunking strategy is suboptimal. Currently, they use a fixed chunk size of 512 tokens with no overlap. What should they do to improve retrieval relevance?

Accepted Answer

Add a 20% token overlap between consecutive chunks.. Option A is correct because adding overlap helps capture context across chunk boundaries, which is a common cause for missing information. Option B may reduce precision and cause noise. Option C without overlap still misses context. Option D may increase missed context due to smaller chunks.

Answer

Increase the chunk size to 1024 tokens to include more context.

Answer

Reduce the chunk size to 256 tokens to increase precision.

Answer

Switch to a sentence-based chunking strategy with no overlap.

Question 13

A data scientist is using OCI Data Science to build a RAG system for medical literature. They have a large corpus of PDFs. They used the default OCI Generative AI embedding model and chunked each PDF into 512-character segments with 10% overlap. However, queries about specific drug doses often return incorrect information, even though the correct dose is present in the corpus. Upon inspection, they find that the retrieved chunks often contain partial dose information or miss the context units (e.g., mg vs. mcg). What improvement should they prioritize?

Accepted Answer

Use a semantic chunking strategy that respects document structure (e.g., paragraphs, sections).. Option A is correct because semantic chunking that respects document structure (e.g., paragraphs, sections) will keep dose information together. Option B (more overlap) may help but does not address structural breaks. Option C (fine-tuning) is heavy and may not fix chunk boundaries. Option D is a workaround, not a core fix.

Answer

Implement a secondary verification step using a rule-based pattern matcher.

Answer

Increase the chunk overlap to 50% to ensure more context.

Answer

Fine-tune the embedding model on medical text.

Question 14

A retail company uses OCI Generative AI Agents to power a product recommendation chatbot on their e-commerce website. The chatbot is integrated with a knowledge base containing product descriptions, customer reviews, and inventory data. Recently, the chatbot has started recommending out-of-stock products frequently, leading to customer frustration. The development team verified that the knowledge base is updated in real-time with inventory data. The chatbot's configuration uses a chunking strategy with a chunk size of 500 tokens and an overlap of 50 tokens. The team suspects the issue is related to how the agent retrieves information. They have access to OCI Logging and Monitoring. Which course of action should the team take first?

Accepted Answer

Increase the chunk overlap from 50 to 150 tokens to ensure inventory status is captured in multiple chunks.. The core issue is that the chatbot retrieves chunks that contain product descriptions but may miss the inventory status because the chunking strategy does not reliably include both pieces of information together. Increasing the chunk overlap from 50 to 150 tokens ensures that inventory data, which may be at the boundary of a chunk, is captured in multiple overlapping chunks, thereby increasing the likelihood that the retrieval step returns a chunk containing both the product and its current stock level. This directly addresses the retrieval gap without altering model behavior or infrastructure.

Answer

Decrease the chunk size to 250 tokens to make chunks more specific.

Answer

Reduce the temperature parameter of the model to 0.2 to reduce hallucinations.

Answer

Enable auto-scaling on the AI cluster to improve response speed.

Question 15

You are a cloud architect at a global e-commerce company. The company is building a RAG-based product support chatbot using OCI Generative AI Service and OCI OpenSearch. The chatbot must answer customer questions in real-time by retrieving from a product knowledge base containing over 10 million documents. The current architecture uses a single vector index with all documents, and the LLM (Cohere Command R+) returns answers in English only. The team observes that queries from non-English customers often return irrelevant results, and the chatbot sometimes fails to generate answers within the 5-second SLA. The leadership wants to support 10 languages and reduce the average response time to under 3 seconds. You need to propose a solution that improves both relevance and latency. Which course of action should you take?

Accepted Answer

Replace the embedding model with a multilingual model and partition the vector index by language to reduce search space.. Option B is correct because partitioning the vector index by language reduces the search space for each query, directly improving retrieval latency, while using a multilingual embedding model ensures that non-English queries are semantically matched to documents in their original language, improving relevance. This combination addresses both the 3-second SLA and the 10-language requirement without relying on translation, which introduces latency and potential loss of meaning.

Answer

Increase the number of OCI OpenSearch nodes and upgrade the LLM to a faster variant.

Answer

Translate all non-English queries to English before retrieval and use an English-only embedding model.

Answer

Implement a caching layer for frequent queries and use a larger LLM for better accuracy.