1Z0-1127 Practice Test 30 — 15 Questions

Question 1

A company uses OCI Generative AI Service to generate personalized email content. They need to ensure that personally identifiable information (PII) is not included in the model's training data. What should they do?

Accepted Answer

Enable data redaction in the service. Option D is correct because OCI Generative AI Service provides a built-in data redaction feature that automatically detects and removes personally identifiable information (PII) from training data before it is used for model training. This ensures compliance with data privacy regulations without requiring manual preprocessing or external tools.

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Encrypt the training data with OCI Vault

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Use the moderation API to scan outputs

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Use a dedicated model endpoint

Question 2

Which THREE of the following are supported capabilities of OCI Generative AI Service?

Accepted Answer

Text summarization. Option A is correct because OCI Generative AI Service includes a dedicated text summarization capability that uses large language models (LLMs) to generate concise summaries from longer documents. This feature is part of the service's core generative AI offerings, supporting use cases like meeting notes summarization and document abstraction.

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Sentiment analysis

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Image generation

Question 3

A team is using OCI Generative AI Agents to build a customer support bot. The bot sometimes generates answers that contradict the knowledge base. What is the most likely cause?

Accepted Answer

The chunking strategy for the knowledge base does not capture enough context overlap.. Option A is correct because when the chunking strategy lacks sufficient context overlap, the retrieved chunks may omit critical surrounding information, causing the generative AI model to infer missing details incorrectly and produce answers that contradict the knowledge base. In OCI Generative AI Agents, the chunking strategy determines how documents are split into smaller pieces for retrieval; without adequate overlap, the model loses the semantic continuity needed to stay faithful to the source material.

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The max tokens value is too low, truncating the response.

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The temperature parameter is set too high, causing the model to hallucinate.

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The model's repetition penalty is too high.

Question 4

A developer wants to use OCI Generative AI Service to summarize long documents. Which endpoint should they use to send the document content?

Accepted Answer

/chat. Option D is correct because the /chat endpoint in OCI Generative AI Service is designed for conversational interactions and can handle long document summarization by accepting the document content as part of the chat context. This endpoint supports multi-turn dialogues and large input payloads, making it suitable for processing and summarizing lengthy documents.

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/generate

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/classify

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/embed

Question 5

A company uses OCI Generative AI Service to build a chatbot for customer support. They notice that the model sometimes generates inappropriate responses. What is the MOST effective way to mitigate this without retraining the model?

Accepted Answer

Configure system instructions to define acceptable behavior. Configuring system instructions is the most effective approach because it allows you to define the model's behavior and constraints at inference time without modifying the underlying model weights. In OCI Generative AI Service, system instructions act as a persistent prompt that guides the model's responses, enabling you to explicitly prohibit inappropriate content and enforce safety guidelines. This is a non-invasive, immediate mitigation that does not require the time, cost, or data preparation associated with retraining or fine-tuning.

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Fine-tune the model with curated safe examples

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Reduce the temperature parameter to 0

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Use the moderation API to filter responses

Question 6

A healthcare company is deploying OCI Generative AI Service for clinical decision support. They must ensure that model outputs are auditable, explainable, and free from patient data exposure. Which combination of OCI features should they use?

Accepted Answer

Use OCI Data Masking to de-identify inputs, and enable model monitoring with explainability outputs via OCI Monitoring and OCI Logging.. Option C is correct because OCI Data Masking can de-identify patient data in inputs before they reach the generative AI model, ensuring no protected health information (PHI) is exposed. Enabling model monitoring with explainability outputs via OCI Monitoring and OCI Logging provides an auditable trail of model decisions and explanations, meeting the requirements for auditability and explainability in clinical decision support.

Answer

Fine-tune a model on de-identified patient notes and use default inference settings.

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Use Retrieval-Augmented Generation with an internet search index for up-to-date medical knowledge.

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Deploy the model in a private endpoint and disable all logging to prevent data leaks.

Question 7

A company is deploying a multi-language chatbot using OCI Generative AI Service. The chatbot must support English, Spanish, and French. The team finds that responses in Spanish are less accurate than in English. They have a small bilingual dataset. What is the best approach?

Accepted Answer

Use a multilingual base model (e.g., mT5) and fine-tune on the bilingual dataset (English and Spanish) using cross-lingual transfer learning.. Option A is correct because fine-tuning a multilingual base model like mT5 on a small bilingual dataset leverages cross-lingual transfer learning, where knowledge from high-resource languages (English) improves performance on low-resource languages (Spanish). This approach is specifically designed for scenarios with limited data and directly addresses the accuracy gap without requiring separate models or translation pipelines.

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Use prompt engineering with language-specific instructions in the system prompt.

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Translate all user queries to English, process them, then translate responses back.

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Train separate fine-tuned models for each language.

Question 8

A manufacturing company uses OCI OpenSearch to build a RAG application that retrieves procedural documents. After deployment, queries often return outdated procedures even though the vector index was refreshed. What is the most likely cause?

Accepted Answer

The full-text search index is not synchronized with the vector index after updates.. Option B is correct because in a RAG application using OCI OpenSearch, the vector index and full-text search index are separate. When procedural documents are updated, the full-text search index may reflect changes immediately, but the vector index requires explicit re-indexing or synchronization to update embeddings. If the vector index is not refreshed after updates, queries can still retrieve outdated vector representations, leading to outdated results despite the index being refreshed.

Answer

The embedding model was fine-tuned on outdated data.

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The BM25 scoring algorithm prioritizes older documents due to term frequency.

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The chunk overlap percentage is too high, causing duplicate context.

Question 9

A developer receives the above error when querying a RAG application. What is the most likely cause and recommended action?

Accepted Answer

The API rate limit has been exceeded; wait for the retry period and implement exponential backoff.. The 429 (Too Many Requests) error indicates the API rate limit has been exceeded. In OCI Generative AI services, rate limits are enforced per tenancy and per region; the recommended action is to wait for the retry-after period and implement exponential backoff to avoid overwhelming the service.

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The model is deprecated; update to the latest model.

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The endpoint URL is incorrect; verify the OCI region endpoint.

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The request payload is malformed; check the input format.

Question 10

A healthcare startup is building a chatbot that retrieves patient treatment guidelines using OCI Generative AI Service and OCI OpenSearch. They require that all retrieved documents are from approved sources only and that the system can explain which source was used for each response. Which combination of features should they implement?

Accepted Answer

Add a metadata filter for source_type='approved' in the retrieval step and include document IDs in the context for the model.. Option A is correct because it directly addresses both requirements: a metadata filter on `source_type='approved'` ensures only approved documents are retrieved from OpenSearch, and including document IDs in the context allows the model to cite the specific source for each response. This approach enforces access control at the retrieval layer while providing traceability, which is essential for compliance in healthcare applications.

Answer

Rely on the vector search's cosine similarity to rank approved sources higher.

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Use prompt engineering to ask the model to ignore non-approved sources.

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Reduce the top-K value to limit the number of retrieved documents.

Question 11

A data scientist is fine-tuning a generative AI model on OCI Data Science using a custom container with GPU resources. The training job fails with an out-of-memory error despite the GPU instance having sufficient memory. The job works fine on a smaller dataset. What is the most likely cause?

Accepted Answer

The batch size is too large for the GPU memory. The most likely cause is that the batch size is too large for the GPU memory. Even though the GPU instance has sufficient total memory, a batch size that exceeds the available GPU memory (after accounting for model parameters, gradients, and optimizer states) will trigger an out-of-memory (OOM) error. Reducing the batch size allows the model to fit within the GPU's memory limits, which explains why the job works on a smaller dataset but fails on a larger one.

Answer

The training script has a memory leak

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The GPU instance is not supported by OCI Data Science

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The model is not compatible with the PyTorch version

Question 12

A research institution uses OCI Data Flow to process large-scale document corpora for a RAG system. They want to minimize latency for end-user queries. Which architecture decision would most effectively reduce query latency?

Accepted Answer

Precompute embeddings offline using OCI Data Flow and store them in an OCI OpenSearch index.. Precomputing embeddings offline with OCI Data Flow and storing them in an OCI OpenSearch index eliminates the need to generate embeddings at query time, which is the primary source of latency. This approach shifts the computationally expensive embedding generation to a batch process, allowing queries to perform only a fast vector similarity search against the precomputed index, drastically reducing end-user response time.

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Embed documents on-the-fly during query time to ensure freshness.

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Use a larger, more accurate embedding model.

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Increase the number of Spark workers for parallel processing of queries.

Question 13

Which TWO actions are best practices when deploying a RAG application using OCI OpenSearch and OCI Generative AI?

Accepted Answer

Implement a reranker to improve the relevance of retrieved documents.. Option B is correct because implementing a reranker improves retrieval precision by re-scoring the top-k documents from the initial vector search using a cross-encoder model, which captures deeper semantic relevance than cosine similarity alone. In OCI OpenSearch, this is typically done via a post-processing step with OCI Generative AI or a dedicated reranking model, ensuring only the most contextually relevant chunks are passed to the LLM for generation.

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Embed every document chunk in real-time during query processing.

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Use very small chunk sizes (e.g., 50 tokens) to maximize granularity.

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Set the LLM temperature to 1.5 to encourage diverse outputs.

Question 14

A company uses OCI Generative AI service with a Cohere Command model for a real-time chat application and experiences high latency. They have already set max_tokens to 50 and temperature to 0.2. Which further change would be most effective in reducing latency?

Accepted Answer

Switch to a smaller model variant.. Switching to a smaller model variant (e.g., from Command to Command-Light) directly reduces the number of parameters and computational steps per token, which lowers inference latency. Since the company has already minimized max_tokens and temperature, the next most impactful change is to use a less resource-intensive model. This is a common optimization for real-time applications where response speed is critical.

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Use asynchronous invocation.

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Disable context caching.

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Increase the number of GPUs.

Question 15

A company is deploying a generative AI service on OCI using the OCI Data Science service with a large language model (LLM) in a VCN. The model inference endpoint must be accessible only from a private subnet within the same VCN. Which networking component should be configured to enable this?

Accepted Answer

Service Gateway. A Service Gateway enables private subnet resources to access OCI services (including the OCI Data Science model deployment endpoint) without traversing the internet. Since the inference endpoint must be accessible only from a private subnet within the same VCN, the Service Gateway provides the necessary private connectivity by routing traffic over the OCI network fabric, not through a NAT or internet gateway.

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NAT Gateway

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Dynamic Routing Gateway (DRG)

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Internet Gateway