1Z0-1127-25 Practice Test 31 — 15 Questions

Question 1

A developer wants to build a RAG application that processes highly sensitive medical records. The documents are already stored in OCI Object Storage. Which vector storage strategy best balances security and performance?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use OCI OpenSearch with a private subnet and VCN security lists. It uses OCI OpenSearch deployed within a private subnet, which ensures that vector data never traverses the public internet, while VCN security lists provide granular traffic control. This architecture balances security (data isolation and access control) with performance (low-latency access within the same VCN or via FastConnect/IPSEC VPN) for sensitive medical records.

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Store vectors in-memory within the application server

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Use OCI OpenSearch with a public endpoint for low latency

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Use a third-party vector database outside OCI

Question 2

A company uses OCI GenAI to build a content moderation system that filters toxic language in user-generated comments. They have a small labeled dataset of 1,000 comments (500 toxic, 500 non-toxic) and need an efficient solution that balances accuracy, cost, and latency. They are considering different model options: fine-tuning a large LLM (e.g., Cohere Command), using a pre-trained LLM with prompting, fine-tuning a smaller BERT-based classifier, or building a rule-based system. The team has moderate ML experience and wants to deploy using OCI Data Science. Which approach is most efficient for this binary classification task?

Accepted Answer

Fine-tune a BERT-based classifier (e.g., 'bert-base-uncased') on the dataset.. Fine-tuning a BERT-based classifier (e.g., 'bert-base-uncased') is the most efficient approach because BERT is specifically designed for text classification tasks, requiring far fewer computational resources and lower latency than large LLMs. With only 1,000 labeled samples, BERT can achieve high accuracy through transfer learning, while keeping inference costs minimal—ideal for a production content moderation system on OCI Data Science.

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Develop a rule-based system using regular expressions and keyword lists.

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Use a pre-trained LLM with a toxic/non-toxic prompt.

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Fine-tune the Cohere Command model on the labeled dataset.

Question 3

A developer is using OCI Data Science to create a RAG pipeline. They have ingested documents into a vector store using OCI Generative AI's text-embedding model. During testing, they notice that queries return very few results (often 0 or 1) even when the knowledge base contains relevant documents. They have set the top-k parameter to 10. What is the most likely cause?

Accepted Answer

The similarity threshold is set too high, filtering out most results.. A high similarity threshold (e.g., >0.9) can exclude many relevant results, leading to few or zero results even with top-k set to 10. Option B: chunk size affects the granularity of text but not directly the number of results returned; small chunks can still be retrieved if the threshold is appropriate. Option C: dimensionality of the embedding model is fixed and does not directly cause zero results; low dimensionality may reduce semantic precision but not eliminate results. Option D: distance metric affects how similarity is computed but not the count; the index can still return results ranked by the chosen metric.

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The documents were chunked with too small a chunk size, losing key information.

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The embedding model's dimensionality is too low to capture semantic differences.

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The vector search index is not configured with the correct distance metric.

Question 4

A user has a prompt that exceeds the model's token limit. What is the best practice to handle this?

Accepted Answer

Summarize the earlier parts of the prompt and include the summary.. When a prompt exceeds the model's token limit, the best practice is to summarize the earlier parts of the prompt and include the summary. This preserves the essential context without exceeding the token limit, as the model's context window is fixed (e.g., 4,096 tokens for GPT-3.5 or 8,192 for GPT-4). Summarization reduces token count while retaining key information, enabling the model to process the entire input within its constraints.

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Increase the max tokens parameter in the API call.

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Truncate the prompt and hope the model understands.

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Split the input into multiple calls and merge results.

Question 5

Which TWO are best practices for building a RAG application on OCI? (Choose two.)

Accepted Answer

Use a vector database such as OCI OpenSearch with ANN indexes for storing embeddings.. OCI OpenSearch with Approximate Nearest Neighbor (ANN) indexes is a best practice for vector storage and retrieval in RAG applications. ANN indexes enable efficient similarity search over high-dimensional embeddings, which is essential for retrieving relevant context from large document collections at low latency.

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Generate embeddings for documents at query time to ensure freshness.

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Store the source documents only in OCI Object Storage and retrieve them at query time using full-text search.

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Use a different embedding model for documents and queries to capture distinct semantics.

Question 6

A company has deployed a fine-tuned GPT model on OCI Generative AI using a dedicated AI cluster with 2 nodes. The endpoint is used by an internal application that generates product descriptions. Recently, the application started receiving timeouts and slow responses. The monitoring dashboard shows that the cluster's CPU utilization is consistently above 90%, and the request queue is growing. The team has verified that the model and code have not changed. The application traffic has increased by 20% over the past month. What should the team do to resolve the issue?

Accepted Answer

Increase the number of nodes in the dedicated AI cluster from 2 to 4.. The dedicated AI cluster with 2 nodes is experiencing sustained CPU utilization above 90% and a growing request queue due to a 20% increase in traffic. Scaling out the cluster by adding more nodes (from 2 to 4) increases the available compute capacity, allowing the cluster to handle the higher inference load without timeouts. This directly addresses the resource bottleneck without requiring code or model changes.

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Switch to a serverless endpoint to handle variable traffic.

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Reduce the batch size in the inference requests to lower CPU usage.

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Implement a caching layer for frequently requested descriptions.

Question 7

A developer wants to implement a simple RAG pipeline using OCI Language's text generation and embedding models. Which OCI SDK method is used to generate embeddings for a text chunk?

Accepted Answer

embed_text. The correct OCI SDK method for generating embeddings for a text chunk is `embed_text`. This method is part of the OCI Language service's `AIServiceLanguageClient` and directly returns vector representations of input text, which are essential for RAG pipelines to enable semantic search and retrieval.

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generate_embeddings

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encode_text

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create_embedding

Question 8

A data scientist is using OCI Generative AI to process a large batch of legal documents. The total cost is higher than expected. Which factor is most likely the primary driver of cost?

Accepted Answer

The total number of tokens processed (input + output). In OCI Generative AI, pricing is primarily based on the total number of tokens processed, which includes both input (prompt) and output (generated) tokens. Processing large batches of legal documents generates high token counts due to lengthy text inputs and verbose outputs, directly increasing cost. The number of API requests alone does not determine cost—a single request with many tokens costs more than many requests with few tokens.

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The number of API requests made

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The choice of sampling strategy (e.g., top-k vs greedy)

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The latency of the inference endpoint

Question 9

During fine-tuning of a Cohere model on OCI Data Science, the loss curve shows a sharp spike after epoch 3. What is the most appropriate action?

Accepted Answer

Gradient clipping.. A sharp spike in the loss curve after epoch 3 during fine-tuning indicates a gradient explosion, where the gradients become excessively large and destabilize the model's weights. Gradient clipping is the most appropriate action because it directly caps the gradient norm (e.g., using `max_grad_norm=1.0` in Cohere's fine-tuning API) to prevent these spikes, ensuring stable training without altering the learning dynamics.

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Reduce learning rate.

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Add more training data.

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Increase batch size.

Question 10

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.. 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 11

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.. The dedicated AI cluster has only 1 replica and CPU utilization is at 90%, indicating that the single replica is overloaded and cannot handle the inference request volume. Increasing the number of replicas distributes the load, reduces latency, and prevents timeouts. This is the most direct and scalable fix for performance bottlenecks in a dedicated OCI Generative AI cluster.

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

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

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Increase the max tokens parameter in the API call.

Question 12

An architect is optimizing an LLM application that processes long documents. The model has a 4096 token limit, but the documents are often 8000 tokens. They are using a chunking strategy. However, model responses sometimes miss key information that spans across chunks. Which technique most directly addresses this issue?

Accepted Answer

Use overlapping chunks to maintain context continuity.. Overlapping chunks ensure that tokens at the boundaries of one chunk are also present at the start of the next, preserving context continuity. This prevents the model from losing information that spans across chunk boundaries, which is a common issue when processing documents longer than the model's 4096-token context window.

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Randomly select parts of the document to include.

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Increase the max_tokens parameter for longer outputs.

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Use a model with a larger context window.

Question 13

A data scientist is using OCI Data Science with the Generative AI service to fine-tune a Cohere Command model on a custom dataset of customer support tickets. After training, the model produces poor, irrelevant responses. What is the most likely cause?

Accepted Answer

Insufficient training data quality or quantity. Insufficient training data quality or quantity is the most likely cause because fine-tuning a Cohere Command model on a custom dataset of customer support tickets requires a sufficiently large and representative dataset to teach the model domain-specific patterns. If the dataset is too small, noisy, or lacks diversity, the model will fail to generalize and produce irrelevant responses, even with correct tokenization and training hyperparameters.

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Incorrect tokenizer configuration

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Too many epochs causing overfitting

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Model architecture mismatch between fine-tuned and base model

Question 14

A developer wants the LLM to solve a math problem by reasoning step by step. Which prompting technique should they use?

Accepted Answer

Chain-of-thought prompting. Chain-of-thought prompting explicitly instructs the model to show its reasoning steps, improving accuracy on multi-step problems.

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Zero-shot prompting

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Tree-of-thought prompting

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Few-shot prompting

Question 15

A developer is building a summarization pipeline using OCI Generative AI. They want to ensure the summary includes key points from the entire document without truncation. Which parameter should they primarily adjust?

Accepted Answer

Max_tokens. The max_tokens parameter controls the maximum length of the generated output. Increasing it allows longer summaries, preventing truncation.

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Frequency_penalty

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Temperature

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Top_p