1Z0-1127-25 Practice Test 47 — 15 Questions

Question 1

A developer is deploying a fine-tuned model using OCI Generative AI service. They want to use a custom container image for inference. Which statement is true?

Accepted Answer

You can upload a container image to OCI Container Registry and reference it when creating a dedicated AI cluster.. OCI Generative AI service allows you to bring your own custom container image for inference by uploading it to OCI Container Registry (OCIR) and referencing it when creating a dedicated AI cluster. This enables you to deploy fine-tuned models with custom inference logic, dependencies, or frameworks that are not available in the built-in serving containers.

Answer

Custom containers are only supported with OCI Data Science, not Generative AI.

Answer

Custom containers are supported only for fine-tuning jobs, not inference.

Answer

Custom containers are not supported; only built-in models are available.

Question 2

A company's RAG application ingests news articles that are updated frequently. The vector store in OCI OpenSearch contains embeddings of the articles. The team notices that outdated information is still retrieved even after updating the source documents. What is the most effective way to ensure the vector store reflects the latest content?

Accepted Answer

Use the OCI OpenSearch document update API to replace embeddings for changed documents. Using the OCI OpenSearch document update API to replace embeddings for changed documents is efficient and targeted, ensuring immediate consistency.

Answer

Increase the TTL for vector indices

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Rely on the LLM to ignore outdated information

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Re-index the entire vector store daily

Question 3

Which THREE factors directly influence the quality of responses in a RAG system? (Choose three.)

Accepted Answer

The prompt template used to ask the LLM. The prompt template directly controls how the LLM interprets the retrieved context and formulates its response. A well-structured prompt with clear instructions, context formatting, and output constraints significantly improves response relevance and accuracy, while a poorly designed prompt can lead to hallucinations or off-topic answers.

Answer

The temperature parameter of the LLM

Answer

The number of GPUs allocated to the LLM

Question 4

Which TWO of the following are valid similarity metrics used in vector search?

Accepted Answer

Cosine similarity. Cosine similarity measures the cosine of the angle between two vectors, focusing on orientation rather than magnitude. It is widely used in vector search for comparing embeddings because it effectively captures semantic similarity in high-dimensional spaces, such as those produced by LLMs.

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Levenshtein distance

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Hamming distance

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Jaccard index

Question 5

Which TWO of the following are best practices when implementing a RAG application using OCI OpenSearch as a vector store?

Accepted Answer

Enable approximate nearest neighbor (ANN) search for large datasets.. For large datasets, exact nearest neighbor (k-NN) search becomes computationally expensive and slow. OCI OpenSearch supports approximate nearest neighbor (ANN) search using algorithms like HNSW, which dramatically reduce latency while maintaining high recall, making it essential for production RAG applications with millions of vectors.

Answer

Use a large embedding dimension (e.g., 1536) to improve accuracy.

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Set index.number_of_replicas to 0 to speed up indexing.

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Store the embedding vectors in the _source field to simplify retrieval.

Question 6

An OCI user observes that their Mistral model produces very repetitive text when temperature is set to 0.9 and top-p to 1.0. Which adjustment is most likely to reduce repetition?

Accepted Answer

Enable frequency penalty. Enabling frequency penalty directly reduces the likelihood of the model repeating the same tokens by subtracting a penalty proportional to the token's existing frequency in the generated text. This is the most targeted way to combat repetitive output without altering the core sampling parameters (temperature and top-p) that control randomness.

Answer

Decrease top-p to 0.5

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Increase temperature to 1.5

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Set top-k to 1

Question 7

An organization wants to use OCI Generative AI for a multilingual translation task. They need high quality and must avoid biases present in the training data. Which THREE strategies should they consider? (Select THREE.)

Accepted Answer

Implement a human-in-the-loop review process to catch biased translations. Fine-tuning on high-quality parallel corpora improves accuracy. Using models designed for translation (e.g., encoder-decoder) often yields better results. Implementing human-in-the-loop review catches biases. Increasing temperature may reduce bias but also reduces quality; it is not a primary strategy for bias mitigation. RAG is not directly applicable to translation as it requires retrieved documents in the target language.

Answer

Use a RAG pipeline to retrieve canonical translations from a trusted database

Answer

Increase the temperature parameter to 1.5 to reduce repetitive biases

Question 8

Which of the following best describes the role of the self-attention mechanism in a Transformer model?

Accepted Answer

It computes a weighted sum of all input token representations, where weights depend on pairwise compatibility between tokens. The self-attention mechanism computes a weighted sum of all input token representations, where the weights are determined by the pairwise compatibility (attention scores) between tokens. This allows each token to dynamically attend to every other token in the sequence, capturing global dependencies without the limitations of fixed local windows or recurrence.

Answer

It encodes the order of tokens in the sequence

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It applies a convolutional filter over local windows of tokens

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It replaces the need for positional encoding by using recurrence

Question 9

A team is optimizing a RAG pipeline for OCI Generative AI. They observe that the model's responses are verbose and often include irrelevant details from the retrieved chunks, reducing user satisfaction. They have already tuned the prompt template. What is the most effective next step?

Accepted Answer

Implement a re-ranking step using a cross-encoder model.. Implementing a re-ranking step with a cross-encoder model directly addresses the problem of verbose and irrelevant responses. Cross-encoders evaluate the query-document pair jointly, producing a fine-grained relevance score that filters out noisy or off-topic chunks before they reach the generation model. This improves the quality of the context provided to the LLM, reducing verbosity and irrelevance without requiring retraining or altering the retrieval threshold.

Answer

Apply instruction tuning on the generation model.

Answer

Reduce the number of retrieved chunks from 5 to 3.

Answer

Increase the similarity threshold for retrieval from 0.7 to 0.85.

Question 10

Which model architecture is used by BERT for natural language understanding tasks?

Accepted Answer

Encoder-only. BERT uses an encoder-only architecture, which processes the entire input sequence bidirectionally. This makes it well-suited for tasks like classification, NER, and QA where understanding the full context is important.

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Recurrent neural network

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Encoder-decoder

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Decoder-only

Question 11

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. 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.

Answer

Encrypt the training data with OCI Vault

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Use the moderation API to scan outputs

Answer

Use a dedicated model endpoint

Question 12

A healthcare company is building a RAG-based chatbot to answer patient queries using medical documents stored in OCI Object Storage. They use OCI Generative AI service with Cohere Command R+ model and OCI OpenSearch as the vector database. The chatbot is deployed on OCI Compute with a Flask application. After deployment, the latency for each query is 15-20 seconds, which is unacceptable. Logs show that the embedding generation step (using OCI Generative AI embedding API) takes 8-10 seconds, and the vector search in OpenSearch takes 5-7 seconds. The team has already enabled connection pooling and increased the compute instance shape to the maximum allowed. Which action would MOST effectively reduce the overall latency?

Accepted Answer

Pre-generate embeddings for all documents during ingestion and store them in the vector database, so at query time only the query embedding is generated and compared.. The primary bottleneck is the embedding generation step (8-10 seconds). By pre-generating embeddings for all documents during ingestion and storing them in the vector database, the query-time embedding generation is eliminated, reducing the per-query latency to only the time needed to generate the query embedding and perform the vector search. This directly addresses the largest contributor to the 15-20 second latency.

Answer

Implement a caching layer with Redis to store previous query results and serve cached responses for identical queries.

Answer

Reindex the OpenSearch vector index with optimal settings (e.g., HNSW algorithm, ef_search param) to speed up vector search.

Answer

Switch to a faster embedding model like Cohere Embed v3 (English) which has lower latency.

Question 13

Which two factors are essential for calculating the cost of using OCI Generative AI for text generation? (Choose two.)

Accepted Answer

Number of input tokens.. The cost of using OCI Generative AI for text generation is primarily determined by the number of input tokens (the prompt you send) and the number of output tokens (the generated response). OCI charges per token processed, making these two factors essential for cost calculation.

Answer

Model architecture (encoder-only vs decoder-only).

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Number of API calls per minute.

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Temperature setting.

Question 14

A financial institution needs to deploy a fine-tuned model on OCI with strict data residency requirements. They must ensure that data used for inference never leaves a specific OCI region. The model is stored in Object Storage in the same region. What additional configuration is needed?

Accepted Answer

Configure the dedicated AI cluster to use a private endpoint and restrict access to the region. Configuring the dedicated AI cluster to use a private endpoint ensures that inference traffic stays within the OCI region and never traverses the public internet. This satisfies the strict data residency requirement by keeping all data and model inference within the designated region, while the model stored in Object Storage in the same region is accessed via the private endpoint without leaving the region.

Answer

Use OCI Data Transfer service to move data

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Set up a VPN connection to on-premises

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Enable cross-region replication on the bucket

Question 15

Which TWO of the following are common applications of large language models in enterprise settings?

Accepted Answer

Summarizing lengthy legal documents.. Large language models (LLMs) excel at abstractive summarization, which involves condensing lengthy legal documents into concise summaries while preserving key facts and legal reasoning. This is a common enterprise application for legal departments, as LLMs can process large volumes of text and generate coherent, context-aware summaries without requiring manual reading.

Answer

Performing real-time signal processing for audio streams.

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Replacing relational databases for data storage.

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Enhancing low-resolution images through super-resolution.