1Z0-1127-25 Practice Test 29 — 15 Questions

Question 1

A data scientist is building a RAG pipeline on OCI. Which TWO components are essential for the retrieval step?

Accepted Answer

Embedding model to convert chunks into vectors. Chunking splits documents into manageable pieces, and embedding converts them into vectors for similarity search.

Answer

Fine-tuned generation model

Answer

Beam search decoder

Answer

Human feedback loop

Question 2

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions by retrieving relevant chunks from the policy documents stored in a vector store at inference time, without requiring model retraining. This decouples the knowledge base from the model weights, enabling monthly document updates by simply re-indexing the vector store, which is far more cost-effective and faster than fine-tuning or retraining.

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Train a custom model from scratch on the policy documents each month

Question 3

Refer to the exhibit. A developer has set this policy to allow an OCI Data Science session to generate embeddings. However, the API call returns a 403 Forbidden. Which of the following is likely missing?

Accepted Answer

The dynamic group does not include the Data Science session. The 403 Forbidden error indicates that the Data Science session does not have the necessary permissions to call the API. In OCI, policies grant permissions to dynamic groups, not directly to resources. The dynamic group must include the Data Science session (e.g., by matching the session's OCID or resource type) for the policy to apply. Without this, the session is not recognized as a principal, and the API call is denied.

Answer

The policy needs a 'where request.region != ...' condition

Answer

The policy should include 'in tenancy' instead of compartment

Answer

The service requires 'manage' permission instead of 'use'

Question 4

Which TWO are required components to implement a basic RAG system using OCI services? (Choose two.)

Accepted Answer

OCI Search with OpenSearch. A RAG system needs a way to parse documents into chunks (OCI Document Understanding) and a vector store to index and search embeddings (OCI Search with OpenSearch).

Answer

OCI Object Storage

Answer

OCI Functions

Answer

OCI Data Flow

Question 5

A DBA has created the above vector index. After running queries, they observe that recall is lower than expected for approximate searches. Which change would most likely improve recall while maintaining query performance?

Accepted Answer

Increase the TARGET ACCURACY value to 99.. Increasing TARGET ACCURACY to 99 directly raises the recall threshold for approximate search, forcing the vector index to retrieve more candidates during the search phase. This improves recall without changing the index structure or query parallelism, so query performance (latency) is only minimally impacted compared to switching index types or drastically altering neighbor partitions.

Answer

Change the index type from IVF to HNSW.

Answer

Increase the number of neighbor partitions (NEIGHBOR PARTITIONS) to 8.

Answer

Reduce the number of neighbor partitions to 2.

Question 6

You are a data scientist at a legal firm. The firm uses OCR to digitize court documents and then indexes them in OCI OpenSearch for a RAG application. The application uses OCI Generative AI Service (Cohere Command) to answer questions about case law. Recently, the team noticed that the answers are often factually incorrect or include information not present in the retrieved documents. After reviewing the pipeline, you find that the chunking strategy splits documents into 512-token chunks with 128-token overlap. The embedding model is Cohere Embed v3 (English), and the retrieval returns the top 5 chunks. The LLM has a context window of 4096 tokens. The team suspects that the chunking strategy is causing loss of context. What is the best course of action to improve answer accuracy?

Accepted Answer

Increase the chunk size to 1024 tokens and overlap to 256 tokens.. Increasing the chunk size to 1024 tokens and overlap to 256 tokens directly addresses the loss of context by ensuring each chunk contains more complete semantic units (e.g., entire paragraphs or legal arguments) while the larger overlap preserves continuity across chunk boundaries. This improves the quality of the embeddings and the relevance of retrieved chunks, leading to more factually accurate answers from the LLM.

Answer

Reduce the chunk overlap to 64 tokens to avoid redundancy.

Answer

Switch to a smaller LLM with a larger context window.

Answer

Increase the number of retrieved chunks from 5 to 10.

Question 7

A data scientist needs to create vector embeddings for a multilingual customer feedback dataset to perform clustering analysis. Which OCI Generative AI embedding model should they choose?

Accepted Answer

Cohere embed-multilingual-v3.0. The embed-multilingual-v3.0 model is designed for multilingual content, making it the best choice for a multilingual dataset. The other options are either English-only or not embedding models.

Answer

Cohere Command R+

Answer

Cohere embed-english-v3.0

Answer

Meta Llama 3 70B

Question 8

A company has deployed a generative AI endpoint using a custom fine-tuned model. They observe that the endpoint is returning 429 (Too Many Requests) errors during business hours. They need to handle this without losing requests. What should they implement?

Accepted Answer

Implement client-side retry with exponential backoff.. Implementing client-side retry with exponential backoff is the standard approach to handle HTTP 429 (Too Many Requests) errors without losing requests. When the OCI Generative AI endpoint returns a 429 status, the client can automatically retry the request after a delay that increases exponentially, reducing the load on the endpoint while ensuring all requests are eventually processed. This pattern is recommended by OCI and follows best practices for rate-limited APIs, as it allows the system to recover from transient capacity issues without manual intervention.

Answer

Increase the endpoint's max tokens limit.

Answer

Reduce the number of concurrent requests from the application.

Answer

Use a dedicated AI cluster with higher capacity.

Question 9

Your team has deployed a fine-tuned GPT-2 model on OCI Model Deployment for a simple text generation API. The model performs text completion for short prompts (e.g., 50 tokens). The endpoint is working but response times are over 10 seconds for these short prompts. The model size is approximately 500MB and you used a VM.Standard.E3.Flex shape (2 OCPU, 16GB RAM). The deployment is in a single replica with no autoscaling. You have verified that the network latency is minimal (<5ms). The model was trained in OCI Data Science using a GPU shape, but during deployment you selected a CPU shape to reduce cost. The model is a transformer-based neural network. You've also confirmed that the deployment is healthy and there are no errors in the logs. The memory usage is within limits. What is the most likely cause of the high latency?

Accepted Answer

Missing GPU acceleration for inference. GPT-2 is a transformer-based neural network that relies heavily on matrix multiplications, which are far more efficiently executed on GPUs due to their parallel architecture. Even though the model is only 500MB, CPU inference for transformer models is notoriously slow because CPUs process sequential operations, while GPUs can parallelize the attention mechanism and feed-forward layers. The 10-second latency for a 50-token prompt is a classic symptom of missing GPU acceleration, as the CPU shape (2 OCPU) lacks the specialized tensor cores needed for fast transformer inference.

Answer

High network latency between the client and the model endpoint

Answer

Model is too large for the VM.Standard.E3.Flex shape

Answer

Insufficient CPU resources for the model size

Question 10

A healthcare organization plans to deploy a RAG application on OCI that handles sensitive patient data. They require that all LLM inference and embedding processing happen within a controlled environment to avoid data leakage to public endpoints. Which OCI feature should they use?

Accepted Answer

OCI Dedicated AI Cluster. OCI Dedicated AI Cluster provides a fully isolated, single-tenant infrastructure for running AI workloads, including LLM inference and embedding processing. This ensures all data processing occurs within a controlled environment without traversing public endpoints, meeting the healthcare organization's requirement to prevent data leakage of sensitive patient data.

Answer

OCI Data Labeling

Answer

OCI Vault

Answer

OCI Data Masking

Question 11

An OCI CLI command above returns embeddings for the phrase 'Hello world'. The developer notices that the embedding vector length is 384 dimensions. However, they expected 768 dimensions. What is the most likely cause?

Accepted Answer

The model 'cohere.embed-multilingual-light-v3.0' outputs 384-dimensional vectors.. The Cohere model 'cohere.embed-multilingual-light-v3.0' is specifically designed to output 384-dimensional embeddings. The developer's expectation of 768 dimensions likely stems from familiarity with larger models like 'cohere.embed-english-v3.0', which outputs 1024 dimensions, or other models that produce 768-dimensional vectors. The embedding dimension is a fixed property of the model, not influenced by input length or CLI display settings.

Answer

The input text 'Hello world' is too short, causing dimension reduction.

Answer

The CLI result is truncated in the display.

Answer

The --truncate END flag reduces the dimension.

Question 12

A user sends an inference request with the JSON parameters shown. They notice the model is returning very short responses. What is the most likely cause?

Accepted Answer

temperature is set too low. A low temperature value (close to 0) makes the model highly deterministic, reducing randomness and often leading to shorter, more conservative responses. In generative AI, temperature controls the probability distribution over tokens; lower values cause the model to favor the most likely tokens, which can result in repetitive or truncated outputs. The user's inference request likely includes a temperature setting that is too low, causing the model to produce very short responses.

Answer

maxTokens is set too high

Answer

topP is set too high

Answer

The modelId is incorrect

Question 13

A developer is using Oracle AI Vector Search with LangChain to build a RAG system on top of Oracle Database 23ai. They have created a VECTOR column and built an HNSW index. To improve recall at the cost of some accuracy, which index parameter should they adjust?

Accepted Answer

Increase the 'neighbors' parameter. Increasing the 'neighbors' parameter (M) in an HNSW index expands the number of bidirectional links per node during graph construction. This denser graph provides more alternative paths during search, improving recall because the search is less likely to miss relevant vectors, but it also increases memory usage and can slightly degrade search speed due to more edges to traverse.

Answer

Increase the chunk_overlap in the text splitter

Answer

Decrease the 'efConstruction' parameter

Answer

Switch from HNSW to IVF index type

Question 14

A practitioner needs to choose a pre-trained model for a sentiment analysis task on customer reviews. The model must be efficient for inference and capable of handling multiple languages. Which architecture is MOST suitable?

Accepted Answer

Mixture of Experts model. Mixture of Experts (MoE) models are designed for efficient inference by activating only a subset of parameters per input, reducing computational cost. They can also support multiple languages by allocating different experts to different language patterns, making them highly suitable for multilingual sentiment analysis. In contrast, encoder-only BERT models are efficient but may not scale as well for multilingual tasks without large capacity, encoder-decoder models are optimized for sequence-to-sequence tasks, and decoder-only models are primarily generative and less efficient for classification.

Answer

Encoder-only BERT model

Answer

Encoder-decoder T5 model

Answer

Decoder-only GPT model

Question 15

A financial services firm needs to ensure that only members of the 'DataScientists' group can use OCI Generative AI resources in the 'prod' compartment. Which IAM policy statement should be applied?

Accepted Answer

Allow group DataScientists to manage genai-family in compartment prod. The 'manage' verb on the 'genai-family' resource type grants full access (including use) to OCI Generative AI resources, and scoping the policy to 'compartment prod' restricts the permission to only that compartment. The requirement is to allow the 'DataScientists' group to use (which implies manage-level access for creating and running inference jobs) Generative AI resources specifically in the 'prod' compartment, not the entire tenancy.

Answer

Allow group DataScientists to manage genai-family in tenancy

Answer

Allow group DataScientists to use ai-services in compartment prod

Answer

Allow group DataScientists to read genai-family in compartment prod