1Z0-1127-25 Practice Test 41 — 15 Questions

Question 1

A developer is building a RAG chatbot for an internal knowledge base. To ensure the system retrieves the most relevant chunks, what is the best practice for chunking?

Accepted Answer

Use semantic chunking with overlap. Semantic chunking with overlap ensures that each chunk preserves coherent meaning by splitting text at natural boundaries (e.g., sentences or paragraphs) rather than arbitrary token counts. The overlap allows the retriever to capture context that spans chunk boundaries, which is critical for RAG systems to retrieve the most relevant chunks for the LLM to generate accurate answers.

Answer

Use very small chunks

Answer

Use fixed-size chunks without overlap

Answer

Use random-sized chunks

Question 2

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

Accepted Answer

Encoder-only. BERT (Bidirectional Encoder Representations from Transformers) uses a Transformer encoder-only architecture. It processes the entire input sequence bidirectionally using self-attention, which is why it excels at natural language understanding tasks such as classification, named entity recognition, and question answering. There is no decoder because BERT does not generate text autoregressively.

Answer

Recurrent neural network

Answer

Encoder-decoder

Answer

Decoder-only

Question 3

A company has a large dataset of legal documents in multiple languages. They need to find documents semantically similar to a query. Which step is essential for this task?

Accepted Answer

Use a text embedding model to convert documents into dense vector representations. Semantic similarity search requires converting documents and the query into dense vector embeddings using a text embedding model, then comparing them via cosine similarity or similar metrics. This is the essential step that enables meaning-based matching across languages.

Answer

Apply BPE tokenization to all documents

Answer

Fine-tune a generation model on the legal documents

Answer

Use beam search to identify similar passages

Question 4

A large enterprise is deploying a generative AI model for internal document summarization. The model is deployed on OCI Data Science using a custom container. The inference endpoint is behind a public load balancer. The security team requires that all traffic between the client and the endpoint be encrypted in transit and that the endpoint not be accessible from the public internet. The current setup uses a public load balancer with an SSL certificate. The VCN has a public subnet for the load balancer and a private subnet for the model deployment. The security team is concerned that the load balancer is publicly accessible. The enterprise wants to maintain high availability and low latency. What should the architect do to meet the security requirements?

Accepted Answer

Replace the public load balancer with a private load balancer in a private subnet, and attach an SSL certificate for encryption.. Replacing the public load balancer with a private load balancer in a private subnet ensures the endpoint is not accessible from the public internet, while attaching an SSL certificate maintains encryption in transit. This satisfies both security requirements without sacrificing high availability or low latency, as the private load balancer still provides load balancing and TLS termination within the VCN.

Answer

Use a site-to-site VPN to connect clients to the VCN and access the endpoint via private IP.

Answer

Remove the load balancer and use a service gateway to access the model deployment directly from the VCN.

Answer

Keep the public load balancer but add a Web Application Firewall (WAF) to block unauthorized IPs.

Question 5

A developer is building a LangChain-powered application that must maintain conversation history across multiple turns. They want to store the chat history in Oracle Database. Which memory type and persistence approach should they use?

Accepted Answer

ConversationBufferMemory with a custom chat message history class backed by Oracle Database. The requirement explicitly states storing chat history in Oracle Database. ConversationBufferMemory stores the full conversation history without summarization or windowing, and by implementing a custom chat message history class backed by Oracle Database, the developer can persist the conversation history directly in Oracle, meeting both the memory type and persistence requirements.

Answer

ConversationSummaryMemory with in-memory storage

Answer

ConversationBufferWindowMemory with Redis persistence

Answer

ConversationKGMemory with file-based storage

Question 6

Which TWO of the following are benefits of using OCI Generative AI service compared to self-hosting an LLM?

Accepted Answer

Built-in content safety filters. Option C is correct because OCI Generative AI provides built-in content moderation and safety filters (guardrails) that screen prompts and responses for harmful or inappropriate content, which a self-hosted LLM would require you to implement and maintain yourself. Option D is correct because the managed service automatically scales inference capacity to handle varying request volumes without you provisioning or managing GPU infrastructure, whereas self-hosting requires manual capacity planning and scaling. Option A is not necessarily true: latency depends on network path, region, and model, and self-hosting can sometimes yield lower latency when the model runs close to the application. Option B is incorrect because data egress costs depend on where the service is accessed from and are not inherently eliminated by using the managed service. Option E is incorrect because OCI Generative AI offers pre-trained and fine-tunable models but does not give you full control over the underlying model weights, which self-hosting would provide.

Answer

Lower latency always

Answer

No data egress costs

Answer

Full control over model weights

Question 7

A developer is using LangChain's ChatPromptTemplate to construct a prompt for a conversational agent. The prompt should include a system message, a placeholder for conversation history, and the latest user query. Which TWO components should they include in the template?

Accepted Answer

SystemMessage. Option B (SystemMessage) is correct because a system message sets the agent's behavior, role, and instructions, which the scenario explicitly requires as part of the prompt. Option C (MessagesPlaceholder with variable name 'history') is correct because MessagesPlaceholder is the LangChain component designed to inject a dynamic list of prior messages (the conversation history) into a ChatPromptTemplate at runtime. The remaining options are not required components here: AIMessage (A) represents a prior model response and would only appear inside the history list, not as a standalone template element, and HumanMessage (D) represents a user turn, which is typically supplied at invocation time or included within the history placeholder rather than being a distinct required template component.

Answer

AIMessage

Answer

HumanMessage

Question 8

A company is building a RAG application for customer support. The knowledge base includes documents in English, Spanish, and French. Which embedding model should they use from OCI Generative AI to ensure accurate retrieval across all languages?

Accepted Answer

cohere.embed-multilingual-light-v3.0. The cohere.embed-multilingual-light-v3.0 model is specifically designed to handle multiple languages, including English, Spanish, and French, ensuring accurate semantic retrieval across all three languages in the RAG application. OCI Generative AI offers this multilingual embedding model to support diverse knowledge bases, whereas single-language models would fail to represent non-English text effectively.

Answer

cohere.generate-english-v2:0

Answer

cohere.embed-english-light-v3.0

Answer

cohere.command-r-plus-v1:0

Question 9

What is the role of the softmax function in the output layer of an LLM?

Accepted Answer

Convert logits to probabilities. The softmax function in the output layer of an LLM converts the raw, unnormalized scores (logits) produced by the final linear layer into a probability distribution over the vocabulary. This allows the model to output a valid probability for each token, where all probabilities sum to 1, enabling sampling or greedy decoding for next-token prediction.

Answer

Apply attention

Answer

Tokenize input

Answer

Compute gradients

Question 10

A company needs to ensure that only authorized users can invoke an endpoint for a generative AI model. Which OCI feature should be used to control access?

Accepted Answer

OCI Identity and Access Management (IAM) policies. OCI Identity and Access Management (IAM) policies are the correct choice because they define who (users, groups, or service principals) can invoke which OCI resources, including generative AI model endpoints. IAM policies use resource-type and verb-based statements (e.g., 'allow group A to manage ai-service-family in compartment X') to enforce authorization at the API level, ensuring only authorized principals can call the model's inference endpoint.

Answer

Network security groups (NSGs)

Answer

VCN flow logs

Answer

OCI Web Application Firewall (WAF)

Question 11

A data scientist needs to generate vector embeddings for a large corpus of text documents to use in a semantic search application. Which OCI service is best suited for this task?

Accepted Answer

OCI Generative AI. OCI Generative AI is the correct choice because it provides a managed service for generating vector embeddings from text using large language models (LLMs) like Cohere. This service is specifically designed for tasks such as semantic search, where embeddings capture the meaning of text to enable similarity comparisons. OCI Vision, Speech, and Language focus on other modalities (images, audio, and NLP tasks like sentiment analysis) and do not offer embedding generation for semantic search.

Answer

OCI Vision

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OCI Speech

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OCI Language

Question 12

A company needs to integrate OCI Generative AI Service with an existing application that uses OCI IAM for authentication. They want to use resource principal to allow the application to call the service without storing API keys. Which step is REQUIRED?

Accepted Answer

Create a dynamic group and a policy granting access to the Generative AI Service. Resource principal authentication in OCI requires the application to be represented by a dynamic group, which matches instances or resources based on defined rules. A policy must then grant that dynamic group access to the Generative AI Service. This avoids storing API keys by using OCI IAM's built-in resource principal token exchange.

Answer

Create an OCI API key for the application

Answer

Enable the Generative AI Service for resource principal in the tenancy

Answer

Assign the application to a group with admin privileges

Question 13

An LLM-based application must comply with data privacy regulations by not memorizing personally identifiable information (PII). Which technique best reduces memorization of PII?

Accepted Answer

Train with differential privacy. Differential privacy (DP) is the correct technique because it directly limits the model's ability to memorize training data, including PII, by adding calibrated noise to the gradient updates during training. This ensures that the model's parameters do not encode specific individual records, providing a formal mathematical guarantee against memorization. Other options like model size, temperature, or training epochs do not address the root cause of memorization in the training process.

Answer

Use a larger model with more parameters

Answer

Decrease the temperature during inference

Answer

Increase the number of training epochs

Question 14

You are a machine learning engineer at a large e-commerce company. You have been tasked with deploying a large language model to power a customer service chatbot that handles product returns and refunds. The model will answer customer queries based on a knowledge base of return policies and FAQs. The company has strict requirements: (1) responses must be factually accurate and grounded in the knowledge base, (2) the system must be cost-effective, and (3) latency should be under 2 seconds per response. You decide to use a pre-trained LLM from OCI Data Science and implement retrieval-augmented generation (RAG). You have two options for the retriever: a dense embedding-based retriever (e.g., using OCI AI Language embeddings) or a sparse keyword-based retriever (e.g., BM25). You also need to decide on the generation model size: a 7B parameter model or a 70B parameter model. You run a pilot test: with the dense retriever + 7B model, average latency is 1.8 seconds and accuracy is 85%. With the sparse retriever + 7B model, latency is 1.2 seconds but accuracy drops to 75%. With the 70B model (any retriever), latency exceeds 5 seconds. Which combination should you choose to meet all requirements?

Accepted Answer

Dense retriever + 7B model.. (dense retriever + 7B model) is correct because it meets all three requirements: factual accuracy (85% accuracy from dense retrieval grounding), latency under 2 seconds (1.8 seconds), and cost-effectiveness (7B model is cheaper to run than 70B). The dense retriever provides better semantic matching for nuanced return policy queries, while the 7B model keeps inference fast and affordable.

Answer

Sparse retriever + 70B model.

Answer

Dense retriever + 70B model.

Answer

Sparse retriever + 7B model.

Question 15

A team is fine-tuning an LLM on OCI Generative AI for a domain-specific task. They have a dataset of 10,000 labeled examples. What is a best practice to avoid catastrophic forgetting during fine-tuning?

Accepted Answer

Include a small percentage of general-domain data in the training mix.. Catastrophic forgetting occurs when a fine-tuned model loses previously learned general knowledge. By including a small percentage (e.g., 5–10%) of general-domain data in the training mix, the model retains its broad capabilities while adapting to the new domain-specific task. This technique, often called 'replay' or 'experience replay,' is a standard practice in continual learning for LLMs.

Answer

Increase the learning rate to speed up adaptation.

Answer

Use only the new domain-specific data for fine-tuning.

Answer

Reduce the number of training epochs to the minimum.