1Z0-1127-25 Practice Exam 52 — 20 Questions

Question 1

Your organization uses OCI Data Science to train a generative AI model for code generation. After training, you want to deploy it as a REST API. You create a model deployment using the OCI console, but after 30 minutes the deployment status is still 'Creating'. You check the logs and see the message: 'Insufficient capacity for shape VM.GPU.A10.1 in availability domain AD-1'. The deployment is configured with a single replica. You have verified your tenancy has sufficient service limits for GPU instances. What should you do to resolve this issue quickly?

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

Accepted Answer

Change the deployment to use a different GPU shape, such as VM.GPU.A10.2. The error indicates that the specific GPU shape VM.GPU.A10.1 lacks capacity in the current availability domain. Switching to a different GPU shape, such as VM.GPU.A10.2, which uses a different instance configuration, can bypass the capacity constraint without requiring a region change or service limit increase. This is the fastest resolution because it directly addresses the availability domain capacity issue while keeping the deployment in the same region and AD.

Answer

Delete the deployment and create it in a different region with more GPU capacity

Answer

Request a service limit increase for GPU shapes

Answer

Wait for 1 hour and check again; capacity may become available

Question 2

A researcher wants to compare the performance of two LLMs on OCI Generative AI: a base model and an instruct model. They notice the instruct model often refuses to generate certain types of content. Which factor most likely explains this behavior?

Accepted Answer

The instruct model has been fine-tuned with reinforcement learning from human feedback (RLHF) to align with safety guidelines.. Instruct models are typically fine-tuned using reinforcement learning from human feedback (RLHF) to align with safety guidelines and ethical constraints. This fine-tuning process teaches the model to refuse generating harmful, biased, or unsafe content, which explains why the instruct model refuses certain types of content while the base model does not.

Answer

The base model was programmed to follow stricter rules.

Answer

The instruct model was trained on a smaller dataset.

Answer

The base model rejects content more often.

Question 3

An AI engineer is testing a large language model on OCI Generative AI and receives this error: 'Token limit exceeded. Maximum context length is 4096 tokens.' The prompt is 4000 tokens long. What is the most effective way to resolve the issue without losing important context?

Accepted Answer

Reduce the prompt length by summarizing or trimming less relevant information.. The error indicates that the combined prompt and generated output exceed the model's maximum context length of 4096 tokens. Since the prompt alone is 4000 tokens, there is very little room for the model to generate a response. Trimming or summarizing less relevant parts of the prompt directly reduces the token count, allowing the model to produce a complete output without exceeding the limit. This approach preserves the most critical context while staying within the model's constraints.

Answer

Switch to a model with a larger context window, if available.

Answer

Increase the max_tokens parameter in the API call.

Answer

Split the prompt into multiple requests and combine outputs.

Question 4

An administrator notices that a dedicated AI cluster is not scaling down after a period of low traffic. What could be the cause?

Accepted Answer

The cluster has a minimum size set to the current number of nodes. A dedicated AI cluster in OCI has a minimum size configuration that prevents the autoscaler from reducing the node count below that threshold. If the current number of nodes equals the configured minimum, the cluster will not scale down even during low traffic, as the autoscaler respects this lower bound. This ensures baseline capacity is always available for inference workloads.

Answer

There are pending inference requests

Answer

The cluster is in a compartment without permissions

Answer

The autoscaling policy uses a cooldown period that is too short

Question 5

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. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

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

Answer

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

Answer

Fine-tune a base LLM on the policy documents monthly

Question 6

A company is using OCI Generative AI Agents to implement a RAG system for employee onboarding. They want to ensure the agent only answers from the uploaded documents and avoids making up information. Which THREE configuration steps should they take?

Accepted Answer

Configure the agent to use only the knowledge base and disable internet search. To ground the agent in provided documents, they should use a knowledge base, set a preamble to restrict knowledge, and disable internet search. Fine-tuning is not needed.

Answer

Increase the temperature to 2.0 for more creative responses

Answer

Fine-tune the underlying model on the onboarding documents

Question 7

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

Answer

OCI Speech

Answer

OCI Language

Question 8

A data scientist is designing a prompt to extract structured information (e.g., JSON) from text using an instruct model on OCI Generative AI. The model sometimes outputs additional text beyond the JSON, breaking parsing. Which prompt engineering technique is most effective to enforce structured output?

Accepted Answer

Include a few-shot example of the expected JSON output in the prompt.. Few-shot prompting provides explicit examples of the desired output format, which instructs the model to follow the exact JSON structure and reduces the likelihood of extraneous text. This technique leverages the model's in-context learning ability to adhere to formatting constraints, making it the most effective for enforcing structured output in OCI Generative AI instruct models.

Answer

Use a base model instead of an instruct model.

Answer

Set the temperature to 0.0 to reduce randomness.

Answer

Increase max_tokens to allow for additional output.

Question 9

A RAG application is hallucinating because the LLM receives irrelevant context from the retrieval step, even when topK is set to 3. Which strategy would best reduce hallucination by improving the relevance of retrieved documents?

Accepted Answer

Add a reranking step after retrieval to select the most relevant chunks. Adding a reranking step after retrieval directly addresses the core issue: even with a low topK, the initial retrieval may return chunks that are semantically similar but not precisely relevant to the query. Reranking uses a cross-encoder model to score each retrieved chunk against the query, reordering them so that only the most contextually relevant chunks are passed to the LLM. This reduces the chance of the LLM receiving irrelevant context, thereby minimizing hallucination.

Answer

Reduce the chunk size to one sentence per chunk

Answer

Implement a query rewriting mechanism

Answer

Increase topK to 10 to provide more context

Question 10

A user wants to invoke an OCI Generative AI endpoint from a cloud function. What is the required authentication method?

Accepted Answer

API signing key. OCI Generative AI endpoints require API signing keys for authentication because they are REST APIs that use the Signature Version 1 algorithm (based on HMAC-SHA256) to sign requests. Cloud Functions must include a signed HTTP header using a user's or service principal's OCI API signing key pair (private key for signing, public key uploaded to OCI) to prove identity and authorization. This is the standard method for programmatic access to OCI services, including Generative AI, and is enforced by the OCI Identity and Access Management (IAM) policy layer.

Answer

User name and password

Answer

Session token

Answer

OCI certificate

Question 11

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 12

An organization wants to allow its data science group to use OCI Generative AI services but restrict access to a specific compartment. Which IAM policy statement correctly achieves this?

Accepted Answer

allow group data-scientists to use generative-ai-family in compartment GenAI-Prod. The 'allow group to use generative-ai-family in compartment ' policy grants access to all GenAI resources within that compartment. The other options either miss the compartment scope or use incorrect verbs.

Answer

allow group data-scientists to manage generative-ai-family in tenancy

Answer

allow group data-scientists to read generative-ai-family in compartment GenAI-Prod

Answer

allow group data-scientists to use generative-ai-family where target.compartment.id = 'GenAI-Prod'

Question 13

Which LangChain memory type is best suited for a long-running conversation where token consumption must be minimized, and the gist of previous exchanges should be retained?

Accepted Answer

ConversationSummaryMemory. ConversationSummaryMemory (D) is best suited for long-running conversations where token consumption must be minimized because it periodically summarizes the conversation history, retaining the gist of previous exchanges in a compressed form. This avoids storing every raw message (as in BufferMemory) while still preserving context, making it ideal for cost-sensitive or token-limited LLM deployments.

Answer

ConversationBufferMemory

Answer

VectorStoreMemory

Answer

ConversationBufferWindowMemory

Question 14

A company is deploying a RAG pipeline using OCI Data Science and OCI Generative AI. The pipeline uses a Cohere command model for generation and a Cohere embed model for retrieval. The team notices that the model occasionally produces hallucinated answers that are not supported by the retrieved context. Which strategy is MOST effective at reducing hallucinations?

Accepted Answer

Implement a faithfulness verification step that re-ranks retrieved passages based on alignment with the generated answer.. Implementing a faithfulness verification step that re-ranks retrieved passages based on alignment with the generated answer directly reduces hallucinations by ensuring the generated output is supported by the retrieved context. Option B is wrong because increasing temperature increases randomness and may lead to more hallucinations. Option C is wrong because increasing the number of retrieved chunks (k) can introduce irrelevant or conflicting information, potentially increasing hallucinations. Option D is wrong because using a larger generative model does not inherently improve faithfulness to the retrieved context and adds computational cost.

Answer

Increase the temperature parameter of the generation model.

Answer

Increase the number of retrieved chunks (k) to provide more context.

Answer

Use a larger generative model with more parameters.

Question 15

A developer notices that an LLM-based question-answering system sometimes provides answers that are correct but from an outdated version of the knowledge base. The system uses RAG with a vector database updated daily. What is the MOST likely root cause?

Accepted Answer

The embedding model was not re-run on the updated documents, so the index contains old embeddings. The core issue is that the vector database index still contains old embeddings. Even though the knowledge base documents are updated daily, if the embedding model is not re-run on those updated documents, the vector representations in the index remain stale. When the RAG system retrieves, it fetches these outdated embeddings, leading to correct but outdated answers. This is a classic index synchronization problem in RAG pipelines.

Answer

The retrieval top-k parameter is set too high

Answer

The chunking strategy splits documents into too-small pieces

Answer

The LLM's training data has a knowledge cutoff date

Question 16

An administrator runs the above CLI command to check the status of a dedicated AI cluster. The cluster is ACTIVE with capacity 10. However, a user reports that inference requests to this cluster are failing with a '429 Too Many Requests' error. What is the most likely cause?

Accepted Answer

The cluster is hitting the maximum inference requests per minute limit. The '429 Too Many Requests' error is an HTTP status code indicating rate limiting has been exceeded. In OCI Generative AI, dedicated AI clusters have a configurable 'maximum inference requests per minute' limit. Even if the cluster is ACTIVE and has capacity (e.g., 10 nodes), hitting this per-minute request cap will cause the API gateway to reject further requests with a 429 error. The administrator must increase the rate limit or implement client-side throttling to resolve this.

Answer

The cluster does not have enough nodes to handle the load

Answer

The user is not in the same compartment as the cluster

Answer

The cluster is not in ACTIVE state

Question 17

A developer is building a code generation assistant and needs to ensure the LLM follows a specific output format (e.g., JSON). Which approach is MOST effective for achieving format adherence without retraining?

Accepted Answer

Provide a few-shot example of the desired JSON format in the prompt. Few-shot prompting—providing explicit examples of the desired JSON format in the prompt—directly guides the LLM's output structure without requiring retraining. This technique leverages in-context learning, where the model infers the required schema from the examples, making it the most effective and efficient method for format adherence.

Answer

Lower the temperature to 0 to reduce output variability

Answer

Fine-tune the model on a dataset of JSON code examples

Answer

Increase the context window to include more code context

Question 18

A company uses OCI Generative AI to generate legal document summaries. They have a custom model deployed on a dedicated AI cluster. They want to ensure that the model is not used by unauthorized users. They also need to log all inference requests for auditing. Which combination of OCI services should they use?

Accepted Answer

OCI Identity and Access Management (IAM) policies and OCI Logging.. OCI IAM policies are the primary mechanism for controlling access to OCI resources, including custom models on dedicated AI clusters, by defining which users or groups can invoke the model. OCI Logging captures detailed logs of all inference requests, including metadata such as timestamps, source IPs, and request payloads, which satisfies the auditing requirement. Together, they provide both authorization enforcement and audit trail without additional services.

Answer

OCI Vault for encryption and OCI Audit for logging.

Answer

OCI Data Safe and OCI Monitoring.

Answer

OCI API Gateway with authentication and OCI Audit.

Question 19

An organization wants to combine keyword search and vector search to improve retrieval accuracy in their RAG pipeline. Which OCI service provides built-in hybrid search capabilities?

Accepted Answer

OCI OpenSearch. OCI OpenSearch is the correct answer because it natively supports hybrid search, which combines keyword-based (BM25) and vector-based (k-NN) queries in a single search request. This allows the RAG pipeline to retrieve documents that match both exact terms and semantic meaning, improving overall accuracy without requiring separate search systems.

Answer

OCI Search with AI

Answer

Autonomous Database with AI Vector Search

Answer

OCI Logging

Question 20

A security administrator needs to grant a data science team access to use OCI Generative AI resources (e.g., run inference, create fine-tuning jobs) but only within a specific compartment. What is the correct IAM policy statement?

Accepted Answer

Allow group DataScientists to manage generative-ai-family in compartment Production. The 'allow group to use generative-ai-family in compartment' is the standard OCI IAM policy for granting access to all GenAI resources in a compartment. The other options have incorrect resource types or conditions.

Answer

Allow group DataScientists to manage llm-models in compartment Production

Answer

Allow group DataScientists to use all-resources in tenancy

Answer

Allow group DataScientists to read ai-services in compartment Production