1Z0-1127-25 Practice Test 42 — 15 Questions

Question 1

An administrator created the above IAM policies. A member of the GenerativeAIAdmins group reports they cannot invoke the model endpoint. Which permission is missing?

Accepted Answer

Permission to use or manage generative-ai-endpoint. The error occurs because the IAM policy grants permissions for 'generative-ai-model' but not for 'generative-ai-endpoint'. Invoking a model endpoint requires the 'use' or 'manage' permission on the 'generative-ai-endpoint' resource type, as the endpoint is the runtime interface that handles inference requests. Without this permission, the API call to the endpoint is denied, even if the user has access to the underlying model.

Answer

Permission to access the compartment

Answer

Permission to manage generative-ai-model

Answer

Permission to read the model's training data

Question 2

A company has deployed a generative AI model on OCI to generate product descriptions. After a recent update, the model started producing outputs with repetitive phrases and poor coherence. The inference endpoint is configured with default parameters. Which single parameter adjustment is most likely to improve output quality?

Accepted Answer

Increase the frequency penalty parameter to 0.5. Increasing the frequency penalty reduces the likelihood of the model repeating the same phrases, directly addressing the repetitive outputs. The frequency penalty subtracts a proportional penalty from tokens that have already appeared, discouraging repetition and improving coherence. Default parameters often have no frequency penalty (0.0), so a small positive value like 0.5 can significantly enhance output diversity.

Answer

Increase the max-tokens parameter to 512

Answer

Increase the temperature parameter to 1.5

Answer

Decrease the top-p parameter to 0.8

Question 3

An organization is concerned about the safety of generated content. Which OCI feature allows them to define custom policies to block inappropriate outputs?

Accepted Answer

Content filtering and safety controls in Generative AI. OCI Generative AI includes built-in content filtering and safety controls that allow organizations to define custom policies to block inappropriate or harmful outputs. These controls operate at the model inference layer, enabling fine-grained filtering based on categories such as toxicity, hate speech, or personally identifiable information (PII). This directly addresses the concern about generated content safety.

Answer

OCI IAM policies

Answer

OCI Audit logs

Answer

OCI Vault

Question 4

An organization needs to ensure that all inference requests to OCI Generative AI are logged for compliance. Which OCI feature should be enabled?

Accepted Answer

OCI Logging for the AI service. OCI Logging for the AI service captures detailed request and response data for inference calls to OCI Generative AI, including payloads, timestamps, and user identities. This feature must be explicitly enabled per service endpoint to meet compliance requirements for logging all inference requests. Unlike Audit logs, which record control-plane operations, OCI Logging provides data-plane logging for the AI service itself.

Answer

OCI Cloud Guard

Answer

OCI Vault

Answer

OCI Audit logs

Question 5

During deployment of a generative AI model, the inference endpoint returns high latency and timeouts. The model is deployed on a dedicated AI cluster with multiple nodes. What is the most likely cause?

Accepted Answer

The cluster nodes are configured with insufficient parallelism or the model is not properly parallelized across nodes. High latency and timeouts in a distributed AI inference deployment typically indicate that the model workload is not efficiently distributed across the cluster nodes. Option C is correct because insufficient parallelism—either due to misconfigured node resources (e.g., insufficient vCPUs, GPU cores, or memory bandwidth) or improper model sharding/parallelization—causes some nodes to become bottlenecks while others remain underutilized, leading to queuing delays and eventual timeouts.

Answer

The inference request batch size is too small

Answer

The model is too large for the cluster memory

Answer

The client-side network is slow

Question 6

A data scientist is evaluating an LLM's performance on a summarization task. They observe that the model produces fluent summaries but often misses key information. Which TWO metrics would best capture this issue? (Select TWO.)

Accepted Answer

Human evaluation with a rubric for completeness. ROUGE-L measures recall of the longest common subsequence, capturing information coverage. Human evaluation can assess completeness. BLEU emphasizes precision and fluency. BERTScore measures semantic similarity but not directly the presence of key points. Perplexity measures model confidence, not recall.

Answer

BLEU score

Answer

Perplexity

Answer

BERTScore

Question 7

An administrator needs to grant a data science team access to create and manage generative AI model endpoints in a specific compartment. Which policy should they create?

Accepted Answer

Allow group DataScientists to manage generative-ai-model-family in compartment Production. The verb 'manage' grants full CRUD (Create, Read, Update, Delete) permissions on the 'generative-ai-model-family' resource type, which is the specific resource family for generative AI model endpoints in OCI. This allows the DataScientists group to create and manage endpoints within the specified compartment without granting broader access to all resources.

Answer

Allow group DataScientists to manage all-resources in compartment Production

Answer

Allow group DataScientists to use generative-ai-model-family in compartment Production

Answer

Allow group DataScientists to read generative-ai-model-family in compartment Production

Question 8

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 9

A data scientist is using the OCI Generative AI SDK to create embeddings for a large corpus of legal documents. They want to perform semantic search. Which endpoint should they use?

Accepted Answer

/v1/embed. The /v1/embed endpoint is specifically designed to generate vector embeddings from input text, which are numerical representations that capture semantic meaning. For semantic search over a large corpus of legal documents, embeddings must be created to enable similarity comparisons, making this the correct choice.

Answer

/v1/classify

Answer

/v1/generate

Answer

/v1/chat

Question 10

An OCI GenAI model generates English to French translation. Which metric is most appropriate to evaluate its quality?

Accepted Answer

BLEU. BLEU (Bilingual Evaluation Understudy) is the standard metric for machine translation tasks because it measures the n-gram overlap between the generated translation and one or more reference translations, directly assessing fluency and adequacy. For English-to-French translation, BLEU correlates well with human judgment of translation quality, making it the most appropriate choice.

Answer

Perplexity

Answer

ROUGE

Answer

F1 score

Question 11

A developer receives the above error when querying a RAG application. What is the most likely cause and recommended action?

Accepted Answer

The API rate limit has been exceeded; wait for the retry period and implement exponential backoff.. The 429 (Too Many Requests) error indicates the API rate limit has been exceeded. In OCI Generative AI services, rate limits are enforced per tenancy and per region; the recommended action is to wait for the retry-after period and implement exponential backoff to avoid overwhelming the service.

Answer

The model is deprecated; update to the latest model.

Answer

The endpoint URL is incorrect; verify the OCI region endpoint.

Answer

The request payload is malformed; check the input format.

Question 12

A prompt engineer is developing a multi-step reasoning application using the ReAct pattern. The model sometimes outputs incomplete reasoning or forgets to perform an action. Which improvement to the prompt is MOST likely to address this issue?

Accepted Answer

Include a template pattern in the system prompt that explicitly defines the format for each step. The ReAct pattern requires the model to alternate between Thought, Action, and Observation steps in a strict format. When the model forgets actions or produces incomplete reasoning, the root cause is usually that the prompt does not enforce the output structure. Embedding an explicit template in the system prompt that defines the exact format for each step (e.g., 'Thought: ... Action: ... Action Input: ... Observation: ...') constrains generation and reliably yields complete, parseable steps.

Answer

Add a system message that says 'You are a reasoning agent'

Answer

Use a zero-shot prompt with the instruction 'Reason step by step and then act'

Answer

Increase the temperature to 0.8 to allow more creative reasoning

Question 13

An engineer sets beam search width to 1 during inference on OCI Generative AI. What is the most likely effect on output?

Accepted Answer

Faster inference. Beam search width of 1 is equivalent to greedy decoding, where only the single most probable token is selected at each step. This eliminates the need to maintain and compare multiple candidate sequences, significantly reducing computational overhead and memory access, which directly speeds up inference.

Answer

More memory usage

Answer

More diverse outputs

Answer

Better quality

Question 14

What is the primary purpose of an embedding model in a RAG pipeline?

Accepted Answer

To convert text into numerical vectors.. The primary purpose of an embedding model in a RAG pipeline is to convert text into numerical vectors (embeddings) that capture semantic meaning. These vectors enable the retrieval component to efficiently find relevant documents by measuring similarity (e.g., cosine similarity) between the query and stored document embeddings. Without this conversion, the system cannot perform semantic search over unstructured text.

Answer

To generate human-like responses.

Answer

To rank search results.

Answer

To summarize long documents.

Question 15

A data scientist is designing a RAG system with a large vector database (hundreds of millions of documents) and requires high recall accuracy. Which vector search index type should be used in OCI Search with OpenSearch?

Accepted Answer

HNSW (Hierarchical Navigable Small World). HNSW is the correct choice because it provides the best trade-off between high recall and low latency for large-scale vector search. It builds a multi-layer graph structure that enables efficient approximate nearest neighbor search, achieving recall accuracy close to brute-force while scaling to hundreds of millions of documents.

Answer

LSH (Locality Sensitive Hashing)

Answer

Flat (brute-force)

Answer

IVF (Inverted File Index)