1Z0-1127-25 Practice Test 14 — 15 Questions

Question 1

Which two are essential components of the Transformer architecture? (Select TWO)

Accepted Answer

Self-attention mechanism. The self-attention mechanism (C) is essential because it is the core operation that lets each token attend to every other token in the sequence, computing scaled dot-product attention (softmax(QK^T/√d_k)V) to capture contextual relationships in parallel. The feed-forward neural network (D) is also essential, as each Transformer block applies a position-wise fully connected network (typically two linear layers with a ReLU/GELU activation) after the attention sublayer to add non-linear transformation capacity. Together with residual connections and layer normalization, these two components form the standard encoder/decoder block. Pooling layers (A) are not required—Transformers use attention rather than pooling for aggregation, and pooling appears mainly in CNNs. Recurrent connections (B) belong to RNNs/LSTMs, which Transformers replaced by removing sequential recurrence. Convolutional layers (E) are characteristic of CNNs, not the standard Transformer block.

Answer

Pooling layers

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Recurrent connections

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Convolutional layers

Question 2

A company has fine-tuned a Cohere Command R model using T-Few and wants to deploy it for real-time inference with the lowest possible latency. They have provisioned a dedicated AI cluster with 2 model units. However, latency is still higher than expected. Which action is MOST likely to reduce latency?

Accepted Answer

Increase the number of model units on the dedicated AI cluster. On OCI Generative AI dedicated AI clusters, model units (MUs) determine the compute capacity allocated to host the model. If latency is higher than expected with 2 MUs, the model is likely under-provisioned relative to the request load, so increasing the number of model units adds serving capacity and reduces per-request latency. This is the most direct lever for a latency problem on a dedicated cluster.

Answer

Reduce the temperature parameter to 0

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Switch from dedicated AI cluster to shared infrastructure

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Use a larger base model like Llama 3 70B

Question 3

A developer is using a large language model to generate code snippets. The model often produces code that is syntactically correct but functionally incorrect. What is the most effective way to improve the functional correctness of the generated code?

Accepted Answer

Provide few-shot examples of correct code in the prompt.. Providing few-shot examples of correct code in the prompt directly demonstrates the desired functional behavior to the model. This technique, known as few-shot prompting, grounds the model's output in concrete examples, significantly improving the likelihood that the generated code will be functionally correct by aligning the model's pattern completion with the intended logic, not just syntax.

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Increase the temperature parameter to generate more creative solutions.

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Ask the model to only output syntactically valid code.

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Set max_tokens to a very high value to allow the model more room to think.

Question 4

A company has deployed a generative AI model endpoint on OCI. They want to monitor token usage and latency for cost optimization. Which OCI service should they use to collect these metrics?

Accepted Answer

OCI Monitoring. A is correct because OCI Monitoring is the native telemetry service that collects and stores metrics such as token usage (e.g., input/output token counts) and latency (e.g., model inference latency) from OCI Generative AI endpoints. These metrics are automatically emitted by the OCI Generative AI service and can be queried via the Monitoring API or visualized in the Console, enabling cost optimization by tracking consumption patterns.

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OCI Events

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OCI Notifications

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OCI Logging

Question 5

Which TWO OCI Generative AI features are available in the Playground for testing models?

Accepted Answer

Adjusting parameters like temperature and max tokens. Option A is correct because the OCI Generative AI Playground exposes inference parameters such as temperature, top-p, top-k, and max tokens, allowing you to tune the model's randomness and response length while testing a model. Option B is correct because the Playground lets you define a system prompt (preamble) and override it to control the model's role, tone, and behavior during interactive testing. Option C is not part of the Playground, since provisioning a dedicated AI cluster is a capacity/infrastructure action performed in the OCI Generative AI service outside the interactive test UI. Option D is not available in the Playground, as training metrics belong to the fine-tuning/custom model workflow rather than the inference testing interface. Option E is not a Playground feature, because submitting a fine-tuning job is a separate, longer-running operation initiated from the fine-tuning section of the service.

Answer

Provisioning a dedicated AI cluster

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Viewing model training metrics

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Submitting a fine-tuning job

Question 6

A developer receives a 403 error when calling the OCI GenAI API from a function. They have set up policies for the function's dynamic group. What is the most likely cause?

Accepted Answer

Missing IAM policy for GenAI service.. A 403 error when calling the OCI GenAI API from a function indicates an authorization failure. Even if the function's dynamic group is correctly configured, the IAM policy must explicitly grant the dynamic group permission to invoke the GenAI service. Without a policy statement like 'Allow dynamic-group [name] to use generative-ai-family in compartment [name]', the API call is denied, resulting in a 403 Forbidden.

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The request body format is incorrect.

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The model is not available in the region.

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The API key is invalid.

Question 7

A customer support company uses Cohere Command on OCI to answer user queries. They have enabled grounding with a knowledge base of product manuals. However, for about 20% of queries, the model provides incorrect product recommendations that are not in the manuals. The team has verified the knowledge base is up to date. What is the most likely cause and solution?

Accepted Answer

The query phrasing may not match the knowledge base; improve the retrieval system or use query rewriting.. Grounding with a knowledge base works by retrieving relevant documents based on the user's query, then conditioning the model's response on those documents. If the query phrasing does not semantically match the content in the manuals (e.g., using synonyms, different terminology, or vague language), the retrieval step may fail to fetch the correct passages. The model then generates an answer without proper grounding, leading to incorrect recommendations. Improving the retrieval system (e.g., using better embeddings, hybrid search) or applying query rewriting to align the query with the knowledge base can fix this.

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The model's temperature is too high, causing creative responses. Lower temperature to 0.

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The model is hallucinating; switch to a larger model.

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The grounding settings are too restrictive; increase the number of retrieved documents.

Question 8

A company notices that some inference requests to their deployed model on OCI Generative AI take longer than acceptable. They want to reduce per-request latency. What should they do?

Accepted Answer

Reduce the maximum number of tokens generated. Reducing the maximum number of tokens generated directly decreases the amount of computation required per inference request because the model stops generating output earlier. Since latency is proportional to the number of output tokens produced, this is the most effective single change to reduce per-request response time in OCI Generative AI deployments.

Answer

Enable request batching

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Use a larger model to improve accuracy

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Increase the number of replicas in the deployment

Question 9

A user wants to access the OCI Generative AI service programmatically. Which credential method is recommended for use in a production application running on OCI Compute?

Accepted Answer

Instance principal. Instance principal authentication is the recommended method for production applications running on OCI Compute because it allows the application to authenticate with OCI services without managing or embedding any credentials. The OCI Compute instance assumes a dynamic group and IAM policy that grants it permissions, and the SDK automatically handles token exchange via the instance metadata service, eliminating the need for long-lived secrets.

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API signing keys

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User password and OCID

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Resource principal

Question 10

Which two actions are required when deploying a custom fine-tuned model using the OCI Generative AI service? (Choose two.)

Accepted Answer

Upload model artifacts to OCI Object Storage. Option D is correct because deploying a custom fine-tuned model in OCI Generative AI requires the model artifacts (weights and configuration) to be stored in an OCI Object Storage bucket, from which the service imports them during creation of the custom model. Option E is correct because a dedicated AI cluster (a hosting cluster sized with the required compute units) must be created to host the custom model, since fine-tuned models are served on dedicated infrastructure rather than the shared on-demand endpoints. Options A and C are incorrect because API Gateway and load balancer configuration are not part of the OCI Generative AI custom model deployment workflow; the service exposes its own endpoint. Option B is incorrect because registering the model in the OCI Data Science Model Catalog is a Data Science workflow, not a requirement for deploying a custom model through the OCI Generative AI service.

Answer

Configure an API Gateway for the model endpoint

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Register the model in the OCI Data Science Model Catalog

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Set up a load balancer for the deployment

Question 11

A developer is using the OCI Generative AI Agents service to build a RAG application. They have uploaded policy PDFs to an OCI Object Storage bucket. What is the next step to make the documents searchable?

Accepted Answer

Create a knowledge base from the Object Storage bucket. In OCI Generative AI Agents, a knowledge base is the construct that ingests documents from a data source such as Object Storage, chunks and embeds them, and stores the vectors in a vector store for semantic retrieval. Creating a knowledge base from the Object Storage bucket is the required next step to make the PDFs searchable. Manual embedding or fine-tuning is not how the Agents service ingests documents.

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Create a dedicated AI cluster to host the documents

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Use the Embeddings API to embed each PDF manually

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Fine-tune a model on the PDFs

Question 12

A company deploys a fine-tuned model on an OCI Generative AI dedicated AI cluster. After deployment, they observe high latency during peak hours. The cluster has only one replica. Which action would most effectively reduce latency without increasing cost unnecessarily?

Accepted Answer

Enable auto-scaling with a maximum of 3 replicas.. Enabling auto-scaling with a maximum of 3 replicas (Option B) is the most effective action because it dynamically adds replicas during peak hours to handle increased load, reducing latency, while limiting the maximum to 3 prevents unnecessary cost overruns. This balances performance and cost, unlike a fixed large replica count or switching models, which either wastes resources or fails to address the root cause of insufficient compute capacity.

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Increase the number of replicas to 10.

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Switch to a larger base model.

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Move to a serverless deployment model.

Question 13

Which OCI Generative AI model family is specifically designed for reranking search results to improve relevance?

Accepted Answer

Cohere Rerank. Cohere Rerank is the OCI Generative AI model family specifically designed for reranking search results to improve relevance. Unlike generation-focused models, Rerank takes a query and a list of candidate documents, scoring each for relevance to the query, thereby enhancing the quality of retrieved results in RAG pipelines.

Answer

Cohere Command R

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Cohere Command R+

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Meta Llama 3

Question 14

A company is deploying a large language model in a customer-facing chatbot. The model's responses must be both accurate and safe. Which combination of techniques should be employed?

Accepted Answer

Use retrieval-augmented generation (RAG) for factual accuracy and a content safety filter for safe outputs.. RAG grounds the model's responses in a verified external knowledge base, reducing hallucinations and improving factual accuracy, while a content safety filter (e.g., a classifier or guardrail) actively blocks toxic or unsafe outputs before they reach the user. This combination addresses both accuracy and safety independently, unlike a single system prompt which is easily bypassed.

Answer

Use only a system prompt instructing the model to be accurate and safe.

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Use a high temperature for creativity and a safety classifier for blocking toxic outputs.

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Fine-tune the model on all historical chat logs and use a high temperature.

Question 15

A developer uses OCI Generative AI's chat endpoint with a system message placed after user messages. The model ignores the system message. What is the most likely reason?

Accepted Answer

The system message is placed after user messages. In OCI Generative AI's chat endpoint, the system message must be placed before user messages to establish the model's behavior and context. When placed after user messages, the model treats it as part of the conversation history rather than a directive, causing it to be ignored. This ordering is a fundamental requirement for the chat API's message structure.

Answer

The system message is too long

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Temperature is set too high

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The model has not been fine-tuned for instruction following