1Z0-1127-25 Using OCI Generative AI Service Practice Test 8 — 15 Questions

Question 1

A developer is using the OCI Generative AI service API and receives a '400 Bad Request' with error 'Model not found'. What is the most likely cause?

Accepted Answer

The model ID is misspelled or does not exist.. The '400 Bad Request' with 'Model not found' error indicates that the model ID provided in the API request does not match any available model in the OCI Generative AI service. This is most commonly caused by a typo, incorrect model name, or referencing a model that has been deprecated or removed from the service catalog.

Answer

The API request lacks authentication.

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The input exceeds the maximum token limit.

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The endpoint region is incorrect.

Question 2

An enterprise wants to use OCI Generative AI to generate personalized email campaigns. They have a large customer database with preferences and past purchase history. Which design is best for high relevance and scalability?

Accepted Answer

Use a pipeline: retrieve relevant customer data and inject into prompt.. It implements a retrieval-augmented generation (RAG) pattern, which is the recommended approach for personalizing outputs at scale in OCI Generative AI. By retrieving only the relevant customer data (e.g., recent purchases, preferences) and injecting it into the prompt, you keep the context window efficient, reduce token costs, and avoid exceeding model limits. This design also allows the model to generate unique, context-aware emails without the overhead of fine-tuning per customer or the rigidity of rule-based systems.

Answer

Use a single prompt with all customer details as context.

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Fine-tune a model on each customer's history separately.

Answer

Use a rule-based engine with AI-generated templates.

Question 3

Which THREE factors should be considered when choosing between fine-tuning a model and using a pre-trained model with prompt engineering? (Select three.)

Accepted Answer

Size of available dataset. Option B is correct because fine-tuning typically requires a substantial labeled dataset, whereas prompt engineering can work with few or no examples, so the size of the available dataset directly determines which approach is feasible. Option D is correct because fine-tuning consumes significant compute for training and hosting, while prompt engineering mainly uses inference, making the available budget for compute resources a key decision factor. Option E is correct because if the task demands specialized domain-specific terminology or nuanced jargon, fine-tuning can embed that knowledge into the model, whereas prompt engineering must supply it through carefully crafted prompts. Option A is not a primary factor here because both approaches can be optimized for latency and response time depends more on deployment and model size than on the fine-tune-versus-prompt choice. Option C is not relevant because internet connectivity affects how a model is accessed or deployed, not the fundamental decision between fine-tuning and prompt engineering.

Answer

Required response time

Answer

Internet connectivity

Question 4

Which THREE OCI Generative AI service features help in controlling the cost of API calls? (Select three.)

Accepted Answer

Using a smaller model. Option A (Using a smaller model) is correct because OCI Generative AI prices inference per token, and smaller models (e.g., lower-parameter or lighter-tier models) have lower per-token rates, directly reducing the cost of each API call. Option C (Using stop sequences) is correct because stop sequences terminate generation as soon as a specified string is produced, cutting the number of output tokens billed and thus lowering cost. Option D (Setting max_tokens limit) is correct because it caps the number of tokens the model can generate, preventing unexpectedly long completions from inflating output-token charges. Option B (Increasing temperature) does not belong because temperature only affects randomness/creativity of sampling, not token counts or pricing. Option E (Enabling response streaming) does not belong because streaming only changes how tokens are delivered to the client, not how many tokens are generated or billed.

Answer

Increasing temperature

Answer

Enabling response streaming

Question 5

Refer to the exhibit. A developer runs the command and receives the error. What is the issue?

Accepted Answer

The max-tokens value exceeds the allowed range.. The error returned by the API indicates that the max-tokens parameter is outside the permitted range for the model. Each model enforces a maximum completion-token limit (for example, 4096 or 16384 depending on the model), and requesting more than that limit causes a validation error before any inference runs. The fix is to lower max-tokens to a value within the model's documented range.

Answer

The message is too short.

Answer

The chat-id is invalid.

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The endpoint is incorrect.

Question 6

Refer to the exhibit. A user in group GenAIUsers tries to use the `oci generative-ai model chat` command but gets 'not authorized'. Why?

Accepted Answer

The statement should be 'use' instead of 'read'.. The OCI IAM policy for Generative AI requires the 'use' verb to allow inference operations like chat, not the 'read' verb. The 'read' verb only grants permission to view metadata and list resources, not to invoke the model for chat completions. The error 'not authorized' occurs because the policy statement incorrectly uses 'read' instead of 'use', which is the required permission for the `oci generative-ai model chat` command.

Answer

The policy is not active yet.

Answer

The command requires managing the model.

Answer

The group is not in the correct compartment.

Question 7

A company wants to use OCI Generative AI service to generate email summaries for customer support. They need to ensure low latency and data residency in Frankfurt. What should they use?

Accepted Answer

Use the managed serving endpoint in Frankfurt region.. Managed serving endpoints in OCI Generative AI are pre-deployed, fully managed endpoints that provide low-latency inference and are available in specific regions, including Frankfurt. This ensures data residency in Frankfurt while meeting the latency requirements for real-time email summarization.

Answer

Use the playground in US region.

Answer

Use OCI AI Quick Actions.

Answer

Create a dedicated AI cluster in Frankfurt.

Question 8

A data scientist wants to quickly test a prompt with different parameters like temperature and max tokens without writing code. Which OCI GenAI feature should they use?

Accepted Answer

OCI Generative AI Playground.. The OCI Generative AI Playground is a web-based, no-code interface that allows data scientists to interactively test prompts and adjust parameters like temperature and max tokens without writing any code. This directly matches the user's requirement for quick, code-free experimentation.

Answer

OCI CLI.

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OCI SDK.

Answer

OCI Data Science Notebooks.

Question 9

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.

Answer

The request body format is incorrect.

Answer

The model is not available in the region.

Answer

The API key is invalid.

Question 10

A company is fine-tuning a Llama model on OCI with dedicated AI cluster. They want to use their own training data stored in Oracle Object Storage. What must they do to ensure the fine-tuning job can access the data?

Accepted Answer

Configure a resource principal for the cluster to read the bucket.. OCI Generative AI dedicated AI clusters require a resource principal (an IAM principal) to authenticate and authorize access to data stored in Oracle Object Storage. By configuring the cluster with a resource principal that has read permissions on the bucket, the fine-tuning job can securely read the training data without exposing credentials or using public URLs.

Answer

Upload the data to the dedicated cluster's local storage.

Answer

Use OCI Data Flow to transfer data.

Answer

Create a pre-authenticated request for the bucket.

Question 11

A company notices that their OCI GenAI managed serving endpoint returns incomplete responses for long prompts. What is the most likely cause?

Accepted Answer

The model's context window is exceeded.. When a prompt exceeds the model's context window, the model cannot process the entire input, leading to truncated or incomplete responses. OCI GenAI managed serving endpoints enforce a maximum context length (e.g., 4096 tokens for many models), and any prompt longer than this limit will be cut off, causing incomplete output.

Answer

The max tokens parameter is set too high.

Answer

The top_p value is too low.

Answer

The temperature is set too high.

Question 12

A developer wants to integrate OCI GenAI into a Java application. Which SDK should they use?

Accepted Answer

OCI Java SDK.. The OCI Java SDK is the official Oracle Cloud Infrastructure software development kit for Java, providing typed clients for services including OCI Generative AI. A Java application integrating OCI GenAI must use the Java SDK to authenticate, construct requests, and call the GenAI inference endpoints. The other SDKs target different languages and cannot be used from Java.

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OCI JavaScript SDK.

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OCI Python SDK.

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OCI CLI.

Question 13

A company is using dedicated AI cluster for fine-tuning. Which TWO best practices help optimize cost?

Accepted Answer

Manually scale down the cluster when not in use.. Option B is correct because manually scaling down (or terminating) the dedicated AI cluster when it is not actively fine-tuning eliminates idle compute charges, which is the primary cost driver for dedicated clusters. Option E is correct because selecting the smallest model that still meets the task's quality requirements reduces GPU memory, compute time, and node count needed for fine-tuning, directly lowering cost. Option A is incorrect because a larger replica count increases the number of running nodes and thus raises cost without necessarily improving fine-tuning throughput. Option C is incorrect because a managed serving endpoint is for inference, not fine-tuning, so it does not address the training cost scenario. Option D is incorrect because leaving the cluster running continuously incurs charges even when idle, which is the opposite of a cost optimization best practice.

Answer

Use the largest replica count.

Answer

Use the managed serving endpoint instead.

Answer

Leave the cluster running continuously.

Question 14

A startup wants to quickly prototype a chatbot using OCI Generative AI service. They have no prior experience with OCI. They want to test different models and parameters without writing any code and within a few minutes. They also want to save prompts and compare results. Which approach should they use?

Accepted Answer

Use the OCI Generative AI Playground.. The OCI Generative AI Playground is a no-code, web-based interface that allows users to quickly test different models and parameters, save prompts, and compare results without writing any code or having prior OCI experience. It directly meets the startup's requirement for rapid prototyping within minutes.

Answer

Create a dedicated AI cluster and use the OCI SDK.

Answer

Use OCI Data Science Notebooks with the GenAI SDK.

Answer

Use OCI Functions to invoke the GenAI API.

Question 15

A company uses OCI Generative AI service for customer support summarization. They notice the model frequently misses key details and generates hallucinations. What should they do first?

Accepted Answer

Adjust the prompt to be more specific and include few-shot examples.. Adjusting the prompt to be more specific and including few-shot examples is the first and most effective step to reduce hallucinations and improve detail capture in OCI Generative AI. Prompt engineering directly guides the model's attention and output format without altering core parameters or models, addressing the root cause of missing key details.

Answer

Increase the temperature parameter.

Answer

Use a different base model.

Answer

Increase the max tokens.