AI-900 Describe features of generative AI workloads on Azure Practice Test 2 — 15 Questions

Question 1

A marketing team uses Azure OpenAI Service to generate headline ideas for a campaign. They find the generated headlines are often too similar and lack creativity. Which parameter should they increase to introduce more randomness in the generated text?

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

Accepted Answer

Temperature. (Temperature) is correct because temperature controls the randomness of token selection in the model's probability distribution. Increasing temperature (e.g., from 0.7 to 1.0) flattens the probability curve, making lower-probability tokens more likely to be chosen, which introduces more diversity and creativity in the generated headlines.

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Frequency penalty

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Top_p (nucleus sampling)

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Presence penalty

Question 2

A game development studio uses Azure OpenAI Service to generate unique backstories for non-player characters (NPCs). They want the generated stories to be coherent and relevant to a given character class (e.g., warrior, mage) but also creative and varied. Which parameter should the studio adjust primarily to increase the creativity and variety of the generated text?

Accepted Answer

Increase the temperature parameter. Increasing the temperature parameter makes the model's output more random by scaling the probability distribution over tokens, which encourages less likely word choices and thus increases creativity and variety in generated text. For the game studio, a higher temperature (e.g., 0.8–1.0) will produce more diverse and imaginative backstories for different character classes, while still maintaining coherence if not set too high.

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Increase the top_p parameter

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Increase the frequency_penalty parameter

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Decrease the max_tokens parameter

Question 3

A marketing team wants to use Azure OpenAI Service to generate product descriptions that consistently match a specific brand voice. They have a small set of example descriptions that demonstrate the desired tone. They want to adapt the model without retraining it from scratch. Which approach should they take?

Accepted Answer

Use prompt engineering with few-shot learning by including the example descriptions in the prompt. Prompt engineering with few-shot learning allows the model to infer the desired brand voice from the example descriptions included directly in the prompt, without requiring retraining. This approach leverages the model's in-context learning capability, where it adapts its output based on the provided examples while keeping the base model unchanged.

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Fine-tune the base model on the example descriptions

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Increase the temperature parameter to the maximum value

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Train a new model using Azure Machine Learning

Question 4

A company uses Azure OpenAI Service to generate long technical reports. To manage costs, the development team needs to accurately estimate the number of tokens that a given prompt will consume before making any API call. Which Azure OpenAI Service feature should they use to obtain this estimate?

Accepted Answer

The Token Counter tool in Azure OpenAI Studio. The Token Counter tool in Azure OpenAI Studio is specifically designed to estimate the number of tokens a prompt will consume before making an API call. This allows developers to predict costs accurately by calculating token usage for both input and expected output, without incurring actual API charges.

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The Chat Completions API

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The Embeddings API

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The Content Filter configuration

Question 5

A developer is using Azure OpenAI Service to generate Python code snippets. They notice that the generated code often contains repetitive function definitions and loops. Which parameter should be increased to reduce this repetition?

Accepted Answer

Frequency penalty. The frequency penalty parameter reduces repetition by penalizing tokens that have already appeared in the generated text, making the model less likely to reuse the same functions or loops. Increasing this value directly discourages the model from generating repetitive patterns, which is exactly the issue described.

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Temperature

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Max tokens

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Top P

Question 6

A marketing team uses Azure OpenAI Service to generate headline ideas for a campaign. They find the generated headlines are often too similar and lack creativity. Which parameter should they increase to introduce more randomness in the generated text?

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

Accepted Answer

Frequency penalty. Frequency penalty directly reduces repetition by penalizing tokens that have already appeared in the generated text. A higher frequency penalty value (e.g., 0.5 to 1.0) decreases the likelihood of the model reusing the same phrases or words, making the output more diverse and less repetitive.

Answer

Temperature

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Top-p

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Max tokens

Question 7

A developer uses Azure OpenAI Service to generate creative marketing copy. The API costs are based on the total number of tokens processed (input + output). To minimize costs, the developer wants to ensure that the generated text is as brief as possible while still being effective. Which parameter should the developer adjust in the API request?

Accepted Answer

max_tokens. (max_tokens) is correct because this parameter directly controls the maximum number of tokens the model can generate in a single response. By setting a lower max_tokens value, the developer caps the length of the output, which reduces the total tokens processed (input + output) and thus lowers API costs. Other parameters influence the style or diversity of the output but do not directly limit the length of the generated text.

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temperature

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top_p

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frequency_penalty

Question 8

A developer uses Azure OpenAI Service to generate product descriptions. Each description must be concise and not exceed 50 words. Which parameter should the developer set in the API request to control the output length?

Accepted Answer

max_tokens. The `max_tokens` parameter in the Azure OpenAI API directly controls the maximum number of tokens (words or subwords) in the generated output. By setting `max_tokens` to a value that corresponds to 50 words, the developer ensures the model stops generating once the limit is reached, producing concise descriptions.

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Temperature

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top_p

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frequency_penalty

Question 9

A marketing team uses Azure OpenAI Service to generate social media posts. They want the generated text to be more creative and diverse, with unexpected word choices. Which parameter should they increase?

Accepted Answer

temperature. Increasing the temperature parameter makes the model more creative and diverse by raising the probability of sampling lower-probability tokens, leading to unexpected word choices. Temperature controls the randomness of token selection, with higher values (e.g., 0.9) producing more varied outputs, which aligns with the team's goal of generating creative social media posts.

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frequency_penalty

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presence_penalty

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top_p

Question 10

A marketing team uses Azure OpenAI Service to generate headline ideas for a campaign. They find the generated headlines are often too similar and lack creativity. Which parameter should they increase to introduce more randomness in the generated text?

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

Accepted Answer

Temperature. Increasing the temperature parameter makes the model's output more random and diverse by scaling the probability distribution over possible next tokens. A higher temperature (e.g., 0.9) flattens the distribution, giving lower-probability tokens a better chance to be selected, which directly addresses the lack of creativity and variety in the generated taglines.

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Top P

Answer

Frequency penalty

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Max tokens

Question 11

A financial analyst uses Azure OpenAI Service to generate summaries of quarterly earnings reports. The analyst provides the raw text of the report in the prompt and wants the summary to stick strictly to the facts presented in that text, without adding any external information or speculation. Which technique should the analyst employ to minimize the risk of the model inventing information?

Accepted Answer

Use grounding by including the report text in the prompt and explicitly instructing the model to base the summary only on that text.. Grounding the model with the source text and explicitly instructing it to base the summary solely on that text is the most direct way to reduce hallucination. Azure OpenAI Service relies on the prompt for context; by providing the raw report and a strict instruction, the model is constrained to extract facts from the provided content rather than generating novel information.

Answer

Set the temperature parameter to a high value.

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Set the frequency penalty to the maximum allowed value.

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Set the max_tokens parameter to a very small number.

Question 12

A writer uses Azure OpenAI Service to generate story ideas. The current configuration uses a temperature setting of 0, causing the model to produce identical outputs for the same prompt. The writer wants more creative and diverse outputs. Which parameter should be increased?

Accepted Answer

temperature. Temperature controls the randomness of the model's output. A temperature of 0 makes the model deterministic, always choosing the most likely next token, which leads to identical outputs for the same prompt. Increasing the temperature (e.g., to 0.7 or higher) introduces more randomness, allowing the model to sample from less likely tokens and produce more creative, diverse story ideas.

Answer

max_tokens

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top_p

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frequency_penalty

Question 13

A developer uses Azure OpenAI Service to generate code snippets. They need the model to produce the most likely completion each time, with no randomness or creativity. Which parameter should they set?

Accepted Answer

temperature = 0. Setting temperature = 0 forces the model to always select the token with the highest probability at each step, eliminating randomness and ensuring deterministic, most-likely completions. This is ideal for tasks like code generation where consistency and predictability are required, as it disables the sampling randomness that higher temperature values introduce.

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temperature = 1

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top_p = 0.5

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frequency_penalty = 0.5

Question 14

A developer uses Azure OpenAI Service to generate product reviews for an e-commerce site. The developer notices that the model often repeats the same phrases within the same review, making the output sound unnatural. Which parameter should the developer adjust to reduce this repetition?

Accepted Answer

Frequency_penalty. The frequency_penalty parameter reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. A higher frequency_penalty value (e.g., 0.5 to 1.0) discourages the model from reusing the same words or phrases, making the output more diverse and natural. This directly addresses the issue of repetitive phrasing in product reviews.

Answer

Temperature

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Top_p

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Max_tokens

Question 15

A developer uses Azure OpenAI Service to generate multiple alternative product slogans. The developer wants to get exactly 5 different slogan options in a single API call, each being a separate piece of text. Which parameter should the developer set to control the number of completions returned?

Accepted Answer

n. The 'n' parameter in Azure OpenAI Service specifies the number of completions (candidate responses) to generate for each API call. Setting n=5 returns exactly five distinct slogan options as separate text strings, fulfilling the requirement of a single request producing multiple alternatives.

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temperature

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max_tokens

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stop