AWS AIF-C01 Free Practice Questions — Page 1

Q: A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts. An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders. What should the AI practitioner include in the report to meet the transparency and explainability requirements?

B. Partial dependence plots (PDPs). Partial dependence plots (PDPs) are visualization tools that show the marginal effect of features on the predicted outcome of a model. They help stakeholders understand how different input variables influence the model's predictions. PDPs are ideal for transparency and explainability because they provide intuitive visual explanations of model behavior without requiring technical expertise. Code for model training and convergence tables are too technical for stakeholders. Sample data alone does not explain model behavior.

Q: A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents. Which solution meets these requirements?

C. Develop a summarization chatbot.. A summarization chatbot is the ideal solution for extracting key points from legal documents. LLMs excel at text summarization tasks where they can read lengthy documents and condense them into key points and summaries. Named entity recognition only identifies entities like names and dates but does not extract key points. A recommendation engine suggests items based on user preferences which is not relevant here. Translation systems convert text between languages rather than extracting key information.

Q: A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output. Which ML algorithm meets these requirements?

A. Decision trees. Decision trees are highly interpretable ML algorithms that provide clear documentation of how decisions are made. Each node in a decision tree represents a feature-based decision and the path from root to leaf shows exactly how the classification was determined. This transparency makes decision trees ideal when explainability is required. Linear regression is for continuous predictions not classification. Logistic regression is less interpretable for multi-class problems. Neural networks are black-box models that lack interpretability.

Q: A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly. Which evaluation metric should the company use to measure the model's performance?

B. Accuracy. Accuracy is the correct metric for measuring how many images were classified correctly. It calculates the ratio of correct predictions to total predictions. R-squared and RMSE are regression metrics used for continuous value predictions not classification tasks. Learning rate is a hyperparameter used during training to control how much to adjust model weights and is not an evaluation metric.

Q: A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language. Which solution will align the LLM response quality with the company's expectations?

A. Adjust the prompt.. Adjusting the prompt is the most effective way to control LLM output format and style. Through prompt engineering you can specify desired response length language and format. Choosing a different model size does not guarantee specific output characteristics. Increasing temperature makes outputs more random and creative not more controlled. Increasing Top K also increases randomness in token selection. Prompt adjustment provides direct control over response characteristics.

Question 1

A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts. An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders. What should the AI practitioner include in the report to meet the transparency and explainability requirements?

Answer

Show Answer & Explanation

Correct Answer: B. Partial dependence plots (PDPs)

Partial dependence plots (PDPs) are visualization tools that show the marginal effect of features on the predicted outcome of a model. They help stakeholders understand how different input variables influence the model's predictions. PDPs are ideal for transparency and explainability because they provide intuitive visual explanations of model behavior without requiring technical expertise. Code for model training and convergence tables are too technical for stakeholders. Sample data alone does not explain model behavior.

Question 2

A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents. Which solution meets these requirements?

Answer

Show Answer & Explanation

Correct Answer: C. Develop a summarization chatbot.

A summarization chatbot is the ideal solution for extracting key points from legal documents. LLMs excel at text summarization tasks where they can read lengthy documents and condense them into key points and summaries. Named entity recognition only identifies entities like names and dates but does not extract key points. A recommendation engine suggests items based on user preferences which is not relevant here. Translation systems convert text between languages rather than extracting key information.

Question 3

A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output. Which ML algorithm meets these requirements?

Answer

Show Answer & Explanation

Correct Answer: A. Decision trees

Decision trees are highly interpretable ML algorithms that provide clear documentation of how decisions are made. Each node in a decision tree represents a feature-based decision and the path from root to leaf shows exactly how the classification was determined. This transparency makes decision trees ideal when explainability is required. Linear regression is for continuous predictions not classification. Logistic regression is less interpretable for multi-class problems. Neural networks are black-box models that lack interpretability.

Question 4

A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly. Which evaluation metric should the company use to measure the model's performance?

Answer

Show Answer & Explanation

Correct Answer: B. Accuracy

Accuracy is the correct metric for measuring how many images were classified correctly. It calculates the ratio of correct predictions to total predictions. R-squared and RMSE are regression metrics used for continuous value predictions not classification tasks. Learning rate is a hyperparameter used during training to control how much to adjust model weights and is not an evaluation metric.

Question 5

A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language. Which solution will align the LLM response quality with the company's expectations?

Answer

Show Answer & Explanation

Correct Answer: A. Adjust the prompt.

Adjusting the prompt is the most effective way to control LLM output format and style. Through prompt engineering you can specify desired response length language and format. Choosing a different model size does not guarantee specific output characteristics. Increasing temperature makes outputs more random and creative not more controlled. Increasing Top K also increases randomness in token selection. Prompt adjustment provides direct control over response characteristics.

AWS AIF-C01 Free Practice Questions — Page 1

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