AWS Certified AI Practitioner (AIF-C01)

The AWS Certified AI Practitioner (AIF-C01) were last updated on today.

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Viewing questions 31-35 out of 120 questions

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Topic 1 - Exam A

Question #31 Topic 1

A large retail bank wants to develop an ML system to help the risk management team decide on loan allocations for different demographics. What must the bank do to develop an unbiased ML model?

A Reduce the size of the training dataset.
B Ensure that the ML model predictions are consistent with historical results.
C Create a different ML model for each demographic group.
D Measure class imbalance on the training dataset. Adapt the training process accordingly.

Suggested Answer: D
NOTE: The correct answer is D because to develop an unbiased ML model, it's crucial to measure class imbalance on the training dataset and adapt the training process accordingly. This ensures that all demographic groups are represented fairly and reduces the risk of bias.

Question #32 Topic 1

A retail store wants to predict the demand for a specific product for the next few weeks by using the Amazon SageMaker DeepAR forecasting algorithm. Which type of data will meet this requirement?

A Text data
B Image data
C Time series data
D Binary data

Suggested Answer: C
NOTE: Amazon SageMaker DeepAR 算法主要用于时间序列数据的预测，因此选项 C (Time series data) 是正确的。其他选项如文本数据、图像数据和二进制数据不符合此算法的要求。

Question #33 Topic 1

A software company builds tools for customers. The company wants to use AI to increase software development productivity. Which solution will meet these requirements?

A Use a binary classification model to generate code reviews.
B Install code recommendation software in the company's developer tools.
C Install a code forecasting tool to predict potential code issues.
D Use a natural language processing (NLP) tool to generate code.

Suggested Answer: B
NOTE: 选项 B 是正确的，因为安装代码推荐软件可以直接在开发工具中提供代码建议，从而提高开发效率。而其他选项涉及到的技术（如二元分类模型、代码预测工具和自然语言处理）虽然也可能对提高生产力有所帮助，但不如直接安装代码推荐软件来得直接和有效。

Question #34 Topic 1

An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML team wants to build a mechanism that the ML team can use to audit models. Which solution should the ML team use when publishing the custom ML models?

A Create documents with the relevant information. Store the documents in Amazon S3.
B Use AWS AI Service Cards for transparency and understanding models.
C Create Amazon SageMaker Model Cards with intended uses and training and inference details.
D Create model training scripts. Commit the model training scripts to a Git repository.

Suggested Answer: C
NOTE: 选项C（创建包含预期用途、训练和推理细节的Amazon SageMaker模型卡片）是最合适的选择，因为它能够提供透明度和理解模型所需的详细信息。此外，这与问题中提到的需要一个可以审计模型的机制相匹配。

Question #35 Topic 1

Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

A Amazon Personalize
B Amazon SageMaker JumpStart
C PartyRock, an Amazon Bedrock Playground
D Amazon SageMaker endpoints

Suggested Answer: B
NOTE: Amazon SageMaker JumpStart 提供了一种简便的方式来快速部署和使用基础模型，包括预构建的机器学习模型、内置算法和示例笔记本。这使得 AI 开发团队可以快速在他们自己的 VPC 中进行部署和使用。