Free Amazon AIF-C01 Exam Questions

Question 1

A security company is evaluating Amazon Rekognition to enhance its Machine Learning (ML) capabilities. However, the data science team needs to identify scenarios where Amazon Rekognition may not be the most suitable solution. Understanding these limitations will help the team select the right tools for different aspects of their security system.Given this context, which of the following use cases is NOT the right fit for Amazon Rekognition?

A : Celebrity recognition

B : Enable multilingual user experiences in your applications

C : Face-based user identity verification

D : Searchable media libraries

Answer: B

Question 2

A security company is evaluating Amazon Rekognition to enhance its Machine Learning (ML) capabilities. However, the data science team needs to identify scenarios where Amazon Rekognition may not be the most suitable solution. Understanding these limitations will help the team select the right tools for different aspects of their security system.Given this context, which of the following use cases is NOT the right fit for Amazon Rekognition?

A : Celebrity recognition

B : Enable multilingual user experiences in your applications

C : Face-based user identity verification

D : Searchable media libraries

Answer: B

Question 3

Which type of cloud computing does Amazon Elastic Compute Cloud (EC2) represent?

A : Infrastructure as a Service (IaaS)

B : Network as a Service (NaaS)

C : Platform as a Service (PaaS)

D : Software as a Service (SaaS)

Answer: A

Question 4

A retail company is looking to streamline its machine learning workflows and improve collaboration between data science teams. The team is considering using MLflow with Amazon SageMaker to manage the end-to-end machine learning lifecycle. Understanding how MLflow integrates with Amazon SageMaker will help the team decide if this combination is the right fit for their machine learning project management needs.Given this context, which statement best defines the use of MLflow with Amazon SageMaker?

A : Label data using human-in-the-loop

B : Leverage no-code ML

C : Manage machine learning experiments

D : Perform automatic model tuning

Answer: C

Question 5

Which of the following are correct regarding model evaluation for Amazon Bedrock? (Select two)

A : Human model evaluation is valuable for assessing qualitative aspects of the model, whereas, automatic model valuation is valuable for assessing quantitative aspects of the model

B : For human model evaluation, you can use either built-in prompt datasets or your own prompt datasets

C : Human model evaluation provides model scores that are calculated using various statistical methods such as BERT Score and F1

D : Automatic model evaluation is valuable for assessing qualitative aspects of the model, whereas, human model valuation is valuable for assessing quantitative aspects of the model

E : Automatic model evaluation provides model scores that are calculated using various statistical methods such as BERT Score and F1

Answer: A,E