How To Pass The AWS Certified AI Practitioner AIF-C01 Exam

The AWS Certified Cloud Practitioner certification serves as an excellent entry point for individuals embarking on their AWS Certification journey, particularly those without prior IT or cloud experience. Passcert has recently introduced their AWS Certified AI Practitioner AIF-C01 Dumps designed to help you thoroughly assess your knowledge and preparedness for the actual exam. By utilizing these AWS Certified AI Practitioner AIF-C01 Dumps, you can identify areas that require further study and reinforce your understanding of key concepts. Moreover, working through these AWS Certified AI Practitioner AIF-C01 Dumps can significantly boost your confidence, increasing your chances of success on exam day.

AWS Certified AI Practitioner AIF-C01 Dumps

AWS Certified AI Practitioner

AWS Certified AI Practitioner validates in-demand knowledge of artificial intelligence (AI), machine learning (ML), and generative AI concepts and use cases. Sharpen your competitive edge and position yourself for career growth and higher earnings. Register now and be among the first to earn this certification.

The AWS Certified AI Practitioner (AIF-C01) exam is intended for individuals who can effectively demonstrate overall knowledge of AI/ML, generative AI technologies, and associated AWS services and tools, independent of a specific job role.

The exam also validates a candidate’s ability to complete the following tasks:

● Understand AI, ML, and generative AI concepts, methods, and strategies in general and on AWS.

● Understand the appropriate use of AI/ML and generative AI technologies to ask relevant questions within the candidate’s organization.

● Determine the correct types of AI/ML technologies to apply to specific use cases.

● Use AI, ML, and generative AI technologies responsibly.

Exam Overview

Category Foundational

Exam duration 120 minutes

Exam format 85 questions

Cost 75 USD/10,000 JPY

Intended candidate Individuals who are familiar with, but do not necessarily build, solutions using AI/ML technologies on AWS

Candidate role examples Business analyst, IT support, marketing professional, product or project manager, line-of-business or IT manager, sales professional

Testing options Pearson VUE testing center or online proctored exam

Languages offered English, Japanese

Exam Domains

The exam has the following content domains and weightings:

● Domain 1: Fundamentals of AI and ML (20% of scored content)

Task Statement 1.1: Explain basic AI concepts and terminologies.

Task Statement 1.2: Identify practical use cases for AI.

Task Statement 1.3: Describe the ML development lifecycle.

● Domain 2: Fundamentals of Generative AI (24% of scored content)

Task Statement 2.1: Explain the basic concepts of generative AI.

Task Statement 2.2: Understand the capabilities and limitations of generative AI for solving business problems.

Task Statement 2.3: Describe AWS infrastructure and technologies for building generative AI applications.

● Domain 3: Applications of Foundation Models (28% of scored content)

Task Statement 3.1: Describe design considerations for applications that use foundation models.

Task Statement 3.2: Choose effective prompt engineering techniques.

Task Statement 3.3: Describe the training and fine-tuning process for foundation models.

Task Statement 3.4: Describe methods to evaluate foundation model performance.

● Domain 4: Guidelines for Responsible AI (14% of scored content)

Task Statement 4.1: Explain the development of AI systems that are responsible.

Task Statement 4.2: Recognize the importance of transparent and explainable models.

● Domain 5: Security, Compliance, and Governance for AI Solutions (14% ofscored content)

Task Statement 5.1: Explain methods to secure AI systems.

Task Statement 5.2: Recognize governance and compliance regulations for AI systems.

How is AWS Certified AI Practitioner different from AWS Certified Cloud Practitioner?

AWS Certified Cloud Practitioner focuses on overall knowledge of AWS Cloud and gives a foundational-level overview of all AWS services. AWS Certified AI Practitioner covers the breadth of AI frameworks, concepts, and associated AWS technologies, with an emphasis on generative AI. The exam content outline for Cloud Practitioner contains only one task statement related to AI.

In contrast, the entire exam content outline for AWS Certified AI Practitioner focuses on AI, ML, and generative AI. You should take the exam that best aligns with your interests and needs. You also have the option to earn both certifications if you want to demonstrate a strong grasp of both AWS cloud and AI/ML.

What certification(s) should I earn next after AWS Certified AI Practitioner?

For individuals transitioning to cloud careers, we recommend AWS Certified Solutions Architect - Associate. For those pursuing careers in data, AI, and machine learning, we recommend AWS Certified Data Engineer - Associate and/or AWS Certified Machine Learning Engineer - Associate.

Share AWS Certified AI Practitioner AIF-C01 Free Dumps

1. A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights.

Which AWS service can the company use to meet this requirement?

A.Amazon Lex

B.Amazon Comprehend

C.Amazon Transcribe

D.Amazon Translate

Answer: B

2. A company has a database of petabytes of unstructured data from internal sources. The company wants to transform this data into a structured format so that its data scientists can perform machine learning (ML) tasks.

Which service will meet these requirements?

A.Amazon Lex

B.Amazon Rekognition

C.Amazon Kinesis Data Streams

D.AWS Glue

Answer: D

3. 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

Answer: D

4. A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.

Which prompt engineering strategy meets these requirements?

A.Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.

B.Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.

C.Provide the new text passage to be classified without any additional context or examples.

D.Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.

Answer: A

5. A company has installed a security camer

a. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.

Which type of bias is affecting the model output?

A.Measurement bias

B.Sampling bias

C.Observer bias

D.Confirmation bias

Answer: B

6. A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.

Which solution meets these requirements?

A.Set a low limit on the number of tokens the FM can produce.

B.Use batch inferencing to process detailed responses.

C.Experiment and refine the prompt until the FM produces the desired responses.

D.Define a higher number for the temperature parameter.

Answer: C

7. A student at a university is copying content from generative AI to write essays.

Which challenge of responsible generative AI does this scenario represent?

A.Toxicity

B.Hallucinations

C.Plagiarism

D.Privacy

Answer: C

8. Which metric measures the runtime efficiency of operating AI models?

A.Customer satisfaction score (CSAT)

B.Training time for each epoch

C.Average response time

D.Number of training instances

Answer: C

9. Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?

A.Helps decrease the model's complexity

B.Improves model performance over time

C.Decreases the training time requirement

D.Optimizes model inference time

Answer: B