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AIF-C01 Practice Questions

AWS Certified AI Practitioner Exam

Last Update 3 days ago
Total Questions : 393

Dive into our fully updated and stable AIF-C01 practice test platform, featuring all the latest AWS Certified AI Practitioner exam questions added this week. Our preparation tool is more than just a Amazon Web Services study aid; it's a strategic advantage.

Our free AWS Certified AI Practitioner practice questions crafted to reflect the domains and difficulty of the actual exam. The detailed rationales explain the 'why' behind each answer, reinforcing key concepts about AIF-C01. Use this test to pinpoint which areas you need to focus your study on.

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Question # 81

Which statement presents an advantage of using Retrieval Augmented Generation (RAG) for natural language processing (NLP) tasks?

Options:

A.  

RAG can use external knowledge sources to generate more accurate and informative responses

B.  

RAG is designed to improve the speed of language model training

C.  

RAG is primarily used for speech recognition tasks

D.  

RAG is a technique for data augmentation in computer vision tasks

Discussion 0
Question # 82

A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company ' s brand voice and messaging requirements.

Which solution meets these requirements?

Options:

A.  

Optimize the model ' s architecture and hyperparameters to improve the model ' s overall performance.

B.  

Increase the model ' s complexity by adding more layers to the model ' s architecture.

C.  

Create effective prompts that provide clear instructions and context to guide the model ' s generation.

D.  

Select a large, diverse dataset to pre-train a new generative model.

Discussion 0
Question # 83

An AI practitioner has prepared a dataset for training models in Amazon SageMaker AI. The AI practitioner wants to share the dataset within the company so that future employees can discover and reuse the dataset.

Which solution will meet these requirements?

Options:

A.  

Copy the training dataset to Amazon Bedrock Knowledge Bases.

B.  

Upload the training data to a shared SageMaker notebook instance.

C.  

Store the training data in SageMaker Feature Store.

D.  

Upload the training data to AWS Data Exchange.

Discussion 0
Question # 84

A documentary filmmaker wants to reach more viewers. The filmmaker wants to automatically add subtitles and voice-overs in multiple languages to their films.

Which combination of steps will meet these requirements? (Select TWO.)

Options:

A.  

Use Amazon Transcribe and Amazon Translate to generate subtitles in other languages

B.  

Use Amazon Textract and Amazon Translate to generate subtitles in other languages

C.  

Use Amazon Polly to generate voice-overs in other languages

D.  

Use Amazon Translate to generate voice-overs in other languages

E.  

Use Amazon Textract to generate voice-overs in other languages

Discussion 0
Question # 85

An online media streaming company wants to give its customers the ability to perform natural language-based image search and filtering. The company needs a vector database that can help with similarity searches and nearest neighbor queries.

Which AWS service meets these requirements?

Options:

A.  

Amazon Comprehend

B.  

Amazon Personalize

C.  

Amazon Polly

D.  

Amazon OpenSearch Service

Discussion 0
Question # 86

A company wants to classify images of different objects based on custom features extracted from a dataset.

Which solution will meet this requirement with the LEAST development effort?

Options:

A.  

Use traditional ML algorithms with custom features extracted from the dataset.

B.  

Use a pre-trained deep learning model and fine-tune the model on the dataset.

C.  

Use a generative adversarial network (GAN) model to classify the images.

D.  

Use a support vector machine (SVM) with manually engineered features for classification.

Discussion 0
Question # 87

A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?

Options:

A.  

Use data from only customers who match the demography of the company ' s overall customer base.

B.  

Collect data from customers who have a past purchase history.

C.  

Ensure that the data is balanced and collected from a diverse group.

D.  

Ensure that the data is from a publicly available dataset.

Discussion 0
Question # 88

A company is testing the security of a foundation model (FM). During testing, the company wants to get around the safety features and make harmful content.

Options:

A.  

Fuzzing training data to find vulnerabilities

B.  

Denial of service (DoS)

C.  

Penetration testing with authorization

D.  

Jailbreak

Discussion 0
Question # 89

A company wants to create an application to summarize meetings by using meeting audio recordings.

Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THRE

E.  

)

• Convert meeting audio recordings to meeting text files by using Amazon Polly.

• Convert meeting audio recordings to meeting text files by using Amazon Transcribe.

• Store meeting audio recordings in an Amazon S3 bucket.

• Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.

• Summarize meeting text files by using Amazon Bedrock.

• Summarize meeting text files by using Amazon Lex.

Options:

Discussion 0
Question # 90

A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions. Which strategy will meet these requirements MOST cost-effectively?

Options:

A.  

Fine-tune the model regularly.

B.  

Train the model by using context data.

C.  

Pre-train and benchmark the model by using context data.

D.  

Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.

Discussion 0
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