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Amazon AIF-C01 Exam (page: 2)
Amazon AWS Certified AI Practitioner
Updated on: 12-Feb-2026

Viewing Page 2 of 43
AIF-C01 PDF 385 Questions

A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket. The data is encrypted with Amazon S3 managed keys (SSE-S3).

The FM encounters a failure when attempting to access the S3 bucket data.

Which solution will meet these requirements?

  1. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
  2. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
  3. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
  4. Ensure that the S3 data does not contain sensitive information.

Answer(s): A

Explanation:

The foundation model needs the appropriate permissions to decrypt the encrypted data in the S3 bucket.
Ensuring that the role used by Amazon Bedrock has permission to access and decrypt the data will resolve the access failure. The other options are not suitable for addressing the encryption and permission issue.



A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.

Which solution will meet these requirements?

  1. Deploy optimized small language models (SLMs) on edge devices.
  2. Deploy optimized large language models (LLMs) on edge devices.
  3. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
  4. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

Answer(s): A

Explanation:

Deploying optimized small language models (SLMs) directly on edge devices provides low latency for inference since the computation happens locally, avoiding the delays associated with network communication. The other options either increase latency or are less suitable for edge deployment.



A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.

Which SageMaker feature meets these requirements?

  1. Amazon SageMaker Feature Store
  2. Amazon SageMaker Data Wrangler
  3. Amazon SageMaker Clarify
  4. Amazon SageMaker Model Cards

Answer(s): A

Explanation:

Amazon SageMaker Feature Store is a centralized repository for storing and managing features, allowing multiple teams to share and reuse variables (features) for model development. The other options serve different purposes, such as data preprocessing or model documentation.



A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.

What can Amazon Q Developer do to help the company meet these requirements?

  1. Create software snippets, reference tracking, and open source license tracking.
  2. Run an application without provisioning or managing servers.
  3. Enable voice commands for coding and providing natural language search.
  4. Convert audio files to text documents by using ML models.

Answer(s): A

Explanation:

Amazon Q Developer is designed to assist developers by generating software snippets, tracking references, and managing open source licenses, which aligns with the company's goal of increasing productivity in software development. The other options do not match the intended use of Amazon Q Developer.



A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.

Which AWS service or feature will meet these requirements?

  1. AWS PrivateLink
  2. Amazon Macie
  3. Amazon CloudFront
  4. Internet gateway

Answer(s): A

Explanation:

AWS PrivateLink allows secure, private connectivity between VPCs and AWS services without needing internet access, making it suitable for meeting regulatory compliance standards. The other options either do not provide private connectivity or require internet access.



A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles.
What is the probability of choosing a green marble from the jar?"

Which solution meets these requirements with the LEAST operational overhead?

  1. Use supervised learning to create a regression model that will predict probability.
  2. Use reinforcement learning to train a model to return the probability.
  3. Use code that will calculate probability by using simple rules and computations.
  4. Use unsupervised learning to create a model that will estimate probability density.

Answer(s): C

Explanation:

Calculating probability in this scenario involves straightforward arithmetic, making it most efficient to use simple rules and computations. This approach requires the least operational overhead compared to using complex ML models, which are unnecessary for such basic tasks.



Which metric measures the runtime efficiency of operating AI models?

  1. Customer satisfaction score (CSAT)
  2. Training time for each epoch
  3. Average response time
  4. Number of training instances

Answer(s): C

Explanation:

Average response time measures how quickly an AI model produces an output, which reflects the runtime efficiency of the model. The other options do not directly measure the efficiency of operating AI models.



A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls.

Which solution meets these requirements?

  1. Build a conversational chatbot by using Amazon Lex.
  2. Transcribe call recordings by using Amazon Transcribe.
  3. Extract information from call recordings by using Amazon SageMaker Model Monitor.
  4. Create classification labels by using Amazon Comprehend.

Answer(s): B

Explanation:

Amazon Transcribe converts audio recordings into text, which allows for further analysis and extraction of key information from customer conversations. The other options do not directly handle audio transcription or extraction of information from audio.



Viewing Page 2 of 43



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