Amazon AWS Certified AI Practitioner AIF-C01 Exam Practice Questions (P. 1)
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Question #1
A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.
An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.
What should the AI practitioner include in the report to meet the transparency and explainability requirements?
An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.
What should the AI practitioner include in the report to meet the transparency and explainability requirements?
- ACode for model training
- BPartial dependence plots (PDPs)
- CSample data for training
- DModel convergence tables
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Question #2
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?
Which solution meets these requirements?
- ABuild an automatic named entity recognition system.
- BCreate a recommendation engine.
- CDevelop a summarization chatbot.
- DDevelop a multi-language translation system.
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Question #3
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?
Which ML algorithm meets these requirements?
- ADecision trees
- BLinear regression
- CLogistic regression
- DNeural networks
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Question #4
A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.
Which evaluation metric should the company use to measure the model's performance?
Which evaluation metric should the company use to measure the model's performance?
- AR-squared score
- BAccuracy
- CRoot mean squared error (RMSE)
- DLearning rate
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Question #5
A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language.
Which solution will align the LLM response quality with the company's expectations?
Which solution will align the LLM response quality with the company's expectations?
- AAdjust the prompt.
- BChoose an LLM of a different size.
- CIncrease the temperature.
- DIncrease the Top K value.
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Question #6
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?
Which SageMaker inference option meets these requirements?
- AReal-time inference
- BServerless inference
- CAsynchronous inference
- DBatch transform
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Question #7
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?
Which ML strategy meets these requirements?
- AIncrease the number of epochs.
- BUse transfer learning.
- CDecrease the number of epochs.
- DUse unsupervised learning.
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Question #8
A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.
Which solution will meet these requirements?
Which solution will meet these requirements?
- AHuman-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
- BData augmentation by using an Amazon Bedrock knowledge base
- CImage recognition by using Amazon Rekognition
- DData summarization by using Amazon QuickSight Q
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Question #9
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?
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?
- AEnsure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
- BSet the access permissions for the S3 buckets to allow public access to enable access over the internet.
- CUse prompt engineering techniques to tell the model to look for information in Amazon S3.
- DEnsure that the S3 data does not contain sensitive information.
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Question #10
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?
Which solution will meet these requirements?
- ADeploy optimized small language models (SLMs) on edge devices.
- BDeploy optimized large language models (LLMs) on edge devices.
- CIncorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
- DIncorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.
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