Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 441:

  A company wants to assess internet quality in remote areas of the world. The company needs to collect internet speed data and store the data in Amazon RDS. The company will analyze internet speed variation throughout each day. The company wants to create an AI model to predict potential internet disruptions. Which type of data should the company collect for this task?

  A. Tabular data
  B. Text data
  C. Time series data
  D. Audio data
  C. Time series data

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  Time series data consists of measurements (such as internet speed) collected over time, which is essential for analyzing variations throughout the day and for building predictive models of potential internet disruptions.

• Question 442:

  A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation. Which AWS service meets these requirements?

  A. Amazon S3
  B. Amazon Elastic Block Store (Amazon EBS)
  C. Amazon Elastic File System (Amazon EFS)
  D. AWS Snowcone
  A. Amazon S3

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  Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.

  Option A (Correct): "Amazon S3":This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.

  Option B:"Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.

  Option C:"Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.

  Option D:"AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.

  References:

  Storing and Managing Datasets on AWS for Machine Learning:AWS recommends using S3 for storing and managing datasets required for ML model training and validation.

• Question 443:

  A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data. Which solution will meet these requirements?

  A. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
  B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
  C. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
  D. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
  D. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

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  Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.

  Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas":This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.

  Option A:"Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it requires coding knowledge to interact with SageMaker's built- in algorithms.

  Option B:"Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.

  Option C:"Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.

  References:

  Amazon SageMaker Canvas Overview:AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.

• Question 444:

  A company deployed a model to production. After 4 months, the model inference quality degraded. The company wants to receive a notification if the model inference quality degrades. The company also wants to ensure that the problem does not happen again. Which solution will meet these requirements?

  A. Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.
  B. Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.
  C. Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.
  D. Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.
  B. Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.

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  Amazon SageMaker Model Monitor enables continuous monitoring of model inference quality and detects data/model drift in production. Setting up notifications ensures the company is alerted if quality degrades again, allowing timely intervention and retraining as needed.

• Question 445:

  An AI practitioner is creating prompts for an Amazon Nova Canvas model. The AI practitioner wants to exclude certain characteristics from the model's generated output.

  Which prompting technique will meet this requirement?

  A. Mask prompts
  B. Negative prompts
  C. Inpainting prompts
  D. Outpainting prompts
  B. Negative prompts

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  Explanation

  Negative prompts are used to tell Amazon Nova Canvas which objects, attributes, or styles should be excluded from the generated image.

• Question 446:

  A company wants to set up private access to Amazon Bedrock APIs from the company's AWS account. The company also wants to protect its data from internet exposure. Which solution meets these requirements?

  A. Use Amazon CloudFront to restrict access to the company's private content.
  B. Use AWS Glue to set up data encryption across the company's data catalog.
  C. Use AWS Lake Formation to manage centralized data governance and cross-account data sharing.
  D. Use AWS PrivateLink to configure a private connection between the company's VPC and Amazon Bedrock.
  D. Use AWS PrivateLink to configure a private connection between the company's VPC and Amazon Bedrock.

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  AWS PrivateLink enables private connectivity between your VPC and Amazon Bedrock APIs without exposing data to the public internet, ensuring that all API calls and data transfers remain secure and private within your AWS account.

• Question 447:

  A manager wants a simple dashboard that shows how each input feature affects a model's predictions for a sales forecast model. Which AWS tool provides this functionality?

  A. Amazon CloudWatch Metrics
  B. Amazon SageMaker Clarify
  C. Amazon Athena
  D. Amazon QuickSight forecasting
  B. Amazon SageMaker Clarify

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  SageMaker Clarify provides feature-attribution explainability (e.g., SHAP values) that helps visualize how individual inputs influence predictions, enabling model transparency for stakeholders.

• Question 448:

  A medical company wants to develop an AI application that can access structured patient records, extract relevant information, and generate concise summaries. Which solution will meet these requirements?

  A. Use Amazon Comprehend Medical to extract relevant medical entities and relationships. Apply rule-based logic to structure and format summaries.
  B. Use Amazon Personalize to analyze patient engagement patterns. Integrate the output with a general purpose text summarization tool.
  C. Use Amazon Textract to convert scanned documents into digital text. Design a keyword extraction system to generate summaries.
  D. Implement Amazon Kendra to provide a searchable index for medical records. Use a template-based system to format summaries.
  A. Use Amazon Comprehend Medical to extract relevant medical entities and relationships. Apply rule-based logic to structure and format summaries.

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  Amazon Comprehend Medical is specifically designed to extract structured medical information (such as medication, condition, test results) from unstructured text. By applying rule-based logic afterward, relevant data can be formatted into concise summaries, meeting both the extraction and summarization needs.

• Question 449:

  A company stores telemetry data from thousands of IoT sensors. The company wants to detect abnormal patterns without labeled data. Which approach is MOST appropriate?

  A. Supervised classification
  B. Few-shot prompting
  C. Autoencoder-based anomaly detection
  D. Topic modeling
  C. Autoencoder-based anomaly detection

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  Autoencoders learn normal data patterns in an unsupervised way. When presented with anomalies, reconstruction error increases, making them ideal for anomaly detection without labels.

• Question 450:

  Which outcome is a result of increasing model transparency?

  A. Reduced need for model validation steps
  B. Elimination of regulatory compliance monitoring requirements
  C. Automatic removal of all bias from model predictions
  D. Enhanced ability to identify bias and improve model governance
  D. Enhanced ability to identify bias and improve model governance

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  Explanation

  Increasing model transparency allows stakeholders to better understand how a model makes decisions, making it easier to detect biases and enforce governance practices.