Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 141:

  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?

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

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  Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.

  Option A (Correct): "Create software snippets, reference tracking, and open- source license tracking":This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.

  Option B:"Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.

  Option C:"Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.

  Option D:"Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.

  References:

  Amazon Q Developer Features:AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

• Question 142:

  Which foundation model (FM) in Amazon Bedrock can be fine-tuned for text, image, and video comprehension?

  A. Amazon Nova Pro
  B. Amazon Titan Multimodal Embeddings G1
  C. Amazon Titan Text G1 - Express
  D. Amazon Nova Micro
  A. Amazon Nova Pro

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  Explanation

  Amazon Nova Pro supports multimodal capabilities and can be fine-tuned for understanding and generating insights across text, image, and video inputs, making it suitable for comprehensive multimodal comprehension tasks.

• Question 143:

  An ecommerce company wants to improve search engine recommendations by customizing the results for each user of the company's ecommerce platform. Which AWS service meets these requirements?

  A. Amazon Personalize
  B. Amazon Kendra
  C. Amazon Rekognition
  D. Amazon Transcribe
  A. Amazon Personalize

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  Amazon Personalize is an AWS service specifically designed to build and deploy personalized recommendations for users. It allows you to create custom machine learning models for personalized user experiences, such as product recommendations, search results, or content suggestions. For an ecommerce company aiming to improve search engine recommendations tailored to each user, Amazon Personalize is the ideal choice as it leverages user behavior data and machine learning to deliver highly relevant recommendations.

• Question 144:

  A company wants to analyze customer reviews and identify whether each review expresses positive, negative, or neutral sentiment.

  Which AWS service should the company use?

  A. Amazon Comprehend
  B. Amazon SageMaker Model Monitor
  C. Amazon CloudWatch
  D. Amazon Polly
  A. Amazon Comprehend

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  Explanation

  Amazon Comprehend is the correct answer because it provides natural language processing capabilities, including sentiment analysis for text.

  Option A (Correct): "Amazon Comprehend": This is correct because it can analyze text and determine sentiment.

  Option B: "Amazon SageMaker Model Monitor" is incorrect because it monitors model quality in production.

  Option C: "Amazon CloudWatch" is incorrect because it is used for monitoring metrics and logs.

  Option D: "Amazon Polly" is incorrect because it converts text to speech.

• Question 145:

  A company is building a generative AI tool. The company will use internal documents to customize a foundation model (FM). Which approach will meet this requirement?

  A. Classification
  B. Continued pre-training
  C. Distillation
  D. Regression
  B. Continued pre-training

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  Continued pre-training involves further training a foundation model (FM) on internal documents, allowing the model to better understand and generate content specific to the company's context and requirements. This approach customizes the FM using the company's own data.

• Question 146:

  A financial company has deployed an ML model to predict customer churn. The model has been running in production for 1 week. The company wants to evaluate how accurately the model predicts churn compared to actual customer behavior. Which metric meets these requirements?

  A. Root mean squared error (RMSE)
  B. Return on investment (ROI)
  C. F1 score
  D. Bilingual Evaluation Understudy (BLEU) score
  C. F1 score

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  The F1 score combines precision (how many predicted churners actually churned) and recall (how many actual churners were correctly identified) into a single metric, making it ideal for measuring your model's accuracy on the binary churn prediction task.

• Question 147:

  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?

  A. Use supervised learning to create a regression model that will predict probability.
  B. Use reinforcement learning to train a model to return the probability.
  C. Use code that will calculate probability by using simple rules and computations.
  D. Use unsupervised learning to create a model that will estimate probability density.
  C. Use code that will calculate probability by using simple rules and computations.

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  The problem involves a simple probability calculation that can be handled efficiently by straightforward mathematical rules and computations. Using machine learning techniques would introduce unnecessary complexity and operational overhead.

  Option C (Correct): "Use code that will calculate probability by using simple rules and computations":This is the correct answer because it directly solves the problem with minimal overhead, using basic probability rules.

  Option A:"Use supervised learning to create a regression model" is incorrect as it overcomplicates the solution for a simple probability problem.

  Option B:"Use reinforcement learning to train a model" is incorrect because reinforcement learning is not needed for a simple probability calculation.

  Option D:"Use unsupervised learning to create a model" is incorrect as unsupervised learning is not applicable to this task.

  References:

  Choosing the Right Solution for AI Tasks:AWS recommends using the simplest and most efficient approach to solve a given problem, avoiding unnecessary machine learning techniques for straightforward tasks.

• Question 148:

  An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance. Which metric will help the AI practitioner evaluate the performance of the model?

  A. Confusion matrix
  B. Correlation matrix
  C. R2 score
  D. Mean squared error (MSE)
  A. Confusion matrix

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  A confusion matrix is the correct metric for evaluating the performance of a classification model, such as the deep learning model built to classify types of materials in images.

• Question 149:

  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?

  A. Real-time inference
  B. Serverless inference
  C. Asynchronous inference
  D. Batch transform
  C. Asynchronous inference

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  Asynchronous inference is suitable for handling large input data and long processing times while still providing responses without blocking other requests. It allows for near real-time latency, whereas the other options are less suitable given the input size and processing time constraints.

• Question 150:

  Which type of AI model makes numeric predictions?

  A. Diffusion
  B. Regression
  C. Transformer
  D. Multi-modal
  B. Regression

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  Regression models are designed to predict continuous numerical values (for example, forecasting sales figures or estimating house prices), making them the appropriate choice for numeric predictions.