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Free Amazon AIF-C01 Exam Questions (page: 14)

Page 13 of 68
PDF with 334 Questions

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?

  1. 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.
  2. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
  3. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
  4. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

Answer(s): D

Explanation:

Amazon SageMaker Canvas is a no-code tool that allows users to build ML models and make predictions without requiring programming knowledge. It is ideal for users with no coding experience, providing an easy interface for importing data and generating predictive models. The other options require more technical expertise or are not designed for no-code model building.



A company has installed a security camera. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.

Which type of bias is affecting the model output?

  1. Measurement bias
  2. Sampling bias
  3. Observer bias
  4. Confirmation bias

Answer(s): B

Explanation:

Sampling bias occurs when the training data is not representative of the overall population, leading to disproportionate flagging of specific groups. In this case, the model may have been trained on biased data that did not adequately represent all ethnic groups, resulting in skewed predictions. The other types of bias do not directly apply to the selection of training data or its representativeness.



A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.

Which AI learning strategy provides this self-improvement capability?

  1. Supervised learning with a manually curated dataset of good responses and bad responses
  2. Reinforcement learning with rewards for positive customer feedback
  3. Unsupervised learning to find clusters of similar customer inquiries
  4. Supervised learning with a continuously updated FAQ database

Answer(s): B

Explanation:

Reinforcement learning allows the chatbot to learn from interactions by receiving rewards for positive customer feedback, which helps the model self-improve over time. The other options do not directly provide a mechanism for continuous self-improvement based on interactions.



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?

  1. Confusion matrix
  2. Correlation matrix
  3. R2 score
  4. Mean squared error (MSE)

Answer(s): A

Explanation:

A confusion matrix provides detailed insights into the performance of a classification model by showing the true positives, false positives, true negatives, and false negatives. This metric helps evaluate how well the model classifies the different types of materials in images. The other metrics are not as suitable for evaluating a classification model.



A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.

Which solution will meet these requirements?

  1. Implement moderation APIs.
  2. Retrain the model with a general public dataset.
  3. Perform model validation.
  4. Automate user feedback integration.

Answer(s): A

Explanation:

Implementing moderation APIs can help filter and block inappropriate or unwanted images before they are returned by the chatbot. The other options do not directly address ensuring that the chatbot avoids returning inappropriate images.



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