Identify Features of Generative AI Solutions AI-900 Practice Questions (Page 4)

Questions 16–20

1. 16application · medium

   An architecture firm wants to generate multiple 3D building layouts for a new development project. They need to explore many design variations quickly. Which generative AI use case does this represent?

   • ASimulation and modeling
   • BData augmentation
   • CContent creation for marketing
   • DText summarization
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2. 17application · medium

   An automotive company wants to simulate pedestrian behavior in rare traffic scenarios to test their autonomous driving system. They need to generate realistic synthetic video sequences of pedestrians crossing roads in unusual ways. Which generative AI approach should they use?

   • AUse a regression model to predict pedestrian positions in existing videos.
   • BUse a generative model to create synthetic video frames of pedestrians in the required scenarios.
   • CUse a clustering algorithm to group similar pedestrian behaviors.
   • DUse a classification model to label existing videos of pedestrians.
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4. 18foundation · easy

   How does generative AI create new text content?

   • ABy predicting the next word in a sequence based on learned patterns
   • BBy applying grammatical rules to a set of keywords
   • CBy retrieving and reordering sentences from a fixed database
   • DBy copying and pasting from existing documents
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5. 19application · medium

   A company is using a generative AI model to create job description templates. They notice that the generated text tends to use masculine pronouns more often than feminine pronouns for technical roles. What should they do to address this bias?

   • AUse a different generative model type, such as a VAE, to avoid bias.
   • BIgnore the bias because it is only a small percentage of the output.
   • CEvaluate the model's outputs for bias and retrain or fine-tune the model with a more balanced dataset.
   • DManually correct each generated description after generation.
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6. 20application · medium

   A bank wants to use generative AI to create synthetic transaction data for training a fraud detection model. They need the synthetic data to be realistic and not expose real customer information. What is the most important consideration?

   • AGenerate data that is identical to real transactions to maximize accuracy.
   • BEnsure the synthetic data does not contain any real customer personal data.
   • CMake the synthetic data as large as possible to improve model accuracy.
   • DUse a simple generative model to avoid overfitting.
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