Which machine learning technique can be used for anomaly detection?

• A.A machine learning technique that understands written and spoken language.
• B.A machine learning technique that classifies objects based on user supplied images.
• C.A machine learning technique that analyzes data over time and identifies unusual changes.
• D.A machine learning technique that classifies images based on their contents.

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Which scenario is an example of a webchat bot?

• A.From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.
• B.Determine whether reviews entered on a website for a concert are positive or negative, and then add a thumbs up or thumbs down emoji to the reviews.
• C.Translate into English questions entered by customers at a kiosk so that the appropriate person can call the customers back.
• D.Accept questions through email, and then route the email messages to the correct person based on the content of the message.

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To complete the sentence, select the appropriate option in the answer area.
Using Recency, Frequency, and Monetary (RFM) values to identify segments of a customer base is an example of___________.

Correct Answer:

Classification
Explanation:

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You have insurance claim reports that are stored as text.
You need to extract key terms from the reports to generate summaries.
Which type of Al workload should you use?

• A.conversational Al
• B.anomaly detection
• C.natural language processing
• D.computer vision

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To complete the sentence, select the appropriate option in the answer area.

Correct Answer:

Explanation
Reliability & Safety
https://en.wikipedia.org/wiki/Tay_(bot)
"To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It's also important to be able to verify that these systems are behaving as intended under actual operating conditions. How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing. We believe that rigorous testing is essential during system development and deployment to ensure AI systems can respond safely in unanticipated situations and edge cases, don't have unexpected performance failures, and don't evolve in ways that are inconsistent with original expectations"

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