AI-900 Microsoft Azure AI Fundamentals Exam Questions and Answers – Page 3

The latest Microsoft AI-900 Azure AI Fundamentals certification actual real practice exam question and answer (Q&A) dumps are available free, which are helpful for you to pass the Microsoft AI-900 Azure AI Fundamentals exam and earn Microsoft AI-900 Azure AI Fundamentals certification.

Question 201

Conversational AI

Answer: An automated chat to answer questions about refunds and exchange

Question 202

The handling of unusual or missing values provided to an AI system is a consideration for the Microsoft _____ principle for responsible AI.

A. Inclusiveness
B. Privacy and security
C. Reliability and safety
D. Transparency

Explanation
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used. At Microsoft, we are continuing to research privacy and security breakthroughs (see next unit) and invest in robust compliance processes to ensure that data collected and used by our AI systems is handled responsibly.

Reliability and safety: AI systems need to be reliable and safe in order to be trusted. It is important for a system to perform as it was originally designed and for it to respond safely to new situations. Its inherent resilience should resist intended or unintended manipulation. Rigorous testing and validation should be established for operating conditions to ensure that the system responds safely to edge cases, and A/B testing and champion/challenger methods should be integrated into the evaluation process. An AI system’s performance can degrade over time, so a robust monitoring and model tracking process needs to be established to reactively and proactively measure the model’s performance and retrain it, as necessary, to modernize it.

Question 203

Predicting whether a patient will develop diabetes based on the patients medical history is an example of anomaly detection

Answer: No

Question 204

Identifying suspicious sign-ins by looking for deviations from usual patterns is an example of anomaly detection

Answer: Yes

Question 205

Forecasting housing prices based on historical data is an example of anomaly detection

Answer: No

Question 206

You build a machine learning model by using the automate machine learning user interface (UI). You need to ensure that the model meets the Microsoft transparency principle for responsible AI. What should you do?

Answer: Enable explain best model (ML ‘Black box’)

Question 207

You are developing a model to predict events by using classification. You have a confusion matrix for the model scored on test data as shown in the following exhibit
There are ____ false negatives

Answer: 1 033

Question 208

You are developing a model to predict events by using classification. You have a confusion matrix for the model scored on test data as shown in the following exhibit
There are ____ correctly predicted positives

Answer: 11

Question 209

For machine learning progress, how should you split data for training and evaluation

Answer: Randomly split the data into rows for training and rows for evaluation

Question 210

A company employs a team of customer service agents to provide telephone and email support to customers. The company develops a webchat bot to provide automated answers to common customer queries. Which business benefit should the company expect as a result of creating the webchat bot solution

Answer: Reduced workload for the customer service agents