AI-900 : Microsoft Azure AI Fundamentals : Part 01

AI-900 : Microsoft Azure AI Fundamentals : Part 01

1. 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?

   • increased sales
   • a reduced workload for the customer service agents
   • improved product reliability

2. For a machine learning progress, how should you split data for training and evaluation?

   • Use features for training and labels for evaluation.
   • Randomly split the data into rows for training and rows for evaluation.
   • Use labels for training and features for evaluation.
   • Randomly split the data into columns for training and columns for evaluation.
   Explanation:

   The Split Data module is particularly useful when you need to separate data into training and testing sets. Use the Split Rows option if you want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50. You can also randomize the selection of rows in each group, and use stratified sampling.

3. HOTSPOT

   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.

   

   Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.

   NOTE: Each correct selection is worth one point.

   

   

   Explanation:

   Box 1: 11

   

   TP = True Positive.
   The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).

   Box 2: 1,033
   FN = False Negative

4. You build a machine learning model by using the automated machine learning user interface (UI).

   You need to ensure that the model meets the Microsoft transparency principle for responsible AI.

   What should you do?

   • Set Validation type to Auto.
   • Enable Explain best model.
   • Set Primary metric to accuracy.
   • Set Max concurrent iterations to 0.
   Explanation:
   Model Explain Ability.
   Most businesses run on trust and being able to open the ML “black box” helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.

5. HOTSPOT

   For each of the following statements, select Yes if the statement is true. Otherwise, select No.

   NOTE: Each correct selection is worth one point.

   

   

   Explanation:

   Anomaly detection encompasses many important tasks in machine learning:
   Identifying transactions that are potentially fraudulent.
   Learning patterns that indicate that a network intrusion has occurred.
   Finding abnormal clusters of patients.
   Checking values entered into a system.

6. HOTSPOT

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

   

   

   Explanation:

   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.

7. DRAG DROP

   Match the types of AI workloads to the appropriate scenarios.

   To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.

   NOTE: Each correct selection is worth one point.

   

   AI-900 Part 01 Q07 006 Answer

   Explanation:
   Box 3: Natural language processing
   Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.

8. You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.

   This is an example of which Microsoft guiding principle for responsible AI?

   • fairness
   • inclusiveness
   • reliability and safety
   • accountability
   Explanation:

   Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.

9. DRAG DROP

   Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.

   To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.

   

   

   NOTE: Each correct selection is worth one point.

   Explanation:

   Box 1: Reliability and safety
   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.

   Box 2: Accountability
   The people who design and deploy AI systems must be accountable for how their systems operate. Organizations should draw upon industry standards to develop accountability norms. These norms can ensure that AI systems are not the final authority on any decision that impacts people’s lives and that humans maintain meaningful control over otherwise highly autonomous AI systems.

   Box 3: Privacy and security
   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

10. HOTSPOT

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

    

    

    Explanation:

    Reliability and safety: 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.

11. You are building an AI system.

    Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?

    • Ensure that all visuals have an associated text that can be read by a screen reader.
    • Enable autoscaling to ensure that a service scales based on demand.
    • Provide documentation to help developers debug code.
    • Ensure that a training dataset is representative of the population.

12. DRAG DROP

    Match the types of AI workloads to the appropriate scenarios.To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.

    

    

    NOTE: Each correct selection is worth one point.

13. Your company is exploring the use of voice recognition technologies in its smart home devices. The company wants to identify any barriers that might unintentionally leave out specific user groups.

    This an example of which Microsoft guiding principle for responsible AI?

    • accountability
    • fairness
    • inclusiveness
    • privacy and security

14. What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution.

    NOTE: Each correct selection is worth one point.

    • knowledgeability
    • decisiveness
    • inclusiveness
    • fairness
    • opinionatedness
    • reliability and safety

15. HOTSPOT

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

    

    

16. HOTSPOT

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

    

    

17. You run a charity event that involves posting photos of people wearing sunglasses on Twitter.

    You need to ensure that you only retweet photos that meet the following requirements:

    – Include one or more faces.
    – Contain at least one person wearing sunglasses.

    What should you use to analyze the images?

    • the Verify operation in the Face service
    • the Detect operation in the Face service
    • the Describe Image operation in the Computer Vision service
    • the Analyze Image operation in the Computer Vision service

18. Which metric can you use to evaluate a classification model?

    • true positive rate
    • mean absolute error (MAE)
    • coefficient of determination (R2)
    • root mean squared error (RMSE)
    Explanation:

    What does a good model look like?
    An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.

19. Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution.

    NOTE: Each correct selection is worth one point.

    • dataset
    • compute
    • pipeline
    • module
    Explanation:

    You can drag-and-drop datasets and modules onto the canvas.

20. You need to create a training dataset and validation dataset from an existing dataset.

    Which module in the Azure Machine Learning designer should you use?

    • Select Columns in Dataset
    • Add Rows
    • Split Data
    • Join Data
    Explanation:

    A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data.
    Use the Split Data module to divide a dataset into two distinct sets.
    The studio currently supports training/validation data splits