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The AI-102 certification exam covers a wide range of topics, including Azure Cognitive Services, Natural Language Processing (NLP), and Computer Vision. You will be tested on your ability to develop and implement AI solutions using various Azure services and tools.
To prepare for the Microsoft AI-102 exam, candidates should have a strong understanding of Azure services and AI concepts. They should also have experience in designing and implementing AI solutions, as well as knowledge of programming languages such as Python and C#. In addition, candidates should be familiar with machine learning concepts such as supervised and unsupervised learning, deep learning, and reinforcement learning. With the Microsoft AI-102 Certification, professionals can demonstrate their expertise in AI solution development and implementation on the Azure platform, opening up opportunities for career advancement and higher salaries.
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The Microsoft AI-102 Exam covers a wide range of topics, including natural language processing, computer vision, and speech recognition. Candidates are required to have a thorough understanding of Azure Cognitive Services, Bot Services, and Azure Machine Learning. They must also be able to design and implement AI solutions that are scalable, secure, and reliable.
NEW QUESTION # 16
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 17
You have the following C# method.
You need to deploy an Azure resource to the East US Azure region. The resource will be used to perform sentiment analysis.
How should you call the method?
Answer: D
Explanation:
To perform sentiment analysis, we specify TextAnalytics, not ContentModerator.
Possible SKU names include: 'F0','F1','S0','S1','S2','S3','S4','S5','S6','S7','S8' Possible location names include: westus, eastus Reference:
https://docs.microsoft.com/en-us/powershell/module/az.cognitiveservices/new-azcognitiveservicesaccount
NEW QUESTION # 18
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Normalization involves eliminating relationships between database tables.
* No
* Normalization does not eliminate relationships. In fact, it often creates relationships between tables by splitting data into smaller, related tables.
* Normalizing a database reduces data redundancy.
* Yes
* The primary goal of normalization is to minimize duplicate data and ensure that each fact is stored only once.
* Normalization improves data integrity.
* Yes
* By reducing redundancy and ensuring data is stored in a structured manner, normalization enforces consistency and integrity across the database.
* No
* Yes
* Yes
Correct Responses:
* Database normalization basics
* Database design and normalization
Microsoft References
NEW QUESTION # 19
You are building a retail chatbot that will use a QnA Maker service.
You upload an internal support document to train the model. The document contains the following question: "What is your warranty period?" Users report that the chatbot returns the default QnA Maker answer when they ask the following question: "How long is the warranty coverage?" The chatbot returns the correct answer when the users ask the following question: 'What is your warranty period?" Both questions should return the same answer.
You need to increase the accuracy of the chatbot responses.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. (Choose three.)
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/edit-knowledge-base
NEW QUESTION # 20
Hotspot Question
You have an Azure subscription that contains an Azure AI Content Safety resource named resource1.
You are building an app that will analyze text by using resource1.
You need to identify text that contains hateful content.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
"categories_analysis"
The categories_analysis property contains categorized insights into the type of content detected in the text, including hate speech.
blocklist_match would be used if the system were matching against a predefined blocklist, which is not the case here.
content is too broad and does not specifically analyze the detected category.
"{hate_result.severity}"
severity represents the intensity or degree of hateful content detected in the text.
This is the most relevant choice to quantify and report the level of hate detected.
Other options like blocklist_item_id or blocklist_item_text are only applicable when matching against blocklists.
NEW QUESTION # 21
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