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Microsoft AI-102 exam measures the candidate's ability to design and implement AI solutions that meet business requirements. It covers a wide range of topics, such as data preprocessing, feature engineering, model training, model deployment, and monitoring. Candidates should have a good understanding of machine learning algorithms, programming languages such as Python and R, and Azure services such as Azure Storage, Azure Kubernetes Service, and Azure Functions.
Microsoft AI-102 certification exam is designed for professionals who want to demonstrate their expertise in designing and implementing solutions that use the Microsoft Azure AI platform. AI-102 Exam Tests the candidate's ability to build end-to-end solutions using various Azure AI services, such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service. AI-102 exam also assesses the candidate's understanding of AI concepts, including natural language processing, computer vision, and decision-making.
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Microsoft AI-102 Exam is designed for individuals who have a strong understanding of artificial intelligence (AI) and how it can be implemented in Microsoft Azure. AI-102 exam focuses on the design and implementation of AI solutions that can help businesses improve their operations, increase efficiency, and generate insights from data.
NEW QUESTION # 29
You train a Custom Vision model used in a mobile app.
You receive 1,000 new images that do not have any associated data.
You need to use the images to retrain the model. The solution must minimize how long it takes to retrain the model.
Which three actions should you perform in the Custom Vision portal? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
1 - upload all the images
2 - Get suggested tags
3 - Review the suggestions and confirm the tags
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/suggested-tags
NEW QUESTION # 30
Hotspot Question
You are building a language learning solution.
You need to recommend which Azure services can be used to perform the following tasks:
- Analyze lesson plans submitted by teachers and extract key fields,
such as lesson times and required texts.
- Analyze learning content and provide students with pictures that
represent commonly used words or phrases in the text.
The solution must minimize development effort.
Which Azure service should you recommend for each task? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
https://learn.microsoft.com/en-us/azure/ai-services/immersive-reader/overview#display-pictures- for-common-words
NEW QUESTION # 31
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:
NEW QUESTION # 32
You are building an app that will process incoming email and direct email messages to either French or English language support teams.
Which Azure Cognitive Services API should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 33
You have an existing Azure Cognitive Search service.
You have an Azure Blob storage account that contains millions of scanned documents stored as images and PDFs.
You need to make the scanned documents available to search as quickly as possible. What should you do?
Answer: D
Explanation:
Incorrect Answers:
A: Need more search units to process the data in parallel. B: Run them in parallel, not sequentially.
C: Need a blob indexer.
Note: A blob indexer is used for ingesting content from Azure Blob storage into a Cognitive Search index. Index large datasets Indexing blobs can be a time-consuming process. In cases where you have millions of blobs to index, you can speed up indexing by partitioning your data and using multiple indexers to process the data in parallel. Here's how you can set this up:
Partition your data into multiple blob containers or virtual folders Set up several data sources, one per container or folder.
Create a corresponding indexer for each data source. All of the indexers should point to the same target search index.
One search unit in your service can run one indexer at any given time. Creating multiple indexers as described above is only useful if they actually run in parallel.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-howto-indexing-azure-blob-storage
NEW QUESTION # 34
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