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?
To make the scanned documents available for search as quickly as possible, you should divide the data into multiple virtual folders, create a separate indexer for each folder, and increase the search units. Each indexer will process its respective virtual folder in parallel, utilizing the extra search units to handle the workload more efficiently. This approach leverages the parallel processing capabilities of Azure Cognitive Search and ensures a faster indexing process.
D is correct answer. Also marked correct on Udemy course practice test.
In my opinion, D is correct on this question.
FOCUS "virtual folders" word.
Tricky question think of virtual folder AS blob containers and the answer will be obvious
e, option D is the best choice because it leverages the scalability and parallel processing capabilities of Azure Cognitive Search to efficiently index a large volume of documents. By organizing documents into virtual folders and creating an indexer for each folder, you can maximize the throughput of the indexing process. Increasing search units further supports this by allocating more resources to the task, thereby minimizing the time required to make the scanned documents searchable.
I think correct answer is D https://learn.microsoft.com/en-us/azure/search/search-howto-large-index#run-indexers-in-parallel
D is the answer. https://learn.microsoft.com/en-us/azure/search/search-howto-large-index#run-indexers-in-parallel If you partition your data, you can create multiple indexer-data-source combinations that pull from each data source and write to the same search index. Because each indexer is distinct, you can run them at the same time, populating a search index more quickly than if you ran them sequentially. Make sure you have sufficient capacity. One search unit in your service can run one indexer at any given time. Creating multiple indexers is only useful if they can run in parallel.
If your data source is an Azure Blob Storage container or Azure Data Lake Storage Gen 2, enumerating a large number of blobs can take a long time (even hours) until this operation is completed. This will cause that your indexer's documents succeeded count isn't increased during that time and it may seem it's not making any progress, when it is. If you would like document processing to go faster for a large number of blobs, consider partitioning your data into multiple containers and create parallel indexers pointing to a single index.
Was on exam 7 Jun 2022
how to do this is defined here: https://docs.microsoft.com/en-us/azure/search/search-howto-indexing-azure-blob-storage#index-large-datasets The response is missing the data source creation for each virtual folder or blob container. D is not correct, but the less wrong of a response… So I give it a "pass", nowadays it is misleading and not fully correct...
seems to be correct
Selected Answer: D
D makes sense. "virtual folders".
"One search unit in your service can run one indexer at any given time. Creating multiple indexers is only useful if they can run in parallel" so A and C are out. B is out as you are not running the indexers in parallel. Besides it's hard to image that with millions of scanned you don't have virtual folders in place to split the data already.
correct ans