Win IT Exam with Last Dumps 2024

Projections are the physical tables, objects, and files in a knowledge store that accept content from a Cognitive Search AI enrichment pipeline. If you're creating a knowledge store, defining and shaping projections is most of the work.
Objects is used when you need the full JSON representation of your data and enrichments in one JSON document. As with table projections, only valid JSON objects can be projected as objects, and shaping can help you do that.
Note: Form Recognizer analyzes your forms and documents, extracts text and data, maps field relationships as key-value pairs, and returns a structured JSON output. You quickly get accurate results that are tailored to your specific content without excessive manual intervention or extensive data science expertise.
Incorrect:
Not Tables: Tables is used for data that's best represented as rows and columns, or whenever you need granular representations of your data (for example, as data frames). Table projections allow you to define a schematized shape, using a Shaper skill or use inline shaping to specify columns and rows.
Not File: File is used when you need to save normalized, binary image files.
Reference:
https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview

A simple fix to most throttling issues is to throw more resources at the search service (typically replicas for query-based throttling, or partitions for indexing-based throttling). However, increasing replicas or partitions adds cost, which is why it is important to know the reason why throttling is occurring at all.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-performance-analysis

Step 1: Sign in to the QnA portal.
Step 2: Create an Azure Cognitive multi-service resource:

Step 3: On the Create page, provide the following information.
Name: Text12345678 -

Step 4: Configure additional settings for your resource as needed, read and accept the conditions (as applicable), and then select Review + create.
Step 5: Navigate to the Azure portal. Then locate and select The Text Analytics service resource Text12345678 (which you created in Step 4).
Step 6: Next, from the left-hand navigation menu, locate Monitoring and select Diagnostic settings. This screen contains all previously created diagnostic settings for this resource.
Step 7: Select + Add diagnostic setting.
Step 8: When prompted to configure, select the storage account and OMS workspace that you'd like to use to store you diagnostic logs. Note: If you don't have a storage account or OMS workspace, follow the prompts to create one.
Step 9: Select Audit, RequestResponse, and AllMetrics. Then set the retention period for your diagnostic log data. If a retention policy is set to zero, events for that log category are stored indefinitely.
Step 10: Click Save.
It can take up to two hours before logging data is available to query and analyze. So don't worry if you don't see anything right away.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account
https://docs.microsoft.com/en-us/azure/cognitive-services/diagnostic-logging

Part 1: Create a search service search12345678
Step 1: Sign in to the QnA portal.
Step 2: Create an Azure Cognitive multi-service resource:

Step 3: On the Create page, provide the following information.
Name: search12345678 -

Step 4: Click Review + create -
Part 2: Start the Import data wizard and create a data source
Step 5: Click Import data on the command bar to create and populate a search index.

Step 6: In the wizard, click Connect to your data > Samples > hotels-sample. This data source is built-in. If you were creating your own data source, you would need to specify a name, type, and connection information. Once created, it becomes an "existing data source" that can be reused in other import operations.

Step 7: Continue to the next page.
Step 8: Skip the "Enrich content" page
Step 9: Configure index.
Make sure English is selected for the fields.

Step 10: Continue and finish the wizard.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account
https://docs.microsoft.com/en-us/azure/search/search-get-started-portal

Part 1: Create a search service captions12345678
Step 1: Sign in to the QnA portal.
Step 2: Create an Azure Cognitive multi-service resource:

Step 3: On the Create page, provide the following information.
Name: captions12345678ֲ¨
Pricing tier: Free -

Step 4: Click Review + create -
(Step 5: Create a data source
In Connect to your data, choose Azure Blob Storage. Choose an existing connection to the storage account and container you created. Give the data source a name, and use default values for the rest.)

Reference:
https://docs.microsoft.com/en-us/azure/search/search-create-service-portal
https://docs.microsoft.com/en-us/azure/search/cognitive-search-quickstart-ocr

Step 1: Sign in to the Azure Portal.
Step 2: Navigate to the Form Recognizer Sample Tool (at https://fott-2-1.azurewebsites.net)
Step 3: On the sample tool home page select Use prebuilt model to get data.

Step 4: Select the Form Type you would like to analyze from the dropdown window.
Step 5: In the Source: URL field, paste the selected URL and select the Fetch button.
Step 6: In the Choose file for analysis use the file in the C:\Resources\Invoices folder and select the Fetch button.

Step 7: Select Run analysis. The Form Recognizer Sample Labeling tool will call the Analyze Prebuilt API and analyze the document.
Step 8: View the results - see the key-value pairs extracted, line items, highlighted text extracted and tables detected.

Step 9: Save the results as C:\Resources\Invoices\Results.json.
Reference:
https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/quickstarts/try-sample-label-tool

Face API is an AI service that analyzes faces in images.
Embed facial recognition into your apps for a seamless and highly secured user experience. No machine-learning expertise is required. Features include face detection that perceives facial features and attributes - such as a face mask, glasses, or face location - in an image, and identification of a person by a match to your private repository or via photo ID.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/

Fields have data types and attributes. The check boxes across the top are index attributes controlling how the field is used.
* Retrievable means that it shows up in search results list. You can mark individual fields as off limits for search results by clearing this checkbox, for example for fields used only in filter expressions.
* Filterable, Sortable, and Facetable determine whether fields are used in a filter, sort, or faceted navigation structure.
* Searchable means that a field is included in full text search. Strings are searchable. Numeric fields and Boolean fields are often marked as not searchable.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-get-started-portal

Step 1: In the Azure dashboard, click Create a resource.
Step 2: In the search bar, type "Cognitive Services."
You'll get information about the cognitive services resource and a legal notice. Click Create.
Step 3: You'll need to specify the following details about the cognitive service (refer to the image below for a completed example of this page):
Subscription: choose your paid or trial subscription, depending on how you created your Azure account.
Resource group: click create new to create a new resource group or choose an existing one.
Region: choose the Azure region for your cognitive service. Choose: East US Azure region.
Name: choose a name for your cognitive service. Enter: AAA12345678
Pricing Tier: Select: Free pricing tier

Step 4: Review and create the resource, and wait for deployment to complete. Then go to the deployed resource.
Note: The Computer Vision Image Analysis service can extract a wide variety of visual features from your images. For example, it can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Tag visual features -
Identify and tag visual features in an image, from a set of thousands of recognizable objects, living things, scenery, and actions. When the tags are ambiguous or not common knowledge, the API response provides hints to clarify the context of the tag. Tagging isn't limited to the main subject, such as a person in the foreground, but also includes the setting (indoor or outdoor), furniture, tools, plants, animals, accessories, gadgets, and so on.
Try out the image tagging features quickly and easily in your browser using Vision Studio.
Reference:
https://docs.microsoft.com/en-us/learn/modules/analyze-images-computer-vision/3-analyze-images
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-image-analysis

Step 1: In the Azure dashboard, click Create a resource.
Step 2: In the search bar, type "Cognitive Services."
You'll get information about the cognitive services resource and a legal notice. Click Create.
Step 3: You'll need to specify the following details about the cognitive service (refer to the image below for a completed example of this page):
Subscription: choose your paid or trial subscription, depending on how you created your Azure account.
Resource group: click create new to create a new resource group or choose an existing one.
Region: choose the Azure region for your cognitive service. Choose: East US Azure region.
Name: choose a name for your cognitive service. Enter: AAA12345678
Pricing Tier: Select: Free pricing tier
Step 4: Review and create the resource, and wait for deployment to complete. Then go to the deployed resource.
Note: The Computer Vision Image Analysis service can extract a wide variety of visual features from your images. For example, it can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Tag visual features -
Identify and tag visual features in an image, from a set of thousands of recognizable objects, living things, scenery, and actions. When the tags are ambiguous or not common knowledge, the API response provides hints to clarify the context of the tag. Tagging isn't limited to the main subject, such as a person in the foreground, but also includes the setting (indoor or outdoor), furniture, tools, plants, animals, accessories, gadgets, and so on.
Try out the image tagging features quickly and easily in your browser using Vision Studio.
Reference:
https://docs.microsoft.com/en-us/learn/modules/analyze-images-computer-vision/3-analyze-images
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-image-analysis