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Describe Azure virtual networking

Azure virtual networks and virtual subnets enable Azure resources, such as VMs, web apps, and databases, to communicate with each other, with users on the internet, and with your on-premises client computers. You can think of an Azure network as an extension of your on-premises network with resources that link other Azure resources.

Azure virtual networks provide the following key networking capabilities:

Isolation and segmentation

Internet communications

Communicate between Azure resources

Communicate with on-premises resources

Route network traffic

Filter network traffic

Connect virtual networks

Azure virtual networking supports both public and private endpoints to enable communication between external or internal resources with other internal resources.

Public endpoints have a public IP address and can be accessed from anywhere in the world.

Private endpoints exist within a virtual network and have a private IP address from within the address space of that virtual network.

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Identify elements of a search solution

A search index contains your searchable content. In an Azure AI Search solution, you create a search index by moving data through the following indexing pipeline:

Start with a data source: the storage location of your original data artifacts, such as PDFs, video files, and images. For Azure AI Search, your data source could be files in Azure Storage, or text in a database such as Azure SQL Database or Azure Cosmos DB.

Indexer: automates the movement data from the data source through document cracking and enrichment to indexing. An indexer automates a portion of data ingestion and exports the original file type to JSON (in an action called JSON serialization).

Document cracking: the indexer opens files and extracts content.

Enrichment: the indexer moves data through AI enrichment, which implements Azure AI on your original data to extract more information. AI enrichment is achieved by adding and combining skills in a skillset. A skillset defines the operations that extract and enrich data to make it searchable. These AI skills can be either built-in skills, such as text translation or Optical Character Recognition (OCR), or custom skills that you provide. Examples of AI enrichment include adding captions to a photo and evaluating text sentiment. AI enriched content can be sent to a knowledge store, which persists output from an AI enrichment pipeline in tables and blobs in Azure Storage for independent analysis or downstream processing.

Push to index: the serialized JSON data populates the search index.

The result is a populated search index which can be explored through queries. When users make a search query such as "coffee", the search engine looks for that information in the search index. A search index has a structure similar to a table, known as the index schema. A typical search index schema contains fields, the field's data type (such as string), and field attributes. The fields store searchable text, and the field attributes allow for actions such as filtering and sorting. 

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Understand authentication for Azure AI services

You've now learned how to create an AI service resource. But how do you ensure that only those authorized have access to your AI service? This is done through authentication, the process of verifying that the user or service is who they say they are, and that they are authorized to use the service.

Most Azure AI services are accessed through a RESTful API, although there are other ways. The API defines what information is passed between two software components: the Azure AI service and whatever is using it. Having a clearly defined interface is important, because if the AI service is updated, your application must continue to work correctly.

Part of what an API does is to handle authentication. Whenever a request is made to use an AI services resource, that request must be authenticated. For example, your subscription and AI service resource is verified to ensure you have sufficient permissions to access it. This authentication process uses an endpoint and a resource key.

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Partitioning the output file

Partitioning is an optimization technique that enables Spark to maximize performance across the worker nodes. More performance gains can be achieved when filtering data in queries by eliminating unnecessary disk IO.

To save a dataframe as a partitioned set of files, use the partitionBy method when writing the data. The following example saves the bikes_df dataframe (which contains the product data for the mountain bikes and road bikes categories), and partitions the data by category:

Python

bikes_df.write.partitionBy("Category").mode("overwrite").parquet("Files/bike_data")

The folder names generated when partitioning a dataframe include the partitioning column name and value in a column=value format, so the code example creates a folder named bike_data that contains the following subfolders:

Category=Mountain Bikes

Category=Road Bikes

Each subfolder contains one or more parquet files with the product data for the appropriate category.

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Data transformations in eventstreams

You can transform the data as it flows in the eventstream, enabling you to filter, summarize, and reshape it before storing it. Available transformations include:

Filter: Use the Filter transformation to filter events based on the value of a field in the input. Depending on the data type (number or text), the transformation keeps the values that match the selected condition, such as is null or is not null.

Manage fields: This transformation allows you to add, remove, change data type, or rename fields coming in from an input or another transformation.

Aggregate: Use the Aggregate transformation to calculate an aggregation (Sum, Minimum, Maximum, or Average) every time a new event occurs over a period of time. This operation also allows for the renaming of these calculated columns, and filtering or slicing the aggregation based on other dimensions in your data. You can have one or more aggregations in the same transformation.

Group by: Use the Group by transformation to calculate aggregations across all events within a certain time window. You can group by the values in one or more fields. It's like the Aggregate transformation allows for the renaming of columns, but provides more options for aggregation and includes more complex options for time windows. Like Aggregate, you can add more than one aggregation per transformation.

Union: Use the Union transformation to connect two or more nodes and add events with shared fields (with the same name and data type) into one table. Fields that don't match are dropped and not included in the output.

Expand: Use this array transformation to create a new row for each value within an array.

Join: this is a transformation to combine data from two streams based on a matching condition between them.

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Take action with Microsoft Fabric Activator

When monitoring surfaces changing data, anomalies, or critical events, alerts are generated or actions are triggered. Real-time data analytics is commonly based on the ingestion and processing of a data stream that consists of a perpetual series of data, typically related to specific point-in-time events. For example, a stream of data from an environmental IoT weather sensor. Real-Time Intelligence in Fabric contains a tool called Activator that can be used to trigger actions on streaming data. For example, a stream of data from an environmental IoT weather sensor might be used to trigger emails to sailors when wind thresholds are met. When certain conditions or logic is met, an action is taken, like alerting users, executing Fabric job items like a pipeline, or kicking off Power Automate workflows. The logic can be either a defined threshold, a pattern like events happening repeatedly over a time period, or the results of logic defined by a Kusto Query Language (KQL) query.

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Use cases

Security Copilot focuses on making the following highlighted use cases easy to use.

Investigate and remediate security threats - gain context for incidents to quickly triage complex security alerts into actionable summaries and remediate quicker with step-by-step response guidance

Build KQL queries or analyze suspicious scripts - eliminate the need to manually write query-language scripts or reverse engineer malware scripts with natural language translation to enable every team member to execute technical tasks

Understand risks and manage security posture of the organization - get a broad picture of your environment with prioritized risks to uncover opportunities to improve posture more easily

Troubleshoot IT issues faster - synthesize relevant information rapidly and receive actionable insights to identify and resolve IT issues quickly

Define and manage security policies - define a new policy, cross-reference it with others for conflicts, and summarize existing policies to manage complex organizational context quickly and easily

Configure secure lifecycle workflows - build groups and set access parameters with step-by-step guidance to ensure a seamless configuration to prevent security vulnerabilities

Develop reports for stakeholders - get a clear and concise report that summarizes the context and environment, open issues, and protective measures prepared for the tone and language of the report’s audience

These use cases represent just a few of the capabilities that Copilot delivers and that helps make analysts more productive and also helps up-level them.