The company receives data from multiple sources, generates business metrics, and uses AI agents to automate processes. It is necessary to design and implement a basic data and AI pipeline Part 1. There is a REST API at https://api.example.com/ads The API returns objects with the following fields date campaign_id spend clicks It is necessary to implement a Python script that performs the following actions: Gets data for the last 7 days Handles API errors correctly Logs execution statuses Saves data to a CSV file or to a database Part 2. There is an orders table with the following fields: order_id user_id order_date revenue It is necessary to write SQL queries that calculate the following indicators: Revenue by day Number of orders by day ARPU Number of repeat orders Part 3. It is necessary to describe the DAG in Apache Airflow, which consists of the following steps: Getting data from the advertising API Getting order data from a database Transformation and calculation of daily metrics Uploading aggregated data to the storage You need to set up: Launch schedule Retreats Logging and error handling Part 4. It is necessary to describe the structure of the daily_businessmetrics table The table should contain the following fields: date revenue orders_count ad_spend roi It is necessary to describe the formulas for calculating each indicator Part 5. It is necessary to describe the concept of an AI agent that uses data from the daily_business_metrics showcase and performs one of the following business tasks: Control of revenue anomalies Monitoring the effectiveness of advertising Notification of a drop in ROI Generating daily business reports You need to describe: What data does the agent use? How often does it work What decisions does he make Where is sending the result of the work It is necessary to develop criteria for evaluating the agent's performance.