home
Big Data Evolution Spark Basics PySpark RDD DataFrames & Catalyst Basic Operations Data Sources & Formats Advanced Transformations Spark Core & Execution Performance Tuning Structured Streaming Delta Lake / Lakehouse Advanced Projects
About Data Modelling Types of Data Modelling Relational System - Modelling Reporting System - Modelling
Case Studies
LinkedIn System Facebook System WhatsApp System Instagram System Amazon Shopping Ride Hailing (Uber/Ola) Garage System Parking System Netflix System
About Data Governance Objectives of Data Governance Implementation Strategy (How)
Case Studies
Governance First Principles Healthcare Governance Financial Governance Retail Loyalty Governance
Introduction Architecture Virtual Warehouses
Introduction Airflow Architecture Operators & Sensors Advanced Concepts Writing First DAG Production Best Practices
Azure Data Engineer
Evolution of Data Engineering Changing Practices Mastering DE with AI Interview Scenarios AI-DLC Methodology
AWS Lambda Mechanics AWS S3 Case Study DynamoDB Mechanics
diamond Go Premium
person_outline
Hello Guest
Sign in to access all content
Information
About Us Contact Us
Settings & Preferences
App Theme
light_mode Light Mode dark_mode Dark Mode
Account
Sign In / Login
Apache Airflow Airflow
search
keyboard_arrow_down Advanced DAG Concepts & Data Sharing
Data Engineering Path  ·  Airflow
Apache Airflow Logo

Theoretical Quiz — Module 4

📝 Test Your Knowledge

Q1. What is XCom and what are its size limitations?

Show Answer

XCom is a key-value store in the metadata database for sharing small messages between tasks. Size limit depends on the database backend (typically 48 KB for MySQL, 1 GB for PostgreSQL). Never use XCom for large datasets.

Q2. What trigger rule would you use on a join task after a BranchPythonOperator?

Show Answer

trigger_rule="none_failed_min_one_success" — This ensures the join task runs as long as no upstream task failed, even if some were skipped (which is the expected behavior after branching).

Q3. What is Dynamic Task Mapping and how does .expand() work?

Show Answer

Dynamic Task Mapping creates multiple task instances from a single task definition at runtime. .expand(param=list_of_values) creates one task instance per item in the list. This is useful when the number of items to process is not known at DAG parse time.

lock

This content is reserved for Premium Members.

Upgrade to Premium
arrow_back Previous Topic Hands on Quiz Next Topic arrow_forward DAG Structure

Entity Details

Create New Item
help

Submit Technical Query

Have a question or run into an issue? Describe it below, upload an optional screenshot, and our engineering team will answer it!

image Attach image (optional)

Submit Feedback

build Free Developer Utility Free Tool
gavel

Privacy & Legal Disclaimer

1. Client-Side Browser Processing

All utility tools on DeepEngineerHub (including Image to PDF, Text Formatters, JSON Converters, and Encryptors) execute 100% locally within your client browser using WebAssembly and JavaScript. No uploaded images, text, or documents are transmitted, collected, or stored on remote servers.

2. Limitation of Liability ("As-Is" Provision)

Tools and services are provided free of charge for convenience and educational purposes "as-is" without warranties of any kind. DeepEngineerHub shall not be held liable for any data loss, formatting inconsistencies, or indirect damages resulting from tool usage.

3. Open Source & Third-Party Software

Certain utilities utilize open-source client libraries (such as jsPDF, Mermaid.js, Pyodide) licensed under MIT, Apache, or BSD open licenses. All intellectual property remains with their respective copyright holders.