Apache Airflow Certification Practice Test 07
Architect Review Board Questions
Apache Airflow Certification Practice Test 07
Architect Review Board Questions
Insightful Saga β Modern Data Engineering Certification Preparation
Question 121
A newly joined Airflow Architect reviews a retail company's orchestration platform. Current State: - 600 DAGs - 40 developers - Multiple business units - Frequent code duplication Developers often copy entire DAGs and modify only a few parameters. Leadership wants lower maintenance costs. What is the BEST recommendation?
Question 122
A banking company operates Airflow across: - Development - QA - Production Engineers frequently make direct production changes. Several recent incidents were caused by untested deployments. What should be implemented first?
Question 123
A healthcare company expects workflow volume to triple within the next year. Current Airflow workers frequently remain at 90% utilization. What should architects evaluate first?
Question 124
An airline company runs hundreds of independent DAGs. Some DAGs process a few tasks. Others process thousands of records across many systems. Administrators complain scheduling delays are increasing. What platform component should be reviewed first?
Question 125
A global retailer wants engineering teams to create workflows consistently. Current problems: - Different naming styles - Different alerting approaches - Different retry strategies What should be established?
Question 126
A company wants one DAG template that can process: - Sales Data - Customer Data - Product Data - Inventory Data without creating separate DAG definitions. What Airflow capability should architects explore?
Question 127
A telecom company processes data from 5,000 stores. The number of data files changes every day. Hardcoded tasks cannot keep up with variable workloads. What approach is best?
Question 128
A financial institution wants Airflow deployments reviewed before production releases. Which governance process should be implemented?
Question 129
A newly hired Airflow Architect discovers that workflow failures are often reported by business users before engineers notice them. What capability is most urgently needed?
Question 130
A manufacturing company wants workflow metrics centrally available for all teams. Leadership requests visibility into: - Success Rates - Failure Rates - Runtime Trends - SLA Compliance What should be implemented?
Question 131
A company wants stronger disaster recovery procedures for Airflow. Leadership asks: "What happens if the Airflow environment becomes unavailable?" What should architects define first?
Question 132
A platform team wants teams to onboard new DAGs rapidly. Current onboarding takes several weeks. What investment would provide the biggest improvement?
Question 133
A retail company has dozens of DAGs using hardcoded retry values. Architects want consistent recovery behavior. What should be standardized?
Question 134
A healthcare organization must ensure all workflows handling patient data follow identical security standards. What should be implemented?
Question 135
A company wants Airflow to support future growth without requiring major redesigns. What architecture principle should guide designs?
Question 136
A global enterprise wants every Airflow workflow traceable from: Source System β Processing β Target System What capability should become a strategic priority?
Question 137
A support team spends significant time manually investigating recurring workflow failures. What long-term improvement should be prioritized?
Question 138
An insurance company wants all Airflow environments reviewed against common architecture standards. What should be established?
Question 139
A multi-country company wants individual teams to own workflows while still following enterprise standards. What operating approach fits best?
Question 140
A newly hired Principal Airflow Architect is asked: βWhat is the ultimate goal of an enterprise Airflow platform?β
Enterprise Airflow platforms exist to provide dependable orchestration, operational visibility, governance, scalability, and business continuity across data workflows.