Apache Airflow Certification Practice Test 06
Statement Analysis Questions
Apache Airflow Certification Practice Test 06
Statement Analysis Questions
Insightful Saga — Modern Data Engineering Certification Preparation
Question 101
Statement 1: A DAG defines the workflow structure and task dependencies. Statement 2: A DAG stores data generated by workflow execution.
DAGs define workflow orchestration but do not store business data.
Question 102
Statement 1: The Airflow Scheduler determines when tasks should run. Statement 2: The Scheduler executes task logic directly.
Question 103
Statement 1: Workers execute scheduled tasks. Statement 2: Workers are responsible for storing Airflow metadata.
Question 104
Statement 1: XCom is designed for passing small pieces of data between tasks. Statement 2: XCom is intended for transferring large datasets between tasks.
Question 105
Statement 1: Variables help manage environment-specific configuration values. Statement 2: Variables are commonly used to avoid hardcoding configuration values.
Question 106
Statement 1: Sensors can wait for external events before allowing downstream execution. Statement 2: Sensors permanently stop DAG execution until manually resumed.
Question 107
Statement 1: Pools help control concurrency for shared resources. Statement 2: Pools can be used to prevent overwhelming external systems.
Question 108
Statement 1: Connections store integration details for external systems. Statement 2: Connections eliminate the need for credentials entirely.
Question 109
Statement 1: Retries can improve resilience against temporary failures. Statement 2: Retries solve all workflow failures automatically.
Question 110
Statement 1: Task dependencies define workflow execution order. Statement 2: Airflow ignores dependencies when scheduling tasks.
Question 111
Statement 1: Task Groups improve DAG organization. Statement 2: Task Groups change business logic execution order automatically.
Question 112
Statement 1: Catchup can create historical DAG runs for missed schedules. Statement 2: Catchup settings can impact workload volume.
Question 113
Statement 1: Backfill is commonly used to process missed historical execution periods. Statement 2: Backfill is useful after outages or delayed source availability.
Question 114
Statement 1: Scheduler health is important for workflow execution. Statement 2: A failed scheduler may prevent new DAG runs from being created.
Question 115
Statement 1: Centralized logging helps troubleshoot production incidents. Statement 2: Logs are useful during root cause analysis.
Question 116
Statement 1: SLA monitoring helps identify workflows that exceed expected completion windows. Statement 2: A workflow can complete successfully and still violate an SLA.
Question 117
Statement 1: Trigger Rules influence task execution behavior. Statement 2: Different Trigger Rules can produce different workflow outcomes.
Question 118
Statement 1: Airflow is commonly used to orchestrate Databricks, Snowflake, APIs and cloud services. Statement 2: Airflow replaces all processing engines used inside workflows.
Question 119
Statement 1: A production-ready Airflow platform should include monitoring and alerting. Statement 2: Production workflows should rely solely on manual observation.
Question 120
Statement 1: Scalability, observability, reliability and governance are important enterprise Airflow objectives. Statement 2: Enterprise orchestration platforms should be designed only for current workloads.
Enterprise Airflow platforms must support future growth while maintaining reliability, security, monitoring and governance.