Module 4 Quiz: Data Pipelines

Test your understanding of data pipelines with these 30 questions.

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Question 1

What is Apache Airflow primarily used for?

1. Training ML models
2. Orchestrating and scheduling data pipelines
3. Serving models in production
4. Version control for data

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Question 2

What is a feature store?

1. A retail store for ML features
2. A centralized repository for storing and serving ML features
3. A database for model weights
4. A feature selection algorithm

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Question 3

What is Great Expectations used for?

1. Model training
2. Feature selection
3. Data validation and quality testing
4. Deployment automation

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Question 4

What is the difference between batch and stream processing?

1. Batch is faster than stream
2. Batch processes data in groups; stream processes data in real-time
3. Stream is only for video data
4. There is no difference

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Question 5

What is Feast?

1. A celebration after model deployment
2. An open-source feature store
3. A data ingestion tool
4. A monitoring platform

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Question 6

What is a DAG in Apache Airflow?

1. Data Analysis Graph
2. Directed Acyclic Graph representing workflow dependencies
3. Data Aggregation Gateway
4. Distributed Analytics Grid

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Question 7

What is an online feature store used for?

1. Training models
2. Serving features for real-time inference with low latency
3. Storing historical data
4. Batch processing

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Question 8

What is a data contract?

1. Legal agreement for data usage
2. Specification defining expected data schema, quality, and delivery
3. Storage pricing agreement
4. Employee data agreement

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Question 9

What should happen when data validation fails in a pipeline?

1. Continue processing anyway
2. Delete the data
3. Alert the team and stop/quarantine the problematic data
4. Automatically fix the data

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Question 10

What is idempotency in data pipelines?

1. Running pipelines in parallel
2. Running the same operation multiple times produces the same result
3. Processing data only once
4. Encrypting data at rest

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Questions 11-30

(Additional questions covering ETL patterns, feature engineering techniques, data quality dimensions, pipeline monitoring, schema evolution, and more)

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🎉 Quiz Complete!

Next: Module 5: Training Infrastructure