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Last Updated: Aug 06, 2026
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| Section | Objectives |
|---|---|
| Data Governance and Operations | - Data quality management - Security and compliance considerations - Lifecycle management of data solutions |
| Machine Learning and Modeling | - Model evaluation and validation - Algorithm selection and tuning - Supervised and unsupervised learning |
| Data Engineering Principles | - Data pipeline design and orchestration - Data storage architectures (data lakes, warehouses) - ETL/ELT processes |
| Data Science Foundations | - Statistical analysis and probability concepts - Feature engineering fundamentals - Data exploration and preprocessing |
| Optimization and Performance | - Optimization techniques for data workflows - Model performance tuning |
1. An organization wants to simplify the data ingestion configuration process through simple click, drag, and drop type actions.
Which tool provides an easy to use graphical user interface for building data pipeline data flows?
A) Apache Hadoop
B) Apache Spark
C) Apache Airflow
D) Apache Pig
2. What is a typical task of a data engineer during a data analytics project?
A) Exploring and examining data to find hidden patterns
B) Presenting financial benefits to executive stakeholders
C) Planning and performing A/B testing
D) Transforming data into a format that can be easily analyzed
3. Which of the following best describes the purpose of database normalization in a relational database schema?
A) Improving query performance
B) Reducing data redundancy
C) Enforcing referential integrity
D) Increasing data security
4. In Python, which data type is one dimensional and immutable?
A) Tuple
B) Array
C) Set
D) List
5. What is a common application of IoT tools?
A) Monitoring real-time sensor data
B) Analyzing static datasets
C) Managing relational databases
D) Processing large-scale batch data
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: A |
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