Amazon DEA-C01 Practice Test Overview: Ace Your Exam!
This outline details what you can expect from our comprehensive Amazon DEA-C01 practice test, helping you confidently approach the real exam. Think of it as your roadmap to success!
I. Test Overview:
- Total Questions: Approximately 65 multiple-choice questions (this may vary slightly). Remember, it's not the quantity but the quality of your preparation that counts.
- Target Job Roles: This exam is designed for professionals aiming for roles involving data analytics, cloud computing, and business intelligence within the AWS ecosystem. Think Data Analysts, Business Intelligence Engineers, and even Cloud Architects who need a strong data foundation. If you're working with data on AWS, this is your exam.
- Language: English
II. Key Coverage Areas & Practice Test Breakdown:
We've structured our practice test to mirror the real exam's weighting, ensuring you're fully prepared for each section.
A. Data Modeling and Database Design (Approx. 20%)
- Relational Databases: Understanding schemas, normalization, and ACID properties. Think of it like building a well-organized house – each room (table) has its purpose, and everything connects logically.
- Data Warehousing and Dimensional Modeling: Star schemas, snowflake schemas, and fact/dimension tables. Imagine a star – the central fact table shines brightly, surrounded by its dimensional context.
- NoSQL Databases: Understanding different NoSQL database types (document, key-value, graph) and their use cases. This isn't just about choosing the right tool, it's also about understanding why that tool is better in that particular situation.
B. Data Analysis and Querying (Approx. 25%)
- SQL: Writing complex SQL queries, including joins, subqueries, aggregate functions, and window functions. Mastering SQL is like learning a powerful language – the more fluent you are, the more you can unlock.
- Data Wrangling and Cleaning: Handling missing values, outliers, and inconsistent data. It's not always pretty, but cleaning your data is like preparing a delicious meal – you can't make a good dish with bad ingredients.
- Data Visualization: Interpreting charts, graphs, and dashboards to derive actionable insights. Data visualization is the art of telling a story with your data – make sure yours is compelling!
C. Data Transformation and ETL (Approx. 20%)
- ETL Processes: Extract, Transform, Load – understanding the stages and tools used in data integration. Think of this as building a pipeline to get the data where it needs to be, cleaned and ready for analysis.
- Data Transformation Techniques: Data cleaning, data type conversion, and data aggregation.
- AWS Services for ETL: AWS Glue, AWS Data Pipeline, and others. You'll learn how to leverage Amazon's powerful tools to automate your ETL processes.
D. Data Security and Governance (Approx. 15%)
- Data Security Best Practices: Access control, encryption, and data masking. Protecting your data is paramount – think of it like guarding a valuable treasure.
- Data Governance Principles: Compliance regulations and data quality management. Data governance isn't just about rules, it's about building trust and ensuring accountability.
- AWS Security Services: IAM roles, KMS, and other relevant AWS security features.
E. Amazon Web Services (AWS) for Data Analytics (Approx. 20%)
- Amazon Redshift: Understanding its architecture, querying techniques, and performance optimization. Redshift is a powerful tool for data warehousing – learn how to harness its potential.
- Amazon S3, Athena, EMR: Utilizing these services for data storage, querying, and processing. These are your essential building blocks for a solid AWS-based data strategy.
- Other relevant AWS services: This section covers other related services like Glue, QuickSight, and more.
This outline provides a comprehensive overview. Remember to practice consistently, focusing on your weak areas. Good luck!
