To achieve the professional designation of APMG International Enterprise Big Data Engineer from the APMG International, candidates must clear the EBDE Exam with the minimum cut-off score. For those who wish to pass the APMG International Enterprise Big Data Engineer certification exam with good percentage, please take a look at the following reference document detailing what should be included in APMG International Enterprise Big Data Engineer Exam preparation.
The APMG International EBDE Exam Summary, Body of Knowledge (BOK), Sample Question Bank and Practice Exam provide the basis for the real APMG International Certified Enterprise Big Data Engineer (EBDE) exam. We have designed these resources to help you get ready to take APMG International Enterprise Big Data Engineer (EBDE) exam. If you have made the decision to become a certified professional, we suggest you take authorized training and prepare with our online premium APMG International Enterprise Big Data Engineer Practice Exam to achieve the best result.
APMG International EBDE Exam Summary:
| Exam Name | APMG International Enterprise Big Data Engineer |
| Exam Code | EBDE |
| Exam Fee | USD $492 |
| Exam Duration | 120 Minutes |
| Number of Questions | 80 |
| Passing Score | 65% |
| Format | Multiple Choice Questions |
| Schedule Exam | Book an exam |
| Sample Questions | APMG International Enterprise Big Data Engineer Exam Sample Questions and Answers |
| Practice Exam | APMG International Certified Enterprise Big Data Engineer (EBDE) Practice Test |
APMG International Enterprise Big Data Engineer Syllabus Topics:
| Topic | Details |
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Introduction to Data Engineering
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- Understand the role of data engineering in the data lifecycle, including the key activities involved in designing, building, and maintaining data infrastructure, and recognize the importance of data quality, consistency, and accessibility. - Understand the distinct roles and interconnections between data engineering, data analysis, and data science within the broader field of data and analytics. - Understand the key challenges faced by data engineers, including:
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Structured Data and Databases
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- Understand the key concepts and advantages of structured databases, including their use of schemas, SQL for data interaction, and their benefits such as ACID compliance, indexing, and query optimization. - Understand the key components of data modeling, including:
- Understand the key components involved in creating the physical design of a database, including:
- Apply data validation rules, constraints, and referential integrity mechanisms to a database schema to ensure data accuracy, consistency, and reliability, demonstrating the ability to implement practical solutions for maintaining data integrity throughout its lifecycle.
- Apply fundamental SQL commands such as:
- Apply intermediate SQL commands such as:
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Unstructured Data and Databases
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- Describe various unstructured data types and formats, including textual data, multimedia data, sensor data, log files, emails and communication records, and social media data, and explain their characteristics and potential applications. - Explain the key characteristics of NoSQL databases, such as schema flexibility, scalability, distributed architecture, and high performance, and identify scenarios where NoSQL databases are advantageous over traditional relational databases. - Apply the principles of different types of NoSQL databases by designing a data storage solution tailored to a specific use case:
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ETL, Batch and Stream Processing
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- Explain the ETL (Extract, Transform, Load) process by describing its three primary steps:
- Apply the knowledge of Apache NiFi to design and implement a data integration workflow that extracts data from multiple sources, transforms it to ensure data quality and consistency, and loads it into a centralized database for real-time analytics and reporting.
- Apply different types of triggers to initiate batch processing workflows by setting up batch processes based on specific criteria:
- Understand the core principles and characteristics of stream processing, including:
- Apply your knowledge of ETL, Batch Processing, and Stream Processing to a real-world scenario by designing a data workflow that integrates these techniques based on the specific requirements of a given use case. |
| Data Pipelines |
- Explain the concept of data pipelines, including their components, processes, and objectives. - Understand the EBDFA data pipeline architecture, including its key steps:
- Explain the different data ingestion methods:
- Explain the roles and functions of orchestration, management, and monitoring in data pipelines, and describe how they contribute to ensure data processing.
- Understand common data pipeline pattern, and explain their key characteristics:
- Apply various data pipeline patterns to real-world scenarios, demonstrating how each pattern addresses specific challenges, achieves scalability, ensures data quality, and streamlines data processing workflows in the context of modern data engineering and analytics.
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| Data Architectures |
- Understand the role and importance of data architectures in managing, storing, processing, and accessing data within an organization. - Understand common data architectures:
- Understand the structure and purpose of star and snowflake schemas in data warehousing.
- Understand the concept of a data lake house by explaining how it combines the features of data lakes and data warehouses. |
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Machine Learning for Data Engineers
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- Understand the basic types of machine learning by describing the main characteristics and differences between:
- Understand the process of model training in machine learning by describing how an algorithm learns from a labeled dataset.
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Security and Privacy in Data Engineering
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- Explain the key principles of data privacy and data security in data engineering. - Understand fundamental data privacy concepts in data engineering and their importance:
- Understand fundamental essential security concepts in data engineering:
- Evaluate the effectiveness of strategies for cultivating a culture of data privacy and security within an organization. |
Both APMG International and veterans who’ve earned multiple certifications maintain that the best preparation for a APMG International EBDE professional certification exam is practical experience, hands-on training and practice exam. This is the most effective way to gain in-depth understanding of APMG International Enterprise Big Data Engineer concepts. When you understand techniques, it helps you retain APMG International Enterprise Big Data Engineer knowledge and recall that when needed.