To achieve the professional designation of APMG International Enterprise Big Data Professional from the APMG International, candidates must clear the EBDP Exam with the minimum cut-off score. For those who wish to pass the APMG International Enterprise Big Data Professional 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 Professional Exam preparation.
The APMG International EBDP Exam Summary, Body of Knowledge (BOK), Sample Question Bank and Practice Exam provide the basis for the real APMG International Certified Enterprise Big Data Professional (EBDP) exam. We have designed these resources to help you get ready to take APMG International Enterprise Big Data Professional (EBDP) 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 Professional Practice Exam to achieve the best result.
APMG International EBDP Exam Summary:
| Exam Name | APMG International Enterprise Big Data Professional |
| Exam Code | EBDP |
| Exam Fee | USD $299 |
| Exam Duration | 90 Minutes |
| Number of Questions | 60 |
| Passing Score | 65% |
| Format | Multiple Choice Questions |
| Books / Trainings | Find a training provider |
| Schedule Exam | Book an exam |
| Sample Questions | APMG International Enterprise Big Data Professional Exam Sample Questions and Answers |
| Practice Exam | APMG International Certified Enterprise Big Data Professional (EBDP) Practice Test |
APMG International Enterprise Big Data Professional Syllabus Topics:
| Topic | Details |
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Big Data Key Concepts |
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Recall key terms and definitions relating to Big Data Specifically to recall:
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- The definition of Big Data - The names of the four characteristics of Big Data - The names of the two classes of machine learning and the techniques commonly associated with them:
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Understand the origins of Big Data and the characteristics of its key concepts Specifically to understand:
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- The origins of Big Data and the characteristics of the three Big Data development phases:
- The four characteristics of Big Data and how they distinguish Big Data from traditional data analysis:
- The four forms of pattern identification:
- The purpose of the different types of analytics:
- The function of metadata in Big Data environments
- The characteristics of the three data types:
- The role of Hadoop in distributed storage and distributed processing
- The two classes of machine learning and be able to recognize examples of these:
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The Big Data Framework |
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Recall terms and key facts about the Big Data Framework Specifically to recall:
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- The names of the six capabilities of the Big Data Framework |
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Understand the structure of the Big Data Framework Specifically to understand:
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- The relevance of each of the six Big Data Framework capabilities in establishing a Big Data organization
- The different levels of the Big Data maturity model:
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Big Data Strategy |
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Recall key facts about the Big Data Strategy Specifically to recall:
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- The five steps for formulating a Big Data Strategy and their sequence |
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Understand how to formulate a Big Data Strategy and the activities and techniques involved Specifically to understand:
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- The six business drivers influencing the need for a Big Data strategy and how Big Data can be used to generate a competitive advantage
- The Prioritization Matrix
- The activities involved in each of the five steps for formulating a Big Data Strategy:
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Big Data Architecture |
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Recall terms and key facts about Big Data Architecture Specifically to recall:
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- What a reference architecture is and its purpose - Key features about the structure of the NIST Big Data reference architecture:
- The names of the core components in a Hadoop Architecture:
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Understand the high-level principles and design elements of contemporary Big Data Architecture Specifically to understand:
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- The benefits of using a Big Data reference architecture - The functions and activities associated with the logical roles in the reference architecture
- The difference between local and distributed storage and processsing
- The storage mechanisms for Big Data
- The Big Data Real analysis architectures:
- The function of Hadoop in Big Data Environments
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Big Data Algorithms |
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Recall terms and key facts about Big Data Algorithms and Analysis Techniques Specifically to recall:
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- What descriptive statistics are
- Key facts about classification
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Understand the algorithms and analysis techniques fundamental to Big Data Specifically to understand:
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- For each type of descriptive statistic, understand what each statistical operation/distribution measures or shows:
- The characteristics of skew:
- The reason why standardization is used in Big Data calculations
- Recognize and calculate examples of descriptive statistics - The characteristics of the different types of distribution shapes:
- Why the distribution shapes are important to Big Data and data science:
- The implications of population, sample and bias for Big Data
- How correlations are used in Big Data and recognize examples of this. - The differences between correlation and regression - Recognize examples of a classification algorithm - The key characteristics of clustering:
- How outlier detection is used in the context of Big Data
- The key characteristics of each of the Visualization techniques and how each technique is used, with reference to examples:
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Big Data Processes |
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Recall key terms relating to the Big Data Processes Specifically, to recall:
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- The three different main processes that are used in Big Data and their main characteristics
- In which step in the data analysis process are the following tools/techniques typically used and how they are applied in that step:
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Understand the characteristics, activities and techniques of the Big Data Processes Specifically, to understand:
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- The characteristics of the six types of problems that shape the business objectives of Big Data projects:
- The importance of each step within the data analysis process and what occurs in each step;
- The importance of each step within the data governance process and what occurs in each step:
- The importance of each activity within the data management process and the what occurs in each activity:
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Big Data Functions |
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Recall key terms relating to Big Data Functions Specifically, to recall:
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- The names of the five pillars of the Big Data Centre of Excellence and the key characteristics of each pillar:
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Understand the benefits of the Big Data Centre of Excellence, the six organization success factors and the key roles in Big Data teams Specifically, to understand:
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- The benefits of a Big Data Centre of Excellence: - The typical responsibilities and skill sets of the key roles in Big Data teams:
- The six organization success factors for Big Data |
Artificial Intelligence |
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Recall key definitions and facts relating to Artificial Intelligence and Big Data Specifically, to recall:
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- The operational definition of intelligence according to the Turing test - Key facts about cognitive analytics:
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Understand the key concept of Artificial Intelligence and their importance to Big Data Specifically, to understand:
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- The role of rational agents in cognitive analytics - The four essential capabilities of artificial intelligence:
- Key characteristics about Deep Learning in artificial intelligence:
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Both APMG International and veterans who’ve earned multiple certifications maintain that the best preparation for a APMG International EBDP 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 Professional concepts. When you understand techniques, it helps you retain APMG International Enterprise Big Data Professional knowledge and recall that when needed.