Microsoft Azure Data Fundamentals (DP-900) Exam
As the name suggests, the Microsoft Azure Data Fundamentals (DP-900) Exam tests candidates’ basic or fundamental azure data knowledge. The candidates appearing in the Microsoft Azure Data Fundamentals (DP-900) Exam are expected to have a foundational understanding of in-depth data concepts. They should know the implementation of core data concepts using the Microsoft Azure Data Services. The Microsoft Azure Data Fundamentals (DP-900) certification is meant for candidates beginning to work with data in the cloud. Familiarity with the concepts of relational and non-relational data and various kinds of data workloads, whether transactional or analytical, is also needed. The Microsoft Azure Data Fundamentals can be used while preparing for other Azure-related exams. It is often a prerequisite in most Microsoft Azure certifications.
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Microsoft Azure Data Fundamentals Questions and Answers
Match the types of analytics that can be used to answer the business questions.
To answer, drag the appropriate analytics type from the column on the left to its question on the right. Each analytics type may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Box 1: Diagnostic
Diagnostic Analytics: At this stage you can begin to answer some of those why questions. Historical data can begin to be measured against other data to answer the question of why something happened in the past. This is the process of gathering and interpreting different data sets to identify anomalies, detect patters, and determine relationships.
Box 2: Prescriptive
Prescriptive analytics is a combination of data, mathematical models, and various business rules to infer actions to influence future desired outcomes.
Box 3: Cognitive
Select the answer that correctly completes the sentence.
Your company needs to design a database that illustrates the relationships between utilization levels of individual network devices across a local area network.
Which type of data store should you use?
Data as it appears in the real world is naturally connected. Traditional data modeling focuses on defining entities separately and computing their relationships at runtime. While this model has its advantages, highly connected data can be challenging to manage under its constraints.
A graph database approach relies on persisting relationships in the storage layer instead, which leads to highly efficient graph retrieval operations. Azure Cosmos DB's Gremlin API supports the property graph model.
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