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Microsoft DP-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Identify considerations for relational data on Azure20-25%- Describe relational concepts
  • 1. Identify features of relational data
  • 2. Describe normalization
- Describe relational Azure data services
  • 1. Describe Azure SQL family of products
  • 2. Describe SQL Server on Azure Virtual Machines
  • 3. Describe Azure SQL Database
  • 4. Describe Azure SQL Managed Instance
  • 5. Describe Azure Database for MariaDB
  • 6. Describe Azure Database for PostgreSQL
  • 7. Describe Azure Database for MySQL
Topic 2: Describe core data concepts25-30%- Describe ways to represent data
  • 1. Describe features of unstructured data
  • 2. Describe features of structured data
  • 3. Describe features of semi-structured data
- Identify options for data storage
  • 1. Describe common formats for data files
  • 2. Describe types of databases
Topic 3: Describe considerations for working with non-relational data on Azure15-20%- Describe non-relational concepts
  • 1. Identify non-relational data
  • 2. Describe non-relational data offerings
- Describe non-relational Azure data services
  • 1. Describe Azure File storage
  • 2. Describe Azure Table storage
  • 3. Describe Azure Cosmos DB
  • 4. Describe Azure Blob storage
Topic 4: Describe an analytics workload on Azure25-30%- Describe analytics concepts
  • 1. Describe analytical workloads
  • 2. Describe ETL and ELT processes
  • 3. Describe transactional workloads
  • 4. Describe data warehousing
- Describe Azure data analytics services
  • 1. Describe Azure HDInsight
  • 2. Describe Azure Data Factory
  • 3. Describe Azure Synapse Analytics
  • 4. Describe Azure Databricks
  • 5. Describe Microsoft Fabric
  • 6. Describe Azure Stream Analytics
- Describe data visualization in Microsoft Power BI
  • 1. Describe Power BI
  • 2. Describe Power BI reports and dashboards
  • 3. Describe Power BI tools and workflow

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Microsoft Azure Data Fundamentals Sample Questions (Q65-Q70):

NEW QUESTION # 65
Match the Azure services to the appropriate locations in the architecture.
To answer, drag the appropriate service from the column on the left to its location on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-data-ingest-adf
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/sqldw-walkthrough


NEW QUESTION # 66
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:


NEW QUESTION # 67
match the Azure data factory components to the appropriate descriptions.
To answer, drag the appropriate component from the column on the left to its description on the right. Each component may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:

Explanation:

Explanation

Box 1: Dataset
Datasets must be created from paths in Azure datastores or public web URLs, for the data to be accessible by Azure Machine Learning.
Box 2: Linked service
Linked services are much like connection strings, which define the connection information needed for Data Factory to connect to external resources.
Box 3: Pipeline
A pipeline is a logical grouping of activities that together perform a task.


NEW QUESTION # 68
In a fully denormalized database, how is data read and written for a single entity?

Answer: C


NEW QUESTION # 69
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Reference:
https://www.bmc.com/blogs/what-is-batch-processing-batch-processing-explained/


NEW QUESTION # 70
......

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