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Microsoft DP-900 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Identify considerations for relational data on Azure	20–25%	- Describe relational data concepts 1. Relationships, keys, constraints 2. Query data with SQL 3. Normalization - Describe Azure relational data services 1. Azure SQL Database 2. Azure SQL Managed Instance 3. SQL Server on Azure Virtual Machines 4. Open-source databases: PostgreSQL, MySQL, MariaDB
Topic 2: Describe core data concepts	25–30%	- Describe roles and responsibilities 1. Data engineer, data analyst, database administrator - Describe data workloads 1. Transactional workloads 2. Batch and streaming data 3. Analytical workloads - Describe types of data 1. Structured, semi-structured, and unstructured data 2. File formats
Topic 3: Describe an analytics workload on Azure	25–30%	- Describe big data and analytics concepts 1. Data lakes and data warehouses 2. Data ingestion and processing 3. Modern data warehouse architecture - Describe data visualization 1. Data models and reports 2. Power BI capabilities - Describe Azure analytics services 1. Azure Synapse Analytics 2. Azure HDInsight 3. Azure Databricks 4. Microsoft Fabric 5. Azure Data Lake Storage Gen2 - Describe real-time analytics 1. Azure Stream Analytics 2. Stream processing concepts
Topic 4: Describe considerations for working with non-relational data on Azure	15–20%	- Describe Azure Cosmos DB 1. Capabilities and features 2. Consistency levels and partitioning 3. APIs and data models - Describe Azure storage services 1. Blob Storage 2. File Storage 3. Queue Storage 4. Table Storage - Describe non-relational data concepts 1. Use cases for non-relational data 2. Characteristics of NoSQL data

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Quiz DP-900 - Microsoft Azure Data Fundamentals Updated New Braindumps Files

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Microsoft Azure Data Fundamentals Sample Questions (Q91-Q96):

NEW QUESTION # 91
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
Table Description automatically generated

An aggregate function performs a calculation on a set of values, and returns a single value. The following are aggregate functions:
* APPROX_COUNT_DISTINCT
* AVG
* CHECKSUM_AGG
* COUNT
* COUNT_BIG
* GROUPING
* GROUPING_ID
* MAX
* MIN
* STDEV
* STDEVP
* STRING_AGG
* SUM
* VAR
* VARP

NEW QUESTION # 92
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://www.precisely.com/blog/big-data/big-data-101-batch-stream-processing

NEW QUESTION # 93
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Yes
Azure Databricks can consume data from SQL Databases using JDBC and from SQL Databases using the
Apache Spark connector.
The Apache Spark connector for Azure SQL Database and SQL Server enables these databases to act as input
data sources and output data sinks for Apache Spark jobs.
Box 2: Yes
You can stream data into Azure Databricks using Event Hubs.
Box 3: Yes
You can run Spark jobs with data stored in Azure Cosmos DB using the Cosmos DB Spark connector. Cosmos
can be used for batch and stream processing, and as a serving layer for low latency access.
You can use the connector with Azure Databricks or Azure HDInsight, which provide managed Spark clusters
on Azure.

NEW QUESTION # 94
To complete the sentence, select theappropriate option in the answer area.

Answer:

Explanation:

NEW QUESTION # 95
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

NEW QUESTION # 96
......

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