How to operate with relational data on Azure

Here, the candidate should be equipped with knowledge and skills in the following sub domains:

Relational data workloads – the candidate has to gain skills in identifying the appropriate data offerings when it comes to relational workload and learn more about structures of relational data.
Relational Azure data services – this section of the exam is focused on the following: the understanding of IaaS, SaaS and PaaS solutions and their comparison, Azure SQL DB services such as Azure SQL managed instance and Azure virtual machine, Azure Synapse Analytics, and Azure Database for MariaDB, PostgreSQL and for MySQL.
Data querying techniques that require utilizing SQL language – it deals with the Data Manipulation Language (DML) and Data Definition Language (DDL) as well as querying relational data on Azure SQL DB.
Identification of basic management roles for relational data – under this sub domain there are several skills covered that include the deployment and provisioning of relational data, various deployment methods such as Azure Resource Manager templates, Azure portal, Azure command-line interface (CLI), etc. Also, one should be knowledgeable in data components identification (authentication and firewall), the identification of basic connectivity problems such as accessing Azure VNets, firewalls, access from the internet and authentication). One more knowledge area involved is query tools (SQL Management Studio, Azure Data Studio, utility, sqlcmd etc.).

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What is the duration, language, and format of Microsoft DP-900: Microsoft Azure Data Fundamentals Exam

Language: English
Passing score: 700 / 1000
This is the beta exam, would be available on or around March 31, 2020.
Number of Questions: 40 to 60 questions (Since Microsoft does not publish this information, the number of exam questions may change without notice.)
Length of Examination: 50 mins
Type of Questions: This test format is multiple choice.

How to Register For Exam DP-900: Microsoft Azure Data Fundamentals?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=DP-900&examname=Exam%20DP-900:%20Microsoft%20Azure%20Data%20Fundamentals&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fdp-900

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-900

Workload analytics on Azure

Data visualization in Microsoft Power BI – this topic requires candidates to learn more about paginated reporting, dashboards, interactive reports, as well as workflow in Power BI.
Analytics workloads – under this topic, candidates should learn more about transactional workloads as well as data warehousing workloads. Also, they have to get skilled in differentiating between an analytics and a transactional workload, know the difference between real and batch time, and be able to determining when a data warehouse solution is required.
Elements of a modern data warehouse – this sub-section is focused on Azure data services related to modern data warehousing. In addition, applicants should be familiar with modern data warehousing workload and architecture.
Date processing and ingestion on Azure – main topics within this subdomain include data loading common practices, Azure Data Factory components (activities, pipeline, etc.), and data processing.

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Microsoft DP-900日本語 Exam Syllabus Topics:

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

Microsoft Azure Data Fundamentals (DP-900日本語版) Sample Questions:

Question #1

以下の各記述について、正しい場合は「はい」を選択してください。そうでない場合は「いいえ」を選択してください。注：正しい選択肢は1つにつき1点です。

Reveal Solution Discussion 0

Correct Answer:

Explanation:

Box 1: No
The API determines the type of account to create. Azure Cosmos DB provides five APIs: Core (SQL) and MongoDB for document data, Gremlin for graph data, Azure Table, and Cassandra. Currently, you must create a separate account for each API.
Box 2: Yes
Azure Cosmos DB uses partitioning to scale individual containers in a database to meet the performance needs of your application. In partitioning, the items in a container are divided into distinct subsets called logical partitions. Logical partitions are formed based on the value of a partition key that is associated with each item in a container.
Box 3: No
Logical partitions are formed based on the value of a partition key that is associated with each item in a container.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/partitioning-overview

Question #2

あなたの会社では、抽出、ロード、変換 (ELT) プロセスを使用して、顧客関係管理 (CRM) システムからデータウェアハウスにデータをロードすることを計画しています。
ELTプロセスの各段階では、データ処理はどこで行われますか？答えるには、適切な場所を正しい段階にドラッグしてください。各場所は、1回、複数回、またはまったく使用されない場合があります。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要がある場合があります。
注意: 正しい選択ごとに 1 ポイントが付与されます。

Reveal Solution Discussion 0

Correct Answer:

Explanation:

Box 1: The CRM system
Data is extracted from the CRM system.
Box 2: The data warehouse
Data is loaded to the data warehouse.
Box 3: A standalone data analysis tool
The data transformation that takes place usually involves various operations, such as filtering, sorting, aggregating, joining data, cleaning data, deduplicating, and validating data.

Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl