Azure Data Fundamentals (DP-900) - My Story and Study Resources

Reviewed April 2021

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I passed the Microsoft Azure Data Fundamentals exam at the beginning of April 2021. To give back to the community, I decided to share my experience and the study resources I used to prepare for the exam.

First, why did I take the exam 🤷‍♂️

I took the DP-900 exam while preparing for the Azure Solutions Architecture exams. Working through the Microsoft Learn modules for AZ-303/304, I found myself reading about database topics. My knowledge on databases is limited (the biggest database I deployed was the time of Access 2007) so I decided to stop preparing for these exams and review data topics.

Microsoft Virtual Training Days 👩‍🏫

After reviewing the role based certification poster by Microsoft, I identified there was a fundamentals exam. I knew from advertisements and posts within the community that Microsoft offers Virtual Training Days to help individuals getting started with the basics. I visited the Virtual Training Days site and found dates I could attend the video course. After completing the training days, I received a free exam voucher for the DP-900 exam.

Microsoft Learn 🏙️

Even though the video content from the Virtual Training Day was beneficial and covered the topics I expected, I decided to further my knowledge by completing the Azure Data Fundamentals MS Learn modules. I have listed each one below.

Tip: complete any labs they have to offer during these modules. I find doing practicals actually sink in better than just reading.

Was the above enough ⚖️

Personally no, there was language and services described that I personally want to understand more about before going into the exam. I decided to visit the DP-900 certification page and review the exam skill outline document. I created a spreadsheet of each skill measured and used this to look up any information I can find from the Microsoft website. I have provided below a written out copy of the exam skills outline document with links I found and used to help broaden my knowledge (all from Microsoft sources).

1️⃣ Describe core data concepts (15-20%)

1.1. Describe types of core data workloads

1.1.1. describe batch data

1.1.2. describe streaming data

1.1.3. describe the difference between batch and streaming data

1.1.4. describe the characteristics of relational data

1.2. Describe data analytics core concepts

1.2.1. describe data visualization (e.g., visualization, reporting, business intelligence (BI))

1.2.2. describe basic chart types such as bar charts and pie charts

1.2.3. describe analytics techniques (e.g., descriptive, diagnostic, predictive, prescriptive, cognitive)

1.2.4. describe ELT and ETL processing

1.2.5. describe the concepts of data processing

2️⃣ Describe how to work with relational data on Azure (25-30%)

2.1. Describe relational data workloads

2.1.1. identify the right data offering for a relational workload

2.1.2. describe relational data structures (e.g., tables, index, views)

2.2. Describe relational Azure data services

2.2.1. describe and compare PaaS, IaaS, and SaaS solutions

2.2.2. describe Azure SQL database services including Azure SQL Database, Azure SQL

2.3. Managed Instance, and SQL Server on Azure Virtual Machine

2.3.1. describe Azure Synapse Analytics

2.3.2. describe Azure Database for PostgreSQL, Azure Database for MariaDB, and Azure Database for MySQL

2.4. Identify basic management tasks for relational data

2.4.1. describe provisioning and deployment of relational data services

  • Recommend referring to the MS Learn modules

2.4.2. describe method for deployment including the Azure portal, Azure Resource Manager templates, Azure PowerShell, and the Azure command-line interface (CLI)

  • Recommend referring to the MS Learn modules

2.4.3. identify data security components (e.g., firewall, authentication)

2.4.4. identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls)

2.4.5. identify query tools (e.g., Azure Data Studio, SQL Server Management Studio, sqlcmd utility, etc.)

2.5. Describe query techniques for data using SQL language

2.5.1. compare Data Definition Language (DDL) versus Data Manipulation Language (DML)

2.5.2. query relational data in Azure SQL Database, Azure Database for PostgreSQL, and Azure Database for MySQL

3️⃣ Describe how to work with non-relational data on Azure (25-30%)

3.1. Describe non-relational data workloads

3.1.1. describe the characteristics of non-relational data

3.1.2. describe the types of non-relational and NoSQL data

3.1.3. recommend the correct data store

3.1.4. determine when to use non-relational data

3.2. Describe non-relational data offerings on Azure

3.2.1. identify Azure data services for non-relational workloads

3.2.2. describe Azure Cosmos DB APIs

3.2.3. describe Azure Table storage

3.2.4. describe Azure Blob storage

3.2.5. describe Azure File storage

3.3. Identify basic management tasks for non-relational data

3.3.1. describe provisioning and deployment of non-relational data services

  • Recommend referring to the MS Learn modules

3.3.2. describe method for deployment including the Azure portal, Azure Resource Manager templates, Azure PowerShell, and the Azure command-line interface (CLI)

  • Recommend referring to the MS Learn modules

3.3.3. identify data security components (e.g., firewall, authentication, encryption)

3.3.4. identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls)

3.3.5. identify management tools for non-relational data

4️⃣ Describe an analytics workload on Azure (25-30%)

4.1. Describe analytics workloads

4.1.1. describe transactional workloads

4.1.2. describe the difference between a transactional and an analytics workload

4.1.3. describe the difference between batch and real time

4.1.4. describe data warehousing workloads

4.1.5. determine when a data warehouse solution is needed

4.2. Describe the components of a modern data warehouse

4.2.1. describe Azure data services for modern data warehousing such as Azure Data Lake, Azure Synapse Analytics, Azure Databricks, and Azure HDInsight

4.2.2. describe modern data warehousing architecture and workload

4.3. Describe data ingestion and processing on Azure

4.3.1. describe common practices for data loading

4.3.2. describe the components of Azure Data Factory (e.g., pipeline, activities, etc.)

4.3.3. describe data processing options (e.g., Azure HDInsight , Azure Databricks, Azure Synapse Analytics, Azure Data Factory)

4.4. Describe data visualization in Microsoft Power BI

4.4.1. describe the role of paginated reporting

4.4.2. describe the role of interactive reports

4.4.3. describe the role of dashboards

4.4.4. describe the workflow in Power BI

  • No content found

Any other resources before I took the exam 📺

My exam was booked for the afternoon so I decided to get some type of morning cram in by watching some video content that would refresh the new information I learnt. The top hit I found on YouTube was from John Savill who provides a one hour and twenty minute whiteboard video covering the information I learnt from all of the above. This really helped so I would recommend watching it (remember to support content creators by liking and subscribing to their channels):

DP-900 Azure Data Fundamentals Exam Cram Whiteboard Video

Other than this, I didn't use any other resource to prepare for the exam.

Conclusion ✍️

This post wasn't to provide tips and tricks to the exam. The idea of this post is to tell you what got me onto the exam, what resources I found on the way and to give back to the community where I have farmed resources for many years. The intention wasn't to get another certification but to skill up in an area where I was lacking information for another certification I was trying to attain. In the end, it was a bonus for me to be able to sit the exam for free and pass.

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