Exam Domain
A major topic area covered by the certification exam.
Getting Started with DP-900
Free knowledge base
Microsoft Azure Data Fundamentals — key terms, tricks & tips
Everything from the course in one searchable place: 252 entries. Use it to review before a practice test or look up a word you forgot.
252 results
Weighting
The approximate percentage of questions from a specific exam domain.
Getting Started with DP-900
Multiple-Choice Question
A question with one correct answer among several options.
Getting Started with DP-900
Multiple-Response Question
A question requiring selection of all correct answers from a list.
Getting Started with DP-900
Passing Score
The minimum score required to successfully pass the exam.
Getting Started with DP-900
Pearson VUE
The third-party vendor that administers Microsoft certification exams.
Getting Started with DP-900
Proctor
An individual who supervises exam takers, in-person or online.
Getting Started with DP-900
Understanding the DP-900 Exam Structure
DP-900: 'D' for Domains, 'P' for Passing Score, '900' for the target knowledge level. Remember the 700 passing score!
Getting Started with DP-900
Understanding the DP-900 Exam Structure
The DP-900 exam has a passing score of 700 out of 1000. There are no 'fill in the blank' or 'essay' questions; stick to understanding multiple-choice and drag-and-drop formats.
Getting Started with DP-900
Understanding the DP-900 Exam Structure
Ignoring exam weightings and spending too much time on less important topics.
Getting Started with DP-900
Understanding the DP-900 Exam Structure
Not practicing different question types, leading to confusion during the actual exam.
Getting Started with DP-900
Understanding the DP-900 Exam Structure
Underestimating the importance of time management during the exam itself.
Getting Started with DP-900
Microsoft Learn
Free online platform for Microsoft technology training.
Getting Started with DP-900
Learning Path
Structured collection of modules for a specific goal.
Getting Started with DP-900
Module
Self-contained unit within a learning path, covering a topic.
Getting Started with DP-900
Unit
Smallest segment of content within a module or lesson.
Getting Started with DP-900
Knowledge Check
Short quizzes within modules to test understanding.
Getting Started with DP-900
Sandbox
Temporary, free Azure environment for hands-on practice.
Getting Started with DP-900
Certification Page
Official Microsoft page detailing an exam and its objectives.
Getting Started with DP-900
Navigating Microsoft Learn for DP-900
Learn My Modules, Understand Every Section (LMUES) – helps you remember the hierarchy: Learn (platform), Modules (topics), Units (sections).
Getting Started with DP-900
Navigating Microsoft Learn for DP-900
The DP-900 exam objectives are directly mapped to the official learning path modules on Microsoft Learn. Always cross-reference your study with the current exam objectives listed on the DP-900 certification page.
Getting Started with DP-900
Navigating Microsoft Learn for DP-900
Relying solely on third-party materials without cross-referencing with Microsoft Learn, which can lead to outdated or inaccurate information.
Getting Started with DP-900
Navigating Microsoft Learn for DP-900
Skipping the knowledge checks or hands-on exercises within Microsoft Learn modules, missing opportunities to reinforce learning.
Getting Started with DP-900
Navigating Microsoft Learn for DP-900
Not checking the official DP-900 exam page regularly for updates to objectives or recommended study materials.
Getting Started with DP-900
Structured Data
Data with a predefined schema, organized in rows and columns.
Core Data Concepts Explained
Semi-structured Data
Data with organizational tags but no fixed tabular schema.
Core Data Concepts Explained
Unstructured Data
Data without a predefined schema or organizational structure.
Core Data Concepts Explained
Schema
The logical configuration or structure of a database.
Core Data Concepts Explained
JSON
JavaScript Object Notation, a common semi-structured data format.
Core Data Concepts Explained
XML
eXtensible Markup Language, another common semi-structured data format.
Core Data Concepts Explained
Relational Database
A database that stores structured data in tables with predefined relationships.
Core Data Concepts Explained
NoSQL Database
A database that stores semi-structured or unstructured data, offering flexibility.
Core Data Concepts Explained
Ways to Represent Data: Structured, Semi-structured, Unstructured
Think of a library: Structured data is like books neatly categorized by Dewey Decimal. Semi-structured is like books with just author and title tags. Unstructured is a pile of loose papers!
Core Data Concepts Explained
Ways to Represent Data: Structured, Semi-structured, Unstructured
The exam often presents scenarios and asks you to identify the best data type or Azure service. Look for keywords like 'fixed schema,' 'rows and columns,' or 'SQL queries' for structured data. 'Tags,' 'key-value pairs,' 'JSON,' or 'XML' point to semi-structured. 'Documents,' 'images,' 'audio,' 'video,' or 'no predefined format' indicate unstructured data. Memorize the typical Azure services associated with each type.
Core Data Concepts Explained
Ways to Represent Data: Structured, Semi-structured, Unstructured
Assuming all data can be forced into a relational database, leading to complex schemas and poor performance.
Core Data Concepts Explained
Ways to Represent Data: Structured, Semi-structured, Unstructured
Trying to apply SQL queries directly to unstructured data without prior processing or indexing.
Core Data Concepts Explained
Ways to Represent Data: Structured, Semi-structured, Unstructured
Underestimating the storage and processing requirements for unstructured data, especially at scale.
Core Data Concepts Explained
Blob Storage
Object storage for unstructured data (images, videos, backups).
Core Data Concepts Explained
File Storage
Managed file shares accessible via SMB/NFS protocols.
Core Data Concepts Explained
Disk Storage
Persistent, block-level storage for Azure Virtual Machines.
Core Data Concepts Explained
SMB
Server Message Block protocol, used for network file sharing.
Core Data Concepts Explained
NFS
Network File System protocol, another network file sharing protocol.
Core Data Concepts Explained
Access Tiers
Cost-performance levels for Blob storage (Hot, Cool, Archive).
Core Data Concepts Explained
Storing Data Files: Blob, File, Disk Storage Options
Remember 'BFD': Blob for Files (unstructured), File for Directories (shared), Disk for Drivers (VMs).
Core Data Concepts Explained
Storing Data Files: Blob, File, Disk Storage Options
The exam often tests your ability to select the *most appropriate* storage type for a given scenario. Pay close attention to keywords like 'unstructured data,' 'shared file system,' 'VM operating system,' or 'high-performance disk.'
Core Data Concepts Explained
Storing Data Files: Blob, File, Disk Storage Options
Confusing Blob storage with File storage: Blob is for objects (like individual files as objects), File is for shared network drives.
Core Data Concepts Explained
Storing Data Files: Blob, File, Disk Storage Options
Using Disk storage for general file sharing instead of VM-specific needs.
Core Data Concepts Explained
Storing Data Files: Blob, File, Disk Storage Options
Not considering access tiers for Blob storage, leading to higher costs for infrequently accessed data.
Core Data Concepts Explained
OLTP
Online Transaction Processing; handles frequent, small, concurrent read/write operations.
Core Data Concepts Explained
OLAP
Online Analytical Processing; handles complex queries on large historical datasets.
Core Data Concepts Explained
ACID properties
Atomicity, Consistency, Isolation, Durability; guarantees for reliable database transactions.
Core Data Concepts Explained
Data Warehouse
A system used for reporting and data analysis, often storing historical and aggregated data.
Core Data Concepts Explained
HTAP
Hybrid Transactional/Analytical Processing; combines OLTP and OLAP capabilities.
Core Data Concepts Explained
Concurrency
The ability of a system to handle multiple operations or users simultaneously.
Core Data Concepts Explained
Common Data Workloads: Transactional, Analytical, Hybrid
Think 'T' for Transactions (OLTP) – tiny, fast, frequent updates. Think 'A' for Analytics (OLAP) – ample, aggregated, long-running queries.
Core Data Concepts Explained
Common Data Workloads: Transactional, Analytical, Hybrid
The exam often presents scenarios and asks you to identify whether an OLTP or OLAP solution is more appropriate. Look for keywords like 'real-time transactions,' 'order processing,' 'customer records' for OLTP, and 'historical trends,' 'reporting,' 'business intelligence,' 'forecasting' for OLAP. Remember that HTAP is about combining these for immediate insights.
Core Data Concepts Explained
Common Data Workloads: Transactional, Analytical, Hybrid
Confusing OLTP and OLAP use cases: Using an OLTP database for complex analytical queries will lead to poor performance.
Core Data Concepts Explained
Common Data Workloads: Transactional, Analytical, Hybrid
Ignoring data integrity for OLTP: Failing to implement ACID properties can lead to inconsistent data in transactional systems.
Core Data Concepts Explained
Common Data Workloads: Transactional, Analytical, Hybrid
Overlooking the benefits of HTAP: Not considering HTAP for scenarios requiring real-time analytics on operational data.
Core Data Concepts Explained
Batch Processing
Processing data in large groups at scheduled intervals.
Core Data Concepts Explained
Streaming Processing
Continuously processing data as it arrives in real-time.
Core Data Concepts Explained
Interactive Processing
User-initiated queries with immediate, on-demand results.
Core Data Concepts Explained
Latency
The delay between data generation and processing/availability.
Core Data Concepts Explained
Throughput
The amount of data processed over a given time period.
Core Data Concepts Explained
Real-time
Processing data with minimal delay, typically milliseconds/seconds.
Core Data Concepts Explained
Data Processing Options: Batch, Streaming, Interactive
Remember 'BSI': Batch is Big and Slow, Streaming is Swift and Instant, Interactive is Inquisitive and Immediate.
Core Data Concepts Explained
Data Processing Options: Batch, Streaming, Interactive
The exam often asks to identify the best processing type for a given scenario. Keywords like 'daily report,' 'monthly billing,' or 'historical analysis' point to batch. 'Real-time alerts,' 'IoT data,' or 'fraud detection' indicate streaming. 'Ad-hoc query,' 'dashboard,' or 'exploratory analysis' suggest interactive.
Core Data Concepts Explained
Data Processing Options: Batch, Streaming, Interactive
Confusing batch processing with interactive processing. Batch is scheduled and large-scale; interactive is on-demand and user-driven.
Core Data Concepts Explained
Data Processing Options: Batch, Streaming, Interactive
Assuming streaming processing is always the best option. While fast, it can be more complex and costly than batch for scenarios where real-time isn't critical.
Core Data Concepts Explained
Data Processing Options: Batch, Streaming, Interactive
Not considering latency requirements. The acceptable delay for data insights is the primary factor in choosing a processing method.
Core Data Concepts Explained
Data Type
A classification that specifies the kind of values a column can hold (e.g., integer, string, date).
Core Data Concepts Explained
Relationship
A logical connection between two tables in a database, usually via common columns.
Core Data Concepts Explained
Foreign Key
A column or set of columns in one table that refers to the primary key in another table.
Core Data Concepts Explained
Primary Key
A column or set of columns that uniquely identifies each record in a table.
Core Data Concepts Explained
One-to-Many
A relationship where one record in Table A relates to multiple records in Table B.
Core Data Concepts Explained
Many-to-Many
A relationship where multiple records in Table A relate to multiple records in Table B.
Core Data Concepts Explained
Data Concepts: Schema, Data Types, Relationships
Schema is the 'S'tructure. Data Types are the 'T'ypes of values. Relationships 'R'elate tables. STR!
Core Data Concepts Explained
Data Concepts: Schema, Data Types, Relationships
The exam frequently tests your understanding of core database concepts. Memorize the definitions of schema, data types (string, integer, boolean, datetime), and the three main relationship types (one-to-one, one-to-many, many-to-many). Pay attention to keywords like 'structure,' 'kind of data,' and 'how tables connect.'
Core Data Concepts Explained
Data Concepts: Schema, Data Types, Relationships
Confusing a schema with the actual data stored in the database; the schema is the definition, not the content.
Core Data Concepts Explained
Data Concepts: Schema, Data Types, Relationships
Using an inappropriate data type, such as storing numbers that will be calculated as text, leading to errors.
Core Data Concepts Explained
Data Concepts: Schema, Data Types, Relationships
Ignoring relationships between tables, which can lead to data duplication and inconsistency.
Core Data Concepts Explained
Table
A collection of related data organized in rows and columns.
Relational Data on Azure
Column
A vertical entity in a table that contains all data entries of a particular type.
Relational Data on Azure
Row
A horizontal entity in a table representing a single record or instance.
Relational Data on Azure
SQL
Structured Query Language, used to manage and query relational databases.
Relational Data on Azure
DDL
Data Definition Language, for defining database structure (e.g., CREATE TABLE).
Relational Data on Azure
DML
Data Manipulation Language, for managing data within objects (e.g., SELECT, INSERT).
Relational Data on Azure
Relational Data Concepts: Tables, Keys, SQL
Think of a 'Primary' school where each student has a 'Primary Key' (unique ID). When they visit the 'Foreign' language class, their 'Foreign Key' (same ID) links them back to their main record.
Relational Data on Azure
Relational Data Concepts: Tables, Keys, SQL
The exam often tests your understanding of the purpose of primary and foreign keys, and the basic functions of DDL vs. DML commands. Memorize examples of commands for each category.
Relational Data on Azure
Relational Data Concepts: Tables, Keys, SQL
Confusing primary keys with foreign keys: Primary keys are unique within their own table; foreign keys link to primary keys in other tables.
Relational Data on Azure
Relational Data Concepts: Tables, Keys, SQL
Using DML commands when DDL is needed: For example, trying to change a column's data type with UPDATE instead of ALTER TABLE.
Relational Data on Azure
Relational Data Concepts: Tables, Keys, SQL
Forgetting that SQL is declarative: You tell the database what data you want, not step-by-step how to get it.
Relational Data on Azure
Azure SQL Database
Fully managed relational database service based on SQL Server.
Relational Data on Azure
Azure Database for MySQL
Fully managed relational database service based on MySQL Community Edition.
Relational Data on Azure
Azure Database for PostgreSQL
Fully managed relational database service based on PostgreSQL.
Relational Data on Azure
Managed Service
Cloud provider handles infrastructure, patching, backups, and maintenance.
Relational Data on Azure
Open Source
Software with source code freely available and modifiable.
Relational Data on Azure
T-SQL
Transact-SQL, Microsoft's proprietary extension to SQL.
Relational Data on Azure
High Availability
System remains operational even if components fail.
Relational Data on Azure
Azure Relational Data Services: SQL, MySQL, PostgreSQL
Think of the 'SQL' in Azure SQL Database as 'Standard Query Language' for Microsoft's own SQL Server. 'My' in MySQL is for 'My preferred open source web app database.' 'Post' in PostgreSQL is for 'Post-advanced features' or 'Post-enterprise' for complex needs.
Relational Data on Azure
Azure Relational Data Services: SQL, MySQL, PostgreSQL
The exam often tests your ability to choose the correct Azure relational database service for a given scenario. Look for keywords like 'SQL Server compatibility,' 'open-source MySQL,' or 'advanced PostgreSQL features' to guide your answer.
Relational Data on Azure
Azure Relational Data Services: SQL, MySQL, PostgreSQL
Assuming all Azure relational databases are interchangeable; they have distinct strengths.
Relational Data on Azure
Azure Relational Data Services: SQL, MySQL, PostgreSQL
Not considering the cost implications of different service tiers and performance levels.
Relational Data on Azure
Azure Relational Data Services: SQL, MySQL, PostgreSQL
Overlooking the importance of existing developer skill sets and application compatibility.
Relational Data on Azure
Azure SQL Managed Instance
Managed PaaS service with near 100% SQL Server compatibility.
Relational Data on Azure
SQL Server on Azure VMs
IaaS offering for full control over OS and SQL Server.
Relational Data on Azure
PaaS (Platform as a Service)
Cloud service where Microsoft manages infrastructure and OS.
Relational Data on Azure
IaaS (Infrastructure as a Service)
Cloud service where you manage the OS and applications.
Relational Data on Azure
Lift-and-shift
Migrating an application to the cloud with minimal changes.
Relational Data on Azure
SQL Server Agent
A job scheduling service within SQL Server.
Relational Data on Azure
Azure Managed Instances and Virtual Machines for SQL
Think of 'Managed Instance' as a 'Managed Apartment' – you decorate and live in it, but the landlord (Microsoft) handles the building's maintenance. 'SQL VM' is like owning a 'Vacant Mansion' – you're responsible for literally everything!
Relational Data on Azure
Azure Managed Instances and Virtual Machines for SQL
The exam often tests your understanding of management responsibility. Remember: Managed Instance = Microsoft manages OS/SQL engine, you manage databases. SQL VM = You manage everything (OS, SQL, backups, HA/DR).
Relational Data on Azure
Azure Managed Instances and Virtual Machines for SQL
Confusing the level of management responsibility between Managed Instance and SQL VM.
Relational Data on Azure
Azure Managed Instances and Virtual Machines for SQL
Assuming Managed Instance has the exact same features as on-premises SQL Server Enterprise Edition without checking compatibility.
Relational Data on Azure
Azure Managed Instances and Virtual Machines for SQL
Overlooking the increased operational cost and expertise required for SQL Server on Azure VMs.
Relational Data on Azure
Provisioning
Deploying a new database service instance in Azure with initial configurations.
Relational Data on Azure
Vertical Scaling
Increasing or decreasing compute and memory resources of a single database instance.
Relational Data on Azure
Horizontal Scaling
Distributing workload across multiple instances, often using read replicas.
Relational Data on Azure
Point-in-time Restore
Recovering a database to a specific moment within the backup retention period.
Relational Data on Azure
Private Endpoint
Network interface connecting Azure services privately to a virtual network.
Relational Data on Azure
Azure Active Directory
Microsoft's cloud-based identity and access management service.
Relational Data on Azure
Auditing
Recording database events to monitor activity and ensure compliance.
Relational Data on Azure
Basic Management: Provisioning, Scaling, Security
To remember the key management tasks: P.S.S.B.A. - Provision, Scale, Secure, Backup, Audit. Think of 'Please Send Secure Backup Alerts!'
Relational Data on Azure
Basic Management: Provisioning, Scaling, Security
On the exam, be ready to distinguish between vertical and horizontal scaling. Vertical scaling is about 'more power' for one server, while horizontal scaling is about 'more servers.' Also, remember that Azure SQL Database and other PaaS services handle automatic backups and patching, reducing administrative overhead compared to IaaS.
Relational Data on Azure
Basic Management: Provisioning, Scaling, Security
Forgetting to configure network security (firewalls, VNets) during provisioning, leaving the database exposed.
Relational Data on Azure
Basic Management: Provisioning, Scaling, Security
Underestimating performance requirements and not scaling up proactively, leading to application slowdowns during peak times.
Relational Data on Azure
Basic Management: Provisioning, Scaling, Security
Relying solely on default backup retention policies without considering specific compliance or recovery point objectives (RPOs).
Relational Data on Azure
SELECT
T-SQL statement to retrieve data from a database.
Relational Data on Azure
WHERE clause
Filters rows based on specified conditions.
Relational Data on Azure
ORDER BY clause
Sorts the result set of a query.
Relational Data on Azure
INSERT
DML statement to add new rows to a table.
Relational Data on Azure
UPDATE
DML statement to modify existing rows in a table.
Relational Data on Azure
DELETE
DML statement to remove rows from a table.
Relational Data on Azure
Querying Relational Data with T-SQL Basics
To remember the basic SELECT statement order: 'Silly Frogs Want Oranges' (SELECT FROM WHERE ORDER BY).
Relational Data on Azure
Querying Relational Data with T-SQL Basics
The exam expects you to differentiate between DDL (Data Definition Language) and DML (Data Manipulation Language). Remember that SELECT is technically DQL (Data Query Language) but is often grouped with DML for practical purposes. Focus on the core syntax of SELECT, WHERE, and ORDER BY.
Relational Data on Azure
Querying Relational Data with T-SQL Basics
Forgetting the semicolon at the end of a statement (though often optional, it's good practice).
Relational Data on Azure
Querying Relational Data with T-SQL Basics
Using single quotes for column names or keywords instead of string literals.
Relational Data on Azure
Querying Relational Data with T-SQL Basics
Attempting to use `ORDER BY` before `WHERE` in a `SELECT` statement.
Relational Data on Azure
NoSQL
A category of non-relational databases offering flexible schemas and scalability.
Non-Relational Data on Azure
Key-Value Store
Database storing data as unique keys mapped to opaque values.
Non-Relational Data on Azure
Document Database
Stores semi-structured data in flexible, self-describing documents (e.g., JSON).
Non-Relational Data on Azure
Column-Family Database
Stores data in rows with flexible columns grouped into families.
Non-Relational Data on Azure
Graph Database
Stores data as nodes and edges, optimized for relationship traversal.
Non-Relational Data on Azure
Schema Flexibility
Ability to store data without a rigid, predefined structure.
Non-Relational Data on Azure
CAP Theorem
States a distributed system can only guarantee two of Consistency, Availability, Partition Tolerance.
Non-Relational Data on Azure
Non-Relational Data Concepts: Key-Value, Document, Graph, Column
K-D-C-G: 'Kids Don't Care about Graphs!' (Key-Value, Document, Column-Family, Graph) – helps remember the four main types.
Non-Relational Data on Azure
Non-Relational Data Concepts: Key-Value, Document, Graph, Column
For the DP-900 exam, memorize the core use case for each non-relational data model: Key-Value for caching/session, Document for flexible content/catalogs, Column-Family for analytics/time-series, and Graph for relationships/social networks. Look for keywords in scenarios describing these needs.
Non-Relational Data on Azure
Non-Relational Data Concepts: Key-Value, Document, Graph, Column
Assuming one NoSQL model fits all needs; each has specific strengths.
Non-Relational Data on Azure
Non-Relational Data Concepts: Key-Value, Document, Graph, Column
Trying to force a relational schema into a non-relational database.
Non-Relational Data on Azure
Non-Relational Data Concepts: Key-Value, Document, Graph, Column
Overlooking the performance benefits of a specialized NoSQL model for a specific workload.
Non-Relational Data on Azure
Azure Cosmos DB
Globally distributed, multi-model database service with guaranteed low latency.
Non-Relational Data on Azure
Azure Table Storage
Massively scalable key-value store for structured non-relational data.
Non-Relational Data on Azure
Multi-model database
A database that supports multiple data models like document, graph, key-value.
Non-Relational Data on Azure
Consistency models
Defines the freshness and order of data reads after a write operation.
Non-Relational Data on Azure
Global distribution
Data replicated across multiple geographic regions for low latency and high availability.
Non-Relational Data on Azure
Request Units (RU/s)
Throughput measure in Cosmos DB, representing operations per second.
Non-Relational Data on Azure
Partition Key
Determines how data is distributed across logical partitions in Table Storage/Cosmos DB.
Non-Relational Data on Azure
Azure Non-Relational Data Services: Cosmos DB, Table
Think of 'Cosmos' as 'Cosmopolitan' – it's global, sophisticated, and has many 'models' (APIs). 'Table' is like a simple, sturdy table – great for holding lots of basic items cheaply.
Non-Relational Data on Azure
Azure Non-Relational Data Services: Cosmos DB, Table
The exam often tests the core difference: Cosmos DB for global, high-performance, multi-API needs versus Table Storage for massive, simple, cost-effective key-value data. Keywords like 'global distribution,' 'guaranteed latency,' and 'multi-model' point to Cosmos DB. 'Massive scale,' 'low cost,' and 'simple key-value' point to Table Storage.
Non-Relational Data on Azure
Azure Non-Relational Data Services: Cosmos DB, Table
Confusing Table Storage's basic key-value capabilities with Cosmos DB's advanced Table API, which offers more features.
Non-Relational Data on Azure
Azure Non-Relational Data Services: Cosmos DB, Table
Overlooking the cost implications: Cosmos DB is generally more expensive due to its advanced features and guaranteed SLAs.
Non-Relational Data on Azure
Azure Non-Relational Data Services: Cosmos DB, Table
Choosing Table Storage for applications requiring strong consistency across globally distributed regions, which it doesn't natively provide.
Non-Relational Data on Azure
Azure Cache for Redis
An in-memory data store for high-performance caching and real-time data operations.
Non-Relational Data on Azure
Redis
Open-source, in-memory data structure store used for caching, messaging, and more.
Non-Relational Data on Azure
In-memory data store
A database system that primarily relies on main memory for data storage and retrieval.
Non-Relational Data on Azure
Azure Data Lake Storage (ADLS)
Massively scalable and secure data lake solution for big data analytics workloads.
Non-Relational Data on Azure
Hierarchical namespace
A feature in ADLS Gen2 that organizes objects into directories for better performance.
Non-Relational Data on Azure
Big data analytics
The process of examining large and varied data sets to uncover hidden patterns.
Non-Relational Data on Azure
Schema-on-read
A data processing approach where the schema is applied at the time of data retrieval.
Non-Relational Data on Azure
Azure Cache for Redis and Azure Data Lake Storage
Redis is for 'Rapid' data access, like a 'Race car' speeding up your app. Data Lake is for 'Large' amounts of 'Lazy' data, waiting to be analyzed.
Non-Relational Data on Azure
Azure Cache for Redis and Azure Data Lake Storage
The exam often asks you to distinguish between services based on their primary use case. Memorize that Azure Cache for Redis is for high-speed caching and application performance, while Azure Data Lake Storage is for massive, cost-effective storage of raw data for analytics. Keywords: 'low latency,' 'caching,' 'session state' for Redis; 'big data,' 'analytics,' 'raw data,' 'data lake' for ADLS.
Non-Relational Data on Azure
Azure Cache for Redis and Azure Data Lake Storage
Using Azure Cache for Redis for long-term, archival storage of large, raw datasets.
Non-Relational Data on Azure
Azure Cache for Redis and Azure Data Lake Storage
Attempting to use Azure Data Lake Storage for low-latency, real-time application caching.
Non-Relational Data on Azure
Azure Cache for Redis and Azure Data Lake Storage
Confusing the purpose of an operational cache with an analytical data lake.
Non-Relational Data on Azure
Consistency Model
Guarantees about data freshness after writes.
Non-Relational Data on Azure
Indexing
Data structures to speed up data retrieval.
Non-Relational Data on Azure
Strong Consistency
Reads always see the latest committed write.
Non-Relational Data on Azure
Eventual Consistency
Reads eventually reflect latest writes.
Non-Relational Data on Azure
Indexing Policy
Rules defining what data is indexed.
Non-Relational Data on Azure
Basic Management Tasks: Provisioning, Consistency, Indexing
To remember Cosmos DB's consistency models: 'S.B.S.C.E.' - 'Strong Bears Still Consume Eggs!'
Non-Relational Data on Azure
Basic Management Tasks: Provisioning, Consistency, Indexing
The DP-900 exam often tests knowledge of Azure Cosmos DB's consistency models. Memorize the names (Strong, Bounded Staleness, Session, Consistent Prefix, Eventual) and their general characteristics regarding latency, availability, and data freshness. Keywords like 'latest data' or 'most recent write' point to Strong consistency.
Non-Relational Data on Azure
Basic Management Tasks: Provisioning, Consistency, Indexing
Not customizing indexing policies, leading to bloated storage and slow queries.
Non-Relational Data on Azure
Basic Management Tasks: Provisioning, Consistency, Indexing
Using Strong consistency everywhere when not needed, resulting in higher latency and cost.
Non-Relational Data on Azure
Basic Management Tasks: Provisioning, Consistency, Indexing
Under-provisioning throughput (RU/s) for Azure Cosmos DB, causing throttling and poor performance.
Non-Relational Data on Azure
SQL API
Cosmos DB API for querying JSON documents using a SQL-like language.
Non-Relational Data on Azure
Gremlin API
Cosmos DB API for graph databases, using the Gremlin traversal language.
Non-Relational Data on Azure
OData Filter
Standardized query syntax used for filtering data in Azure Table Storage.
Non-Relational Data on Azure
PartitionKey
Part of the primary key in Table Storage, determines data distribution.
Non-Relational Data on Azure
RowKey
Part of the primary key in Table Storage, unique within a partition.
Non-Relational Data on Azure
Blob
An object stored in Azure Blob Storage, can be any type of file.
Non-Relational Data on Azure
Azure Storage Explorer
A GUI tool for managing Azure Storage resources and data.
Non-Relational Data on Azure
Querying and Manipulating Non-Relational Data
To remember the main Cosmos DB APIs: SQL for Structured Queries on JSON, Gremlin for Graphs' Relations, and Table for Tabular Key-Values. (S-G-T)
Non-Relational Data on Azure
Querying and Manipulating Non-Relational Data
The exam often tests your ability to match the correct query language or API to the specific non-relational data service. Keywords like 'document query' point to SQL API, 'graph traversal' to Gremlin API, and 'key-value filter' to OData expressions for Table Storage.
Non-Relational Data on Azure
Querying and Manipulating Non-Relational Data
Trying to perform complex joins directly within Azure Table Storage, which is not designed for relational queries.
Non-Relational Data on Azure
Querying and Manipulating Non-Relational Data
Using a generic SQL query for a Cosmos DB container configured with the Gremlin API; the query language must match the API.
Non-Relational Data on Azure
Querying and Manipulating Non-Relational Data
Forgetting to specify a partition key in Cosmos DB queries when applicable, leading to inefficient cross-partition requests.
Non-Relational Data on Azure
Business Intelligence (BI)
Analyzing historical data for insights into past business performance.
Analytics Workloads on Azure
Real-time Analytics
Processing data as it arrives to gain immediate insights and enable instant responses.
Analytics Workloads on Azure
Descriptive Analytics
Analyzing historical data to understand 'what happened'.
Analytics Workloads on Azure
Predictive Analytics
Using data to forecast future outcomes or probabilities.
Analytics Workloads on Azure
Prescriptive Analytics
Recommending actions to achieve desired outcomes.
Analytics Workloads on Azure
Data Latency
The delay between data generation and its availability for analysis.
Analytics Workloads on Azure
Understanding Analytics Workloads: BI, Big Data, Real-time
Remember the '3 Rs' for analytics: Reports (BI), Raw data (Big Data), Real-time (Stream Analytics).
Analytics Workloads on Azure
Understanding Analytics Workloads: BI, Big Data, Real-time
The exam often tests your ability to match a scenario description to the correct analytics workload type. Look for keywords like 'historical reports,' 'past trends' (BI); 'massive datasets,' 'unstructured data,' 'machine learning' (Big Data); or 'instant insights,' 'live streams,' 'immediate action' (Real-time).
Analytics Workloads on Azure
Understanding Analytics Workloads: BI, Big Data, Real-time
Confusing BI with real-time analytics; BI is historical, real-time is current.
Analytics Workloads on Azure
Understanding Analytics Workloads: BI, Big Data, Real-time
Assuming Big Data only refers to volume; variety and velocity are equally important.
Analytics Workloads on Azure
Understanding Analytics Workloads: BI, Big Data, Real-time
Thinking these workloads are mutually exclusive; they often work together in modern data architectures.
Analytics Workloads on Azure
Synapse Analytics
Unified analytics service for data warehousing and big data.
Analytics Workloads on Azure
Synapse SQL
SQL engine with dedicated (MPP) and serverless pools.
Analytics Workloads on Azure
Synapse Spark
Apache Spark-based engine for big data processing and ML.
Analytics Workloads on Azure
Synapse Data Explorer
Engine optimized for log and time series data analysis.
Analytics Workloads on Azure
Dedicated SQL Pool
Provisioned compute for enterprise data warehousing.
Analytics Workloads on Azure
Serverless SQL Pool
On-demand query service for data in data lakes.
Analytics Workloads on Azure
Synapse Studio
Web-based portal for managing Synapse Analytics.
Analytics Workloads on Azure
Modern Data Warehouse: Azure Synapse Analytics
SYNAPSE: S-QL (Data Warehousing), Y-arn (Spark), N-oSQL (Data Explorer), A-ll-in-one (Unified), P-ipelines (Integration), S-tudio (Management), E-cosystem (Azure Integration).
Analytics Workloads on Azure
Modern Data Warehouse: Azure Synapse Analytics
The exam frequently tests your understanding of the different compute engines within Synapse Analytics. Memorize the primary use cases for Synapse SQL (dedicated vs. serverless), Synapse Spark, and Synapse Data Explorer. Keywords like 'data warehousing,' 'big data processing,' 'log analysis,' and 'time series' are strong indicators.
Analytics Workloads on Azure
Modern Data Warehouse: Azure Synapse Analytics
Confusing dedicated SQL pools with serverless SQL pools: dedicated is for predictable, large-scale DW; serverless is for ad-hoc querying data lake.
Analytics Workloads on Azure
Modern Data Warehouse: Azure Synapse Analytics
Thinking Synapse Analytics is only for SQL data warehousing; it's a unified platform including Spark and Data Explorer for diverse data types.
Analytics Workloads on Azure
Modern Data Warehouse: Azure Synapse Analytics
Underestimating the importance of Data Lake Storage Gen2 as the foundational storage layer for Synapse Analytics.
Analytics Workloads on Azure
Azure Data Factory (ADF)
Cloud-based ETL/ELT service for orchestrating batch data movement and transformation.
Analytics Workloads on Azure
Azure Stream Analytics (ASA)
Real-time, serverless analytics service for processing fast-moving data streams.
Analytics Workloads on Azure
Pipeline (ADF)
A logical grouping of activities that perform a task in Azure Data Factory.
Analytics Workloads on Azure
Activity (ADF)
A processing step within an Azure Data Factory pipeline, e.g., copying data.
Analytics Workloads on Azure
Linked Service (ADF)
Connection information for a data store or compute resource in Azure Data Factory.
Analytics Workloads on Azure
SQL-like Query Language (ASA)
The language used in Azure Stream Analytics to define processing logic for streams.
Analytics Workloads on Azure
Data Ingestion and Processing: Data Factory, Stream Analytics
ADF is like a 'Data Factory' that builds things in batches, while ASA is like a 'Stream' that flows continuously, analyzing things as they pass by.
Analytics Workloads on Azure
Data Ingestion and Processing: Data Factory, Stream Analytics
For the DP-900 exam, precisely memorize that Azure Data Factory is for 'batch data movement and transformation' and Azure Stream Analytics is for 'real-time analytics on streaming data.' Look for keywords like 'scheduled,' 'daily report,' 'ETL' for ADF, and 'real-time,' 'IoT,' 'live dashboard' for ASA.
Analytics Workloads on Azure
Data Ingestion and Processing: Data Factory, Stream Analytics
Confusing batch processing with streaming processing: ADF is for batch, ASA is for streaming.
Analytics Workloads on Azure
Data Ingestion and Processing: Data Factory, Stream Analytics
Trying to use ADF for real-time alerts or immediate IoT insights; it's not designed for low-latency streaming.
Analytics Workloads on Azure
Data Ingestion and Processing: Data Factory, Stream Analytics
Assuming ASA can perform complex, multi-stage data transformations that are better suited for ADF or other compute services.
Analytics Workloads on Azure
Data Lake
A centralized repository for storing all structured and unstructured data at any scale.
Analytics Workloads on Azure
Azure Data Lake Storage Gen2
Scalable, cost-effective storage for big data analytics, built on Azure Blob Storage.
Analytics Workloads on Azure
Power BI
A suite of tools for data analysis, visualization, and sharing business insights.
Analytics Workloads on Azure
Hot tier
Azure storage tier for frequently accessed data, higher cost, lower access cost.
Analytics Workloads on Azure
Cool tier
Azure storage tier for infrequently accessed data, lower cost, higher access cost.
Analytics Workloads on Azure
Archive tier
Azure storage tier for rarely accessed data, lowest cost, highest access cost.
Analytics Workloads on Azure
Data Storage and Analysis: Data Lake, Power BI
Imagine a 'Lake' of raw data, where 'Power'ful 'BI'g fish (insights) swim, waiting to be caught. ADLS is the lake, Power BI is your fishing rod!
Analytics Workloads on Azure
Data Storage and Analysis: Data Lake, Power BI
The exam often tests the core differences between a data lake and a data warehouse, especially regarding schema-on-read vs. schema-on-write. Also, know that ADLS Gen2 is built on Azure Blob Storage and its key features like hierarchical namespace.
Analytics Workloads on Azure
Data Storage and Analysis: Data Lake, Power BI
Confusing a data lake with a data warehouse: A data lake stores raw, unstructured data (schema-on-read), while a data warehouse stores structured, processed data (schema-on-write).
Analytics Workloads on Azure
Data Storage and Analysis: Data Lake, Power BI
Underestimating the importance of data governance and security in a data lake: While flexible, data lakes still require robust security and access controls.
Analytics Workloads on Azure
Data Storage and Analysis: Data Lake, Power BI
Assuming Power BI is only for small datasets: Power BI can connect to and analyze very large datasets, especially when integrated with services like Azure Data Lake Storage.
Analytics Workloads on Azure
Azure Databricks
Apache Spark-based analytics platform for data engineering, science, ML.
Analytics Workloads on Azure
Apache Spark
Open-source distributed processing system for big data workloads.
Analytics Workloads on Azure
Azure HDInsight
Fully managed cloud service for open-source big data frameworks.
Analytics Workloads on Azure
Apache Hadoop
Open-source framework for distributed storage and processing of large datasets.
Analytics Workloads on Azure
Apache Kafka
Distributed streaming platform for building real-time data pipelines.
Analytics Workloads on Azure
Notebooks
Interactive web-based environments for writing and running code.
Analytics Workloads on Azure
Azure Databricks and HDInsight for Analytics
Imagine 'DataBricks' building a 'Sparkling' data mansion (optimized, unified). 'HDInsight' is like a 'High-Definition' view of many 'Open-Source' tools (Hadoop, Kafka, etc.) you can pick from.
Analytics Workloads on Azure
Azure Databricks and HDInsight for Analytics
The exam often asks to differentiate between Databricks and HDInsight. Remember: Databricks is Spark-focused and optimized for ML/DS; HDInsight supports a broader range of open-source frameworks like Hadoop, Kafka, and HBase.
Analytics Workloads on Azure
Azure Databricks and HDInsight for Analytics
Confusing Databricks as a general-purpose big data platform instead of a Spark-optimized one.
Analytics Workloads on Azure
Azure Databricks and HDInsight for Analytics
Assuming HDInsight only supports Spark; it supports many other open-source frameworks.
Analytics Workloads on Azure
Azure Databricks and HDInsight for Analytics
Choosing Databricks when direct access to low-level Hadoop/Kafka cluster configuration is critical.
Analytics Workloads on Azure