A database object whose content is defined by a query.

A database object that helps improve the speed of data retrieval.

A relational database must be used when

Your company needs to implement a relational database in Azure. The solution must minimize ongoing maintenance. Which Azure service should you use?

Azure Resource Manager is a JSON file that describes the architecture of your project.

It refers to the public web-based interface for management of Azure platform.

It is designed for self-service, customizable and simple tasks.

It is a set of commands used to create and manage Azure resources that is available across Azure services and is designed to get you working quickly with Azure, with an emphasis on automation especially if you are working in Linux.

It is a set of cmdlets for managing Azure resources that provides powerful features for automation especially for those working in Windows although it is also supported in Linux and MacOS.

It is an interactive, authenticated, browser-accessible shell for managing Azure resources where you can choose to use either Bash (for Azure CLI snippets) or PowerShell (for Azure PowerShell snippets).

it is an interactive, authenticated, browser-accessible shell for managing Azure resources where you can choose to use either Bash (for Azure CLI snippets) or PowerShell (for Azure PowerShell snippets).

Azure Cloud Shell has a multiple client interfaces such as Azure Portal Integration, Shell Porta, Visual Studio Code Extension, Windows Terminal, Azure Mobile App, Microsoft Docs Integration.

It is one of the automation tools used in managing Azure resources through a terminal.

When should you use Azure Synapse Analytics?

What is the purpose of data ingestion?

What is the primary difference between a data lake and a data warehouse?

Which component of an Azure Data Factory can be triggered to run data ingestion tasks?

When might you use PolyBase?

Which of these services can be used to ingest data into Azure Synapse Analytics?

You have a large amount of data held in files in Azure Data lake storage. You want to retrieve the data in these files and use it to populate tables held in Azure Synapse Analytics. Which processing option is most appropriate?

Which of the components of Azure Synapse Analytics allows you to train AI models using AzureML?

In Azure Databricks how do you change the language a cell uses?

What is the common flow of activity in Power BI?

Which of the following are building blocks of Power BI?

A collection of ready-made visuals, pre-arranged in dashboards and reports is called what in Power BI?

How is data in a relational table organized?

Which of the following is an example of unstructured data?

Which of the following is an example of streaming dataset?

Which of the following tasks is a role of a database administrator?

Which of the following tools is a visualization and reporting tool?

Which of the following roles is not a data job role?

Which one of the following statements is a characteristic of a relational database?

What is an index?

Which one of the following statements is a benefit of using PaaS service, instead of an on-premises system to run your database management systems?

Which of the following services should you use to implement a non-relational database?

Which of the following is a characteristic of non-relational databases?

You are building a system that monitors the temperature throughout a set of office blocks, and sets the air conditioning in each room in each block to maintain a pleasant ambient temperature. Your system has to manage the air conditioning in several thousand buildings spread across the country or region, each building typically contains at least 100 air-conditioned rooms. What type of NoSQL data store is most appropriate for capturing the temperature data to enable it processed quickly?

What is data ingestion?

Which of the following visuals displays the major contributors to a selected result or value?

Which type of analytics helps answer questions about what has happened in the past?

Which deployment requires the fewest changes when migrating an existing SQL Server on-premises solution?

Which of the following statements is true about SQL Server running on a virtual machine?

Which of the following statement is true about Azure SQL Database?

When using an Azure SQL Database managed instance, what is the simplest way to implement backups?

What is the best way to transfer the data in a PostgreSQL database running on-premises into a database running Azure Database for PostgreSQL service?

What are the elements of an Azure Table storage key?

When should you use a block blob, an when you should use a page blob?

Why might you use Azure File storage?

You are building a system that monitors the temperature throughout a set of office blocks, and sets the air conditioning in each room in each block to maintain a pleasant ambient temperature. Your system has to manage the air conditioning in several thousand buildings spread across the country or region, each building typically contains at least 100 air-conditioned rooms. What type of NoSQL data store is most appropriate for capturing the temperature data to enable it processed quickly?

What is provisioning?

What is a security principal?

Which of the following is an advantage of using multi-region replication with Cosmos DB?

It is designed for customers looking to migrate a large number of apps from an on-premises or IaaS, self-built, or ISV provided environment to a fully managed PaaS cloud environment, with as low a migration effort as possible.

It is a globally distributed NoSQL database with low latency, multi-master, and perfect for serverless.

It is a fully fledged SQL Server on IaaS

Columns represent the attributes of the entity in a relational database.

Rows represent each instance of a data entity in a relational data base.

Structured data is often stored in a database in which multiple tables can reference one another by using key values in a relational model.

Semi-structured data is information that has some structure, but which allows for some variation between entity instances.

One common format for semi-structured data is JavaScript Object Notation (JSON).

There are two broad categories of data store in common use:

File stores

Databases

The ability to store data in files is a core element of any computing system.

The most common format for delimited data is comma-separated values (CSV) in which fields are separated by commas, and rows are terminated by a carriage return / new line.

Delimited text is a good choice for structured data that needs to be accessed by a wide range of applications and services in a human-readable format.

It is a ubiquitous format in which a hierarchical document schema is used to define data entities (objects) that have multiple attributes.

JSON a flexible format that's good for both structured and semi-structured data.

It is a human-readable data format that was popular in the 1990s and 2000s. I

XML uses tags enclosed in angle-brackets (<../>) to define elements and attributes.

Common types of data stored as BLOB include images, video, audio, and application-specific documents.

Avro is a row-based format created by Apache Spark.

Each record in Avro contains a header that describes the structure of the data in the record. This header is stored as JSON. The data is stored as binary information.

Avro is a good format for compressing data and minimizing storage and network bandwidth requirements.

ORC (Optimized Row Columnar format) organizes data into columns rather than rows and was developed by HortonWorks for optimizing read and write operations in Apache Hive.

Hive is a data warehouse system that supports fast data summarization and querying over large datasets.

A footer in ORC holds statistical information (count, sum, max, min, and so on) for each of its column.

Parquet is columnar data format created by Cloudera and Twitter.

Parquet specializes in storing and processing nested data types efficiently. It supports very efficient compression and encoding schemes.

Relational databases are commonly used to store and query structured data.

It is a type of Non-relational database in which each record consists of a unique key and an associated value, which can be in any format.

It refers to a specific form of key-value database in which the value is a JSON.

This type of Non-relational database store tabular data comprising rows and columns, in which columns can be divided into groups known as column-families.

This type of Non-relational database store entities as nodes with links to define relationships between them.

Transactional systems are often high-volume, sometimes handling many millions of transactions in a single day.

OLTP solutions rely on a database system in which data storage is optimized for both read and write operations.

OLAP systems enforce transactions that support so-called ACID semantics.

Identify what component of the ACID semantics is being described in statement.

Each transaction is treated as a single unit, which succeeds completely or fails completely.

Identify what component of the ACID semantics is being described in statement.

Transactions can only take the data in the database from one valid state to another.

Identify what component of the ACID semantics is being described in statement.

Concurrent transactions cannot interfere with one another, and must result in a consistent database state.

Identify what component of the ACID semantics is being described in statement.

When a transaction has been committed, it will remain committed.

OLTP systems are typically used to support live applications that process business data - often referred to as line of business (LOB) applications.

Analytical data processing typically uses read-only (or read-mostly) systems that store vast volumes of historical data or business metrics.

Data in the data warehouse may be aggregated and loaded into an online analytical processing (OLAP) model, or cube.

The data in the data lake, data warehouse, and analytical model can be queried to produce reports, visualizations, and dashboards.

In Analytical data processing, an extract, transform, and load (ETL) process copies data from files and OLTP databases into a data warehouse that is optimized for read activity.

In Analytical data processing, data files may be stored in a central data lake for analysis.

Data warehouses are an established way to store data in a relational schema that is optimized for read operations – primarily queries to support reporting and data visualization.

The data warehouse schema may require some denormalization of data in an OLTP data source (introducing some duplication to make queries perform faster).

What is a data warehouse?

Responsible in managing databases, assigning permissions to users, storing backup copies of data and restoring data in the event of a failure.

Responsible in managing infrastructure and processes for data integration across the organization, applying data cleaning routines, identifying data governance rules, and implementing pipelines to transfer and transform data between systems.

Responsible in exploring and analyzing data to create visualizations and charts that enable organizations to make informed decisions.

Data Administrator work with stakeholders to implement policies, tools, and processes for backup and recovery plans to recover following a natural disaster or human-made error.

Database administrator is responsible for managing the security of the data in the database, granting privileges over the data, granting or denying access to users as appropriate.

A data engineer collaborates with stakeholders to design and implement data-related workloads, including data ingestion pipelines, cleansing and transformation activities, and data stores for analytical workloads.

A Data Engineer is responsible for ensuring that the privacy of data is maintained within the cloud and spanning from on-premises to the cloud data stores.

A Data Engineer owns the management and monitoring of data pipelines to ensure that data loads perform as expected.

A data engineer processes raw data into relevant insights based on identified business requirements to deliver relevant insights.

Azure SQL is the collective name for a family of relational database solutions based on the Microsoft SQL

It is a fully managed platform-as-a-service (PaaS) database hosted in Azure.

A hosted instance of SQL Server with automated maintenance, which allows more flexible configuration than Azure SQL DB but with more administrative responsibility for the owner.

A virtual machine with an installation of SQL Server, allowing maximum configurability with full management responsibility.

Data engineers may use Azure SQL database systems as sources for data pipelines that perform extract, transform, and load (ETL) operations to ingest the transactional data into an analytical system.

Data analysts may query Azure SQL databases directly to create reports, though in large organizations the data is generally combined with data from other sources in an analytical data store to support enterprise analytics.

It is a simple-to-use open-source database management system that is commonly used in Linux, Apache, MySQL, and PHP (LAMP) stack apps.

A newer database management system, created by the original developers of MySQL. The database engine has since been rewritten and optimized to improve performance.

MariaDB offers compatibility with Oracle Database.

A hybrid relational-object database. In here, you can store data in relational tables, but also enables you to store custom data types, with their own non-relational properties.

It is a global-scale non-relational (NoSQL) database system that supports multiple application programming interfaces (APIs), enabling you to store and manage data as JSON documents, key-value pairs, column-families, and graphs.

It is a core Azure service that enables you to store data in:

Blob containers, File shares and Tables.

It is an Azure service that enables you to define and schedule data pipelines to transfer and transform data.

It is a comprehensive, unified data analytics solution that provides a single service interface for multiple analytical capabilities, including:

Pipelines, SQL , Apache Spark programming languages and APIs, including Java, Scala, Python, and SQL, Azure Synapse Data Explorer.

It combines the Apache Spark data processing platform with SQL database semantics and an integrated management interface to enable large-scale data analytics.

Data engineers can use Azure Synapse Analytics to create a unified data analytics solution that combines data ingestion pipelines, data warehouse storage, and data lake storage through a single service.

Data Analysts can use the native notebook support in Azure Databricks to query and visualize data in an easy to use web-based interface.

It is an Azure service that provides Azure-hosted clusters for popular Apache open-source big data processing technologies, including:

Apache Spark, Apache Hadoop, Apache HBase , Apache Storm.

It is a real-time stream processing engine that captures a stream of data from an input, applies a query to extract and manipulate data from the input stream, and writes the results to an output for analysis or further processing.

It is a standalone service that offers the same high-performance querying of log and telemetry data as the Azure Synapse Data Explorer runtime in Azure Synapse Analytics.

Data analysts can use Azure Data Explorer to query and analyze data that includes a timestamp attribute, such as is typically found in log files and Internet-of-things (IoT) telemetry data.

Data engineers can incorporate Azure Stream Analytics into data analytics architectures that capture streaming data for ingestion into an analytical data store or for real-time visualization.

Data engineers can use Azure HDInsight to support big data analytics workloads that depend on multiple open-source technologies.

An open-source system for real-time data processing through a topology of spouts and bolts.

A message broker for data stream processing.

An open-source system for large-scale NoSQL data storage and querying.

A distributed system that uses MapReduce jobs to process large volumes of data efficiently across multiple cluster nodes.

A distributed data processing system that supports multiple programming languages and APIs, including Java, Scala, Python, and SQL.

It provides a solution for enterprise-wide data governance and discoverability. You can use it to create a map of your data and track data lineage across multiple data sources and systems, enabling you to find trustworthy data for analysis and reporting.

It is a platform for analytical data modeling and reporting that data analysts can use to create and share interactive data visualizations.

Which single service would you use to implement data pipelines, SQL analytics, and Spark analytics?

Which role is most likely to use Azure Data Factory to define a data pipeline for an ETL process?

Which one of the following tasks is the responsibility of a database administrator?