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What three main types of workload can be found in a typical modern data warehouse? - Streaming Data - Batch Data - Relational Data A ____________________ is a continuous flow of information, where continuous does not necessarily mean regular or constant. data stream __________________________ focuses on moving and transforming data at rest. Batch processing This data is usually well organized and easy to understand. Data stored in relational databases is an example, where table rows and columns represent entities and their attributes. Structured Data This data usually does not come from relational stores, since even if it could have some sort of internal organization, it is not mandatory. Good examples are XML and JSON files. Semi-structured Data Data with no explicit data model falls in this category. Good examples include binary file formats (such as PDF, Word, MP3, and MP4), emails, and tweets. Unstructured Data What type of analysis answers the question "What happened?" Descriptive Analysis What type of analysis answers the question "Why did it happen?" Diagnostic Analysis What type of analysis answers the question "What will happen?" Predictive Analysis What type of analysis answers the question "How can we make it happen?" Prescriptive Analysis The two main kinds of workloads are ______________ and _________________. extract-transform-load (ETL) extract-load-transform (ELT) ______ is a traditional approach and has established best practices. It is more commonly found in on-premises environments since it was around before cloud platforms. It is a process that involves a lot o data movement, which is something you want to avoid on the cloud if possible due to its resource-intensive nature. ETL ________ seems similar to ETL at first glance but is better suited to big data scenarios since it leverages the scalability and flexibility of MPP engines like Azure Synapse Analytics, Azure Databricks, or Azure HDInsight. ELT _______________ is a cloud service that lets you implement, manage, and monitor a cluster for Hadoop, Spark, HBase, Kafka, Store, Hive LLAP, and ML Service in an easy and effective way. Azure HDInsight _____________ is a cloud service from the creators of Apache Spark, combined with a great integration with the Azure platform. Azure Databricks ____________ is the new name for Azure SQL Data Warehouse, but it extends it in many ways. It aims to be the comprehensive analytics platform, from data ingestion to presentation, bringing together one-click data exploration, robust pipelines, enterprise-grade database service, and report authoring. Azure Synapse Analytics A ___________ displays attribute members on rows and measures on columns. A simple ____________ is generally easy for users to understand, but it can quickly become difficult to read as the number of rows and columns increases. table A _____________ is a more sophisticated table. It allows for attributes also on columns and can auto-calculate subtotals. matrix Objects in which things about data should be captured and stored are called: ____________. A. tables B. entities C. rows D. columns B. entities You need to process data that is generated continuously and near real-time responses are required. You should use _________. A. batch processing B. scheduled data processing C. buffering and processing D. streaming data processing D. streaming data processing A. Extract, Transform, Load (ETL) B. Extract, Load, Transform (ELT) 1. Optimize data privacy. 2. Provide support for Azure Data Lake. 1 - A 2 - B Extract, Transform, Load (ETL) is the correct approach when you need to filter sensitive data before loading the data into an analytical model. It is suitable for simple data models that do not require Azure Data Lake support. Extract, Load, Transform (ELT) is the correct approach because it supports Azure Data Lake as the data store and manages large volumes of data. The technique that provides recommended actions that you should take to achieve a goal or target is called _____________ analytics. A. descriptive B. diagnostic C. predictive D. prescriptive D. prescriptive A. Tables B. Indexes C. Views D. Keys 1. Create relationships. 2. Improve processing speed for data searches. 3. Store instances of entities as rows. 4. Display data from predefined queries. 1 - D 2 - B 3 - A 4 - C The process of splitting an entity into more than one table to reduce data redundancy is called: _____________. A. deduplication B. denormalization C. normalization D. optimization C. normalization Azure SQL Database is an example of ________________ -as-a-service. A. platform B. infrastructure C. software D. application A. platform A. Azure Data Studio B. Azure Query editor C. SQL Server Data Tools 1. Query data while working within a Visual Studio project. 2. Query data located in a non-Microsoft platform. 3. Query data from within the Azure portal 1 - C 2 - A 3 - B The act of increasing or decreasing the resources that are available for a service is called: _____________. A. computing B. provisioning C. networking D. scaling D. scaling A. JOIN B. WHERE C. SUM D. COUNT 1. Filter records. 2. Combine rows from multiple tables. 3. Calculate the total value of a numeric column. 4. Determine the number of rows retrieved. 1 - B 2 - A 3 - C 4 - D What are three characteristics of non-relational data? Each correct answer presents a complete solution. A. Forced schema on data structures B. Flexible storage of ingested data C. Entities are self-describing D. Entities may have different fields E. Each row has the exact same columns B. Flexible storage of ingested data C. Entities are self-describing D. Entities may have different fields You have data that consists of JSON-based documents. You need to store the data in an Azure environment that supports efficient non-key, field-based searching. You should use _______________________ as the data store. A. Azure Table Storage B. Azure Blob Storage C. Azure File Storage D. Azure Cosmos DB D. Azure Cosmos DB