Explanation:

Box 1: .partitionBy
Example:
df.write.partitionBy("y","m","d")
mode(SaveMode.Append)
parquet("/data/hive/warehouse/db_name.db/" + tableName)
Box 2: ("Year","Month","Day","Hour","StoreID")
Box 3: .parquet("/Purchases")
Reference:
https://intellipaat.com/community/11744/how-to-partition-and-write-dataframe-in-spark-without-deleting- partitions-with-no-new-data

Explanation:

Reference:
https://docs.microsoft.com/en-us/sql/t-sql/database-console-commands/dbcc-checkdb-transact-sql?view=sql-serv

A replicated table has a full copy of the table available on every Compute node. Queries run fast on replicated tables since joins on replicated tables don't require data movement. Replication requires extra storage, though, and isn't practical for large tables.
Incorrect Answers:
C: A round-robin distributed table distributes table rows evenly across all distributions. The assignment of rows to distributions is random. Unlike hash-distributed tables, rows with equal values are not guaranteed to be assigned to the same distribution.
As a result, the system sometimes needs to invoke a data movement operation to better organize your data before it can resolve a query.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute

Explanation:

Explanation:

Box 1: The server, the elastic pool, and the database
Senario:
SalesSQLDb1 is in an elastic pool named SalesSQLDb1Pool.
Litware technical requirements include: all SQL Server and Azure SQL Database metrics related to CPU and storage usage and limits must be analyzed by using Azure built-in functionality.
Box 2: Azure Event hubs
Scenario: Migrate ManufacturingSQLDb1 to the Azure virtual machine platform.
Event hubs are able to handle custom metrics.
Topic 3, ADatum Corporation
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
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To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview
ADatum Corporation is a retailer that sells products through two sales channels: retail stores and a website.
Existing Environment
ADatum has one database server that has Microsoft SQL Server 2016 installed. The server hosts three mission-critical databases named SALESDB, DOCDB, and REPORTINGDB.
SALESDB collects data from the stores and the website.
DOCDB stores documents that connect to the sales data in SALESDB. The documents are stored in two different JSON formats based on the sales channel.
REPORTINGDB stores reporting data and contains several columnstore indexes. A daily process creates reporting data in REPORTINGDB from the data in SALESDB. The process is implemented as a SQL Server Integration Services (SSIS) package that runs a stored procedure from SALESDB.
Requirements
Planned Changes
ADatum plans to move the current data infrastructure to Azure. The new infrastructure has the following requirements:
* Migrate SALESDB and REPORTINGDB to an Azure SQL database.
* Migrate DOCDB to Azure Cosmos DB.
* The sales data, including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytics process will perform aggregations that must be done continuously, without gaps, and without overlapping.
* As they arrive, all the sales documents in JSON format must be transformed into one consistent format.
* Azure Data Factory will replace the SSIS process of copying the data from SALESDB to REPORTINGDB.
Technical Requirements
The new Azure data infrastructure must meet the following technical requirements:
* Data in SALESDB must encrypted by using Transparent Data Encryption (TDE). The encryption must use your own key.
* SALESDB must be restorable to any given minute within the past three weeks.
* Real-time processing must be monitored to ensure that workloads are sized properly based on actual usage patterns.
* Missing indexes must be created automatically for REPORTINGDB.
* Disk IO, CPU, and memory usage must be monitored for SALESDB.