Microsoft DP-203 Practice Test - Questions Answers, Page 3

Box 1: .partitionBy

Incorrect Answers:

.format:

Method: format():

Arguments: "parquet", "csv", "txt", "json", "jdbc", "orc", "avro", etc.

.bucketBy:

Method: bucketBy()

Arguments: (numBuckets, col, col..., coln)

The number of buckets and names of columns to bucket by. Uses Hive’s bucketing scheme on a filesystem.

Box 2: ("Year", "Month", "Day","GeographyRegionID")

Specify the columns on which to do the partition. Use the date columns followed by the GeographyRegionID column.

Box 3: .saveAsTable("/DBTBL1")

Method: saveAsTable()

Argument: "table_name"

The table to save to.

Reference:

https://www.oreilly.com/library/view/learning-spark-2nd/9781492050032/ch04.html

https://docs.microsoft.com/en-us/azure/databricks/delta/delta-batch

Box 1: GRANT

You can implement column-level security with the GRANT T-SQL statement. With this mechanism, both SQL and Azure Active Directory (Azure AD) authentication are supported.

Box 2: CREATE SECURITY POLICY

Implement RLS by using the CREATE SECURITY POLICY Transact-SQL statement, and predicates created as inline table-valued functions.

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/column-level-security

https://docs.microsoft.com/en-us/sql/relational-databases/security/row-level-security

Box 1: Premium

Credential passthrough requires an Azure Databricks Premium Plan

Box 2: Azure Data Lake Storage credential passthrough

You can access Azure Data Lake Storage using Azure Active Directory credential passthrough. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.

Reference:

https://docs.microsoft.com/en-us/azure/databricks/security/credential-passthrough/adls-passthrough

Box 1: Personal access tokens

You can use storage shared access signatures (SAS) to access an Azure Data Lake Storage Gen2 storage account directly. With SAS, you can restrict access to a storage account using temporary tokens with fine-grained access control.

You can add multiple storage accounts and configure respective SAS token providers in the same Spark session.

Box 2: Azure Active Directory credential passthrough

You can authenticate automatically to Azure Data Lake Storage Gen1 (ADLS Gen1) and Azure Data Lake Storage Gen2 (ADLS Gen2) from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.

After configuring Azure Data Lake Storage credential passthrough and creating storage containers, you can access data directly in Azure Data Lake Storage Gen1 using an adl:// path and Azure Data Lake Storage Gen2 using an abfss:// path:

Reference:

https://docs.microsoft.com/en-us/azure/databricks/data/data-sources/azure/adls-gen2/azure-datalake-gen2-sas-access

https://docs.microsoft.com/en-us/azure/databricks/security/credential-passthrough/adls-passthrough

Box 1: Azure AD authentication

Azure AD authentication has the option to include MFA.

Box 2: Contained database users

Azure AD authentication uses contained database users to authenticate identities at the database level.

Reference:

https://docs.microsoft.com/en-us/azure/azure-sql/database/authentication-mfa-ssms-overview

https://docs.microsoft.com/en-us/azure/azure-sql/database/authentication-aad-overview

Hash-distributed tables improve query performance on large fact tables. Columnstore indexes can achieve up to 100x better performance on analytics and data warehousing workloads and up to 10x better data compression than traditional rowstore indexes. Incorrect Answers:

C, D: Round-robin tables are useful for improving loading speed.

Reference: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute https://docs.microsoft.com/en-us/sql/relational-databases/indexes/columnstore-indexes-query-performance

Clustered columnstore indexes are one of the most efficient ways you can store your data in dedicated SQL pool. Columnstore tables won't benefit a query unless the table has more than 60 million rows.

Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool

Cluster-scoped Init Scripts: Init scripts are shell scripts that run during the startup of each cluster node before the Spark driver or worker JVM starts. Databricks customers use init scripts for various purposes such as installing custom libraries, launching background processes, or applying enterprise security policies. Logs for Cluster-scoped init scripts are now more consistent with Cluster Log Delivery and can be found in the same root folder as driver and executor logs for the cluster.

Reference: https://databricks.com/blog/2018/08/30/introducing-cluster-scoped-init-scripts.html