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NEW QUESTION 91
You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1. You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1. You plan to insert data from the files into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1. You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1.
Solution: In an Azure Synapse Analytics pipeline, you use a Get Metadata activity that retrieves the DateTime of the files.
Does this meet the goal?

A.    Yes
B.    No

Answer: B
Explanation:
Instead use a serverless SQL pool to create an external table with the extra column.
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/create-use-external-tables

NEW QUESTION 92
You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1. You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1. You plan to insert data from the files into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1. You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1.
Solution: You use a dedicated SQL pool to create an external table that has an additional DateTime column.
Does this meet the goal?

A.    Yes
B.    No

Answer: B
Explanation:
Instead use a serverless SQL pool to create an external table with the extra column. Note: In dedicated SQL pools you can only use Parquet native external tables. Native external tables are generally available in serverless SQL pools.
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/create-use-external-tables

NEW QUESTION 93
You have a SQL pool in Azure Synapse that contains a table named dbo.Customers. The table contains a column name Email. You need to prevent nonadministrative users from seeing the full email addresses in the Email column. The users must see values in a format of [email protected] instead. What should you do?

A.    From the Azure portal, set a mask on the Email column.
B.    From the Azure portal, set a sensitivity classification of Confidential for the Email column.
C.    From Microsoft SQL Server Management Studio, set an email mask on the Email column.
D.    From Microsoft SQL Server Management Studio, grant the SELECT permission to the users for all the columns in the dbo.Customers table except Email.

Answer: B

NEW QUESTION 94
You have an Azure Databricks workspace named workspace1 in the Standard pricing tier. Workspace1 contains an all-purpose cluster named cluster1. You need to reduce the time it takes for cluster1 to start and scale up. The solution must minimize costs. What should you do first?

A.    Upgrade workspace1 to the Premium pricing tier.
B.    Configure a global init script for workspace1.
C.    Create a pool in workspace1.
D.    Create a cluster policy in workspace1.

Answer: C
Explanation:
You can use Databricks Pools to Speed up your Data Pipelines and Scale Clusters Quickly. Databricks Pools, a managed cache of virtual machine instances that enables clusters to start and scale 4 times faster.
https://databricks.com/blog/2019/11/11/databricks-pools-speed-up-data-pipelines.html

NEW QUESTION 95
You are designing a streaming data solution that will ingest variable volumes of data. You need to ensure that you can change the partition count after creation. Which service should you use to ingest the data?

A.    Azure Event Hubs Standard
B.    Azure Stream Analytics
C.    Azure Data Factory
D.    Azure Event Hubs Dedicated

Answer: D
Explanation:
The partition count for an event hub in a dedicated Event Hubs cluster can be increased after the event hub has been created.
Incorrect:
Not A: For Azure Event standard hubs, the partition count isn’t changeable, so you should consider long-term scale when setting partition count.
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-features#partitions

NEW QUESTION 96
You have an Azure Synapse Analytics Apache Spark pool named Pool1. You plan to load JSON files from an Azure Data Lake Storage Gen2 container into the tables in Pool1. The structure and data types vary by file. You need to load the files into the tables. The solution must maintain the source data types. What should you do?

A.    Load the data by using PySpark.
B.    Load the data by using the OPENROWSET Transact-SQL command in an Azure Synapse Analytics serverless SQL pool.
C.    Use a Get Metadata activity in Azure Data Factory.
D.    Use a Conditional Split transformation in an Azure Synapse data flow.

Answer: B
Explanation:
Serverless SQL pool can automatically synchronize metadata from Apache Spark. A serverless SQL pool database will be created for each database existing in serverless Apache Spark pools. Serverless SQL pool enables you to query data in your data lake. It offers a T-SQL query surface area that accommodates semi-structured and unstructured data queries. To support a smooth experience for in place querying of data that’s located in Azure Storage files, serverless SQL pool uses the OPENROWSET function with additional capabilities. The easiest way to see to the content of your JSON file is to provide the file URL to the OPENROWSET function, specify csv FORMAT.
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-json-files
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-data-storage

NEW QUESTION 97
You are designing a date dimension table in an Azure Synapse Analytics dedicated SQL pool. The date dimension table will be used by all the fact tables. Which distribution type should you recommend to minimize data movement?

A.    HASH
B.    REPLICATE
C.    ROUND_ROBIN

Answer: B
Explanation:
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:
Not 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.
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute

NEW QUESTION 98
You have an Azure Synapse Analytics workspace named WS1 that contains an Apache Spark pool named Pool1. You plan to create a database named DB1 in Pool1. You need to ensure that when tables are created in DB1, the tables are available automatically as external tables to the built-in serverless SQL pool. Which format should you use for the tables in DB1?

A.    JSON
B.    CSV
C.    Parquet
D.    ORC

Answer: C
Explanation:
Serverless SQL pool can automatically synchronize metadata from Apache Spark. A serverless SQL pool database will be created for each database existing in serverless Apache Spark pools. For each Spark external table based on Parquet and located in Azure Storage, an external table is created in a serverless SQL pool database. As such, you can shut down your Spark pools and still query Spark external tables from serverless SQL pool.
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-storage-files-spark-tables

NEW QUESTION 99
You are designing an anomaly detection solution for streaming data from an Azure IoT hub. The solution must meet the following requirements:
– Send the output to an Azure Synapse.
– Identify spikes and dips in time series data.
– Minimize development and configuration effort.
Which should you include in the solution?

A.    Azure SQL Database
B.    Azure Databricks
C.    Azure Stream Analytics

Answer: C
Explanation:
Anomalies can be identified by routing data via IoT Hub to a built-in ML model in Azure Stream Analytics.
https://docs.microsoft.com/en-us/learn/modules/data-anomaly-detection-using-azure-iot-hub/
https://docs.microsoft.com/en-us/azure/stream-analytics/azure-synapse-analytics-output

NEW QUESTION 100
You are creating a new notebook in Azure Databricks that will support R as the primary language but will also support Scala and SQL. Which switch should you use to switch between languages?

A.    \\[<language>]
B.    %<language>
C.    \\[<language>]
D.    @<language>

Answer: B
Explanation:
You can override the default language by specifying the language magic command %<language> at the beginning of a cell. The supported magic commands are: %python, %r, %scala, and %sql.
https://docs.microsoft.com/en-us/azure/databricks/notebooks/notebooks-use

NEW QUESTION 101
You plan to build a structured streaming solution in Azure Databricks. The solution will count new events in five-minute intervals and report only events that arrive during the interval. The output will be sent to a Delta Lake table. Which output mode should you use?

A.    complete
B.    append
C.    update

Answer: A
Explanation:
Complete mode: You can use Structured Streaming to replace the entire table with every batch.
Incorrect:
Not B: By default, streams run in append mode, which adds new records to the table.
https://docs.databricks.com/delta/delta-streaming.html

NEW QUESTION 102
You are developing an application that uses Azure Data Lake Storage Gen 2. You need to recommend a solution to grant permissions to a specific application for a limited time period. What should you include in the recommendation?

A.    role assignments
B.    account keys
C.    shared access signatures (SAS)
D.    Azure Active Directory (Azure AD) identities

Answer: C
Explanation:
A shared access signature (SAS) provides secure delegated access to resources in your storage account.
https://docs.microsoft.com/en-us/azure/storage/common/storage-sas-overview

NEW QUESTION 103
You are designing an enterprise data warehouse in Azure Synapse Analytics that will contain a table named Customers. Customers will contain credit card information. You need to recommend a solution to provide salespeople with the ability to view all the entries in Customers. The solution must prevent all the salespeople from viewing or inferring the credit card information. What should you include in the recommendation?

A.    row-level security
B.    data masking
C.    Always Encrypted
D.    column-level security

Answer: B
Explanation:
Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics support dynamic data masking. Dynamic data masking limits sensitive data exposure by masking it to non-privileged users. The Credit card masking method exposes the last four digits of the designated fields and adds a constant string as a prefix in the form of a credit card.

NEW QUESTION 104
You have a data warehouse in Azure Synapse Analytics. You need to ensure that the data in the data warehouse is encrypted at rest. What should you enable?

A.    Transparent Data Encryption (TDE).
B.    Advanced Data Security for this database.
C.    Always Encrypted for all columns.
D.    Secure transfer required.

Answer: A
Explanation:
Transparent data encryption (TDE) helps protect Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics against the threat of malicious offline activity by encrypting data at rest.
https://docs.microsoft.com/en-us/azure/azure-sql/database/transparent-data-encryption-tde-overview

NEW QUESTION 105
You are designing a security model for an Azure Synapse Analytics dedicated SQL pool that will support multiple companies. You need to ensure that users from each company can view only the data of their respective company. Which two objects should you include in the solution? (Each correct answer presents part of the solution. Choose two.)

A.    a column encryption key
B.    asymmetric keys
C.    a function
D.    a custom role-based access control (RBAC) role
E.    a security policy

Answer: DE
Explanation:
Azure RBAC is used to manage who can create, update, or delete the Synapse workspace and its SQL pools, Apache Spark pools, and Integration runtimes. Define and implement network security configurations for resources related to your dedicated SQL pool with Azure Policy.
https://docs.microsoft.com/en-us/azure/synapse-analytics/security/synapse-workspace-synapse-rbac
https://docs.microsoft.com/en-us/security/benchmark/azure/baselines/synapse-analytics-security-baseline

NEW QUESTION 106
You have an Azure subscription that contains an Azure Data Factory version 2 (V2) data factory named df1. DF1 contains a linked service. You have an Azure Key vault named vault1 that contains an encryption kay named key1. You need to encrypt df1 by using key1. What should you do first?

A.    Disable purge protection on vault1.
B.    Remove the linked service from df1.
C.    Create a self-hosted integration runtime.
D.    Disable soft delete on vault1.

Answer: B
Explanation:
A customer-managed key can only be configured on an empty data Factory. The data factory can’t contain any resources such as linked services, pipelines and data flows. It is recommended to enable customer-managed key right after factory creation. Note: Azure Data Factory encrypts data at rest, including entity definitions and any data cached while runs are in progress. By default, data is encrypted with a randomly generated Microsoft-managed key that is uniquely assigned to your data factory.
Incorrect:
Not A and D: Should enable Soft Delete and Do Not Purge on Azure Key Vault. Using customer-managed keys with Data Factory requires two properties to be set on the Key Vault, Soft Delete and Do Not Purge. These properties can be enabled using either PowerShell or Azure CLI on a new or existing key vault.
https://docs.microsoft.com/en-us/azure/data-factory/enable-customer-managed-key

NEW QUESTION 107
HotSpot
You plan to develop a dataset named Purchases by using Azure Databricks. Purchases will contain the following columns:
– ProductID
– ItemPrice
– LineTotal
– Quantity
– StoreID
– Minute
– Month
– Hour
– Year
– Day
You need to store the data to support hourly incremental load pipelines that will vary for each StoreID. The solution must minimize storage costs. How should you complete the code? (To answer, select the appropriate options in the answer area.)
DP-300-Exam-Questions-1071

Answer:
DP-300-Exam-Questions-1072
Explanation:
https://intellipaat.com/community/11744/how-to-partition-and-write-dataframe-in-spark-without-deleting-partitions-with-no-new-data

NEW QUESTION 108
Drag and Drop
You plan to create a table in an Azure Synapse Analytics dedicated SQL pool. Data in the table will be retained for five years. Once a year, data that is older than five years will be deleted. You need to ensure that the data is distributed evenly across partitions. The solutions must minimize the amount of time required to delete old data. How should you complete the Transact-SQL statement? (To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.)
DP-300-Exam-Questions-1081

Answer:
DP-300-Exam-Questions-1082
Explanation:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool

NEW QUESTION 109
……


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