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You have an Azure Storage account that contains 100 GB of files. The files contain rows of text and numerical values. 75% of the rows contain description data that has an average length of 1.1 MB. You plan to copy the data from the storage account to an enterprise data warehouse in Azure Synapse Analytics. You need to prepare the files to ensure that the data copies quickly.
Solution: You copy the files to a table that has a columnstore index.
Does this meet the goal?

A.    Yes
B.    No

Answer: B
Instead convert the files to compressed delimited text files.

You are designing an Azure Stream Analytics solution that will analyze Twitter data. You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.
Solution: You use a hopping window that uses a hop size of 10 seconds and a window size of 10 seconds.
Does this meet the goal?

A.    Yes
B.    No

Answer: B
Instead use a tumbling window. Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.

A company has a real-time data analysis solution that is hosted on Microsoft Azure. The solution uses Azure Event Hub to ingest data and an Azure Stream Analytics cloud job to analyze the data. The cloud job is configured to use 120 Streaming Units (SU). You need to optimize performance for the Azure Stream Analytics job. Which two actions should you perform? (Each correct answer presents part of the solution. Choose two.)

A.    Implement event ordering.
B.    Implement Azure Stream Analytics user-defined functions (UDF).
C.    Implement query parallelization by partitioning the data output.
D.    Scale the SU count for the job up.
E.    Scale the SU count for the job down.
F.    Implement query parallelization by partitioning the data input.

Answer: DF
D: Scale out the query by allowing the system to process each input partition separately.
F: A Stream Analytics job definition includes inputs, a query, and output. Inputs are where the job reads the data stream from.

You need to trigger an Azure Data Factory pipeline when a file arrives in an Azure Data Lake Storage Gen2 container. Which resource provider should you enable?

A.    Microsoft.Sql
B.    Microsoft.Automation
C.    Microsoft.EventGrid
D.    Microsoft.EventHub

Answer: C
Event-driven architecture (EDA) is a common data integration pattern that involves production, detection, consumption, and reaction to events. Data integration scenarios often require Data Factory customers to trigger pipelines based on events happening in storage account, such as the arrival or deletion of a file in Azure Blob Storage account. Data Factory natively integrates with Azure Event Grid, which lets you trigger pipelines on such events.

You have an Azure Data Factory that contains 10 pipelines. You need to label each pipeline with its main purpose of either ingest, transform, or load. The labels must be available for grouping and filtering when using the monitoring experience in Data Factory. What should you add to each pipeline?

A.    a resource tag
B.    a correlation ID
C.    a run group ID
D.    an annotation

Answer: D
Annotations are additional, informative tags that you can add to specific factory resources: pipelines, datasets, linked services, and triggers. By adding annotations, you can easily filter and search for specific factory resources.

You are designing a statistical analysis solution that will use custom proprietary Python functions on near real-time data from Azure Event Hubs. You need to recommend which Azure service to use to perform the statistical analysis. The solution must minimize latency. What should you recommend?

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

Answer: C

You have an Azure Data Factory version 2 (V2) resource named Df1. Df1 contains a linked service. You have an Azure Key vault named vault1 that contains an encryption key named key1. You need to encrypt Df1 by using key1. What should you do first?

A.    Add a private endpoint connection to vaul1.
B.    Enable Azure role-based access control on vault1.
C.    Remove the linked service from Df1.
D.    Create a self-hosted integration runtime.

Answer: C
Linked services are much like connection strings, which define the connection information needed for Data Factory to connect to external resources.
Not D: A self-hosted integration runtime copies data between an on-premises store and cloud storage.

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.    Advanced Data Security for this database.
B.    Transparent Data Encryption (TDE).
C.    Secure transfer required.
D.    Dynamic Data Masking.

Answer: B
Azure SQL Database currently supports encryption at rest for Microsoft-managed service side and client-side encryption scenarios. Support for server encryption is currently provided through the SQL feature called Transparent Data Encryption. Client-side encryption of Azure SQL Database data is supported through the Always Encrypted feature.

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 Dedicated
B.    Azure Stream Analytics
C.    Azure Data Factory
D.    Azure Synapse Analytics

Answer: A
You can’t change the partition count for an event hub after its creation except for the event hub in a dedicated cluster.

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

Answer: B
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.
Not A: A hash distributed table is designed to achieve high performance for queries on large tables.
Not C: A round-robin table distributes table rows evenly across all distributions. The rows are distributed randomly. Loading data into a round-robin table is fast. Keep in mind that queries can require more data movement than the other distribution methods.

You have an Azure data solution that contains an enterprise data warehouse in Azure Synapse Analytics named DW1. Several users execute ad hoc queries to DW1 concurrently. You regularly perform automated data loads to DW1. You need to ensure that the automated data loads have enough memory available to complete quickly and successfully when the adhoc queries run. What should you do?

A.    Hash distribute the large fact tables in DW1 before performing the automated data loads.
B.    Assign a smaller resource class to the automated data load queries.
C.    Assign a larger resource class to the automated data load queries.
D.    Create sampled statistics for every column in each table of DW1.

Answer: C
The performance capacity of a query is determined by the user’s resource class. Resource classes are pre-determined resource limits in Synapse SQL pool that govern compute resources and concurrency for query execution. Resource classes can help you configure resources for your queries by setting limits on the number of queries that run concurrently and on the compute-resources assigned to each query. There’s a trade-off between memory and concurrency.
– Smaller resource classes reduce the maximum memory per query, but increase concurrency.
– Larger resource classes increase the maximum memory per query, but reduce concurrency.

You have an Azure Synapse Analytics dedicated SQL pool named Pool1 and a database named DB1. DB1 contains a fact table named Table1. You need to identify the extent of the data skew in Table1. What should you do in Synapse Studio?

A.    Connect to the built-in pool and run DBCC PDW_SHOWSPACEUSED.
B.    Connect to the built-in pool and run DBCC CHECKALLOC.
C.    Connect to Pool1 and query sys.dm_pdw_node_status.
D.    Connect to Pool1 and query sys.dm_pdw_nodes_db_partition_stats.

Answer: A

You are monitoring an Azure Stream Analytics job. You discover that the Backlogged Input Events metric is increasing slowly and is consistently non-zero. You need to ensure that the job can handle all the events. What should you do?

A.    Change the compatibility level of the Stream Analytics job.
B.    Increase the number of streaming units (SUs).
C.    Remove any named consumer groups from the connection and use $default.
D.    Create an additional output stream for the existing input stream.

Answer: B
Backlogged Input Events: Number of input events that are backlogged. A non-zero value for this metric implies that your job isn’t able to keep up with the number of incoming events. If this value is slowly increasing or consistently non-zero, you should scale out your job. You should increase the Streaming Units.

You are designing a star schema for a dataset that contains records of online orders. Each record includes an order date, an order due date, and an order ship date. You need to ensure that the design provides the fastest query times of the records when querying for arbitrary date ranges and aggregating by fiscal calendar attributes. Which two actions should you perform? (Each correct answer presents part of the solution. Choose two.)

A.    Create a date dimension table that has a DateTime key.
B.    Use built-in SQL functions to extract date attributes.
C.    Create a date dimension table that has an integer key in the format of YYYYMMDD.
D.    In the fact table, use integer columns for the date fields.
E.    Use DateTime columns for the date fields.

Answer: BD

You have an Azure subscription that contains an Azure Data Lake Storage account. The storage account contains a data lake named DataLake1. You plan to use an Azure data factory to ingest data from a folder in DataLake1, transform the data, and land the data in another folder. You need to ensure that the data factory can read and write data from any folder in the DataLake1 file system. The solution must meet the following requirements:
– Minimize the risk of unauthorized user access.
– Use the principle of least privilege.
– Minimize maintenance effort.
How should you configure access to the storage account for the data factory? (To answer, select the appropriate options in the answer area.)

Box 1: Azure Active Directory (Azure AD). On Azure, managed identities eliminate the need for developers having to manage credentials by providing an identity for the Azure resource in Azure AD and using it to obtain Azure Active Directory (Azure AD) tokens.
Box 2: a managed identity. A data factory can be associated with a managed identity for Azure resources, which represents this specific data factory. You can directly use this managed identity for Data Lake Storage Gen2 authentication, similar to using your own service principal. It allows this designated factory to access and copy data to or from your Data Lake Storage Gen2.
Note: The Azure Data Lake Storage Gen2 connector supports the following authentication types:
– Account key authentication.
– Service principal authentication.
– Managed identities for Azure resources authentication.

The following code segment is used to create an Azure Databricks cluster:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Box 1: Yes. A cluster mode of `High Concurrency’ is selected, unlike all the others which are `Standard’. This results in a worker type of Standard_DS13_v2.
Box 2: No. When you run a job on a new cluster, the job is treated as a data engineering (job) workload subject to the job workload pricing. When you run a job on an existing cluster, the job is treated as a data analytics (all-purpose) workload subject to all-purpose workload pricing.
Box 3: Yes. Delta Lake on Databricks allows you to configure Delta Lake based on your workload patterns.

Drag and Drop
You have an Azure Synapse Analytics workspace named WS1. You have an Azure Data Lake Storage Gen2 container that contains JSON-formatted files in the following format:
You need to use the serverless SQL pool in WS1 to read the files. 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.)

Box 1: openrowset. The easiest way to see to the content of your CSV file is to provide file URL to OPENROWSET function, specify csv FORMAT.
Box 2: openjson. You can access your JSON files from the Azure File Storage share by using the mapped drive.

Drag and Drop
You have an Azure Synapse Analytics SQL pool named Pool1 on a logical Microsoft SQL server named Server1. You need to implement Transparent Data Encryption (TDE) on Pool1 by using a custom key named key1. Which five actions should you perform in sequence? (To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.)

Step 1: Assign a managed identity to Server1. You will need an existing Managed Instance as a prerequisite.
Step 2: Create an Azure key vault and grant the managed identity permissions to the vault Create Resource and setup Azure Key Vault.
Step 3: Add key1 to the Azure key vault. The recommended way is to import an existing key from a .pfx file or get an existing key from the vault. Alternatively, generate a new key directly in Azure Key Vault.
Step 4: Configure key1 as the TDE protector for Server1. Provide TDE Protector key.
Step 5: Enable TDE on Pool1.


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