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NEW QUESTION # 95
You have an Azure subscription linked to an Azure Active Directory (Azure AD) tenant that contains a service principal named ServicePrincipal1. The subscription contains an Azure Data Lake Storage account named adls1. Adls1 contains a folder named Folder2 that has a URI of https://adls1.dfs.core.windows.net
/container1/Folder1/Folder2/.
ServicePrincipal1 has the access control list (ACL) permissions shown in the following table.
You need to ensure that ServicePrincipal1 can perform the following actions:
* Traverse child items that are created in Folder2.
* Read files that are created in Folder2.
The solution must use the principle of least privilege.
Which two permissions should you grant to ServicePrincipal1 for Folder2? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Access - Execute
- B. Default-Read
- C. Default - Write
- D. Default - Execute
- E. Access - Write
- F. Access - Read
Answer: B,D
Explanation:
Execute (X) permission is required to traverse the child items of a folder.
There are two kinds of access control lists (ACLs), Access ACLs and Default ACLs.
Access ACLs: These control access to an object. Files and folders both have Access ACLs.
Default ACLs: A "template" of ACLs associated with a folder that determine the Access ACLs for any child items that are created under that folder. Files do not have Default ACLs.
Reference:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-access-control
NEW QUESTION # 96
You have an Azure Synapse Analytics dedicated SQL pool.
You need to Create a fact table named Table1 that will store sales data from the last three years. The solution must be optimized for the following query operations:
Show order counts by week.
* Calculate sales totals by region.
* Calculate sales totals by product.
* Find all the orders from a given month.
Which data should you use to partition Table1?
- A. month
- B. week
- C. product
- D. region
Answer: A
Explanation:
Table partitions enable you to divide your data into smaller groups of data. In most cases, table partitions are created on a date column.
Benefits to queries
Partitioning can also be used to improve query performance. A query that applies a filter to partitioned data can limit the scan to only the qualifying partitions. This method of filtering can avoid a full table scan and only scan a smaller subset of data. With the introduction of clustered columnstore indexes, the predicate elimination performance benefits are less beneficial, but in some cases there can be a benefit to queries.
For example, if the sales fact table is partitioned into 36 months using the sales date field, then queries that filter on the sale date can skip searching in partitions that don't match the filter.
Note: Benefits to loads
The primary benefit of partitioning in dedicated SQL pool is to improve the efficiency and performance of loading data by use of partition deletion, switching and merging. In most cases data is partitioned on a date column that is closely tied to the order in which the data is loaded into the SQL pool. One of the greatest benefits of using partitions to maintain data is the avoidance of transaction logging. While simply inserting, updating, or deleting data can be the most straightforward approach, with a little thought and effort, using partitioning during your load process can substantially improve performance.
NEW QUESTION # 97
You need to output files from Azure Data Factory.
Which file format should you use for each type of output? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://www.datanami.com/2018/05/16/big-data-file-formats-demystified
NEW QUESTION # 98
A company uses Azure Stream Analytics to monitor devices.
The company plans to double the number of devices that are monitored.
You need to monitor a Stream Analytics job to ensure that there are enough processing resources to handle the additional load.
Which metric should you monitor?
- A. Late Input Events
- B. Watermark delay
- C. Early Input Events
- D. Input Deserialization Errors
Answer: B
Explanation:
Explanation
There are a number of resource constraints that can cause the streaming pipeline to slow down. The watermark delay metric can rise due to:
* Not enough processing resources in Stream Analytics to handle the volume of input events.
* Not enough throughput within the input event brokers, so they are throttled.
* Output sinks are not provisioned with enough capacity, so they are throttled. The possible solutions vary widely based on the flavor of output service being used.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-time-handling
NEW QUESTION # 99
You plan to develop a dataset named Purchases by using Azure databricks Purchases will contain the following columns:
* ProductID
* ItemPrice
* lineTotal
* Quantity
* StorelD
* 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 rode? To answer, select the appropriate options In the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://intellipaat.com/community/11744/how-to-partition-and-write-dataframe-in-spark-without-deleting-partitions-with-no-new-data
NEW QUESTION # 100
You have an Azure subscription that contains an Azure Synapse Analytics workspace named Workspaces a Log Analytics workspace named Workspace2, and an Azure Data Lake Storage Gen2 container named Container1.
Workspace1 contains an Apache Spark job named Job1 that writes data to Container1. Workspace1 sends diagnostics to Workspace2.
From Synapse Studio, you submit Job1.
What should you use to review the LogQuery output of the job?
- A. the Apache Spark applications option on the Monitor tab
- B. the Spark monitoring URL returned after Job1 is submitted
- C. the files in the result subfolder of Container1
- D. a table in Workspace2
Answer: A
NEW QUESTION # 101
You have an Azure subscription.
You need to deploy an Azure Data Lake Storage Gen2 Premium account. The solution must meet the following requirements:
* Blobs that are older than 365 days must be deleted.
* Administrator efforts must be minimized.
* Costs must be minimized
What should you use? To answer, select the appropriate options in the answer are a. NOTE Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 102
You need to implement an Azure Databricks cluster that automatically connects to Azure Data lake Storage Gen2 by using Azure Active Directory (Azure AD) integration. How should you configure the new clutter? To answer, select the appropriate options in the answers are a. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 103
You have an Azure Data Lake Storage Gen2 container.
Data is ingested into the container, and then transformed by a data integration application. The data is NOT modified after that. Users can read files in the container but cannot modify the files.
You need to design a data archiving solution that meets the following requirements:
New data is accessed frequently and must be available as quickly as possible.
Data that is older than five years is accessed infrequently but must be available within one second when requested.
Data that is older than seven years is NOT accessed. After seven years, the data must be persisted at the lowest cost possible.
Costs must be minimized while maintaining the required availability.
How should you manage the data? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blob-storage-tiers
https://azure.microsoft.com/en-us/updates/reduce-data-movement-and-make-your-queries-more-efficient-with-the-general-availability-of-replicated-tables/
https://azure.microsoft.com/en-us/blog/replicated-tables-now-generally-available-in-azure-sql-data-warehouse/
NEW QUESTION # 104
You have an Azure Data Factory pipeline that contains a data flow. The data flow contains the following expression.
Answer:
Explanation:
See the answer in explanation.
Explanation
See below answer.
NEW QUESTION # 105
You have an Azure Synapse Analytics dedicated SQL pool named Pool1 that contains a table named Sales.
Sales has row-level security (RLS) applied. RLS uses the following predicate filter.
A user named SalesUser1 is assigned the db_datareader role for Pool1. Which rows in the Sales table are returned when SalesUser1 queries the table?
- A. all the rows
- B. only the rows for which the value in the SalesRep column is SalesUser1
- C. only the rows for which the value in the User_Name column is SalesUser1
- D. only the rows for which the value in the SalesRep column is Manager
Answer: C
NEW QUESTION # 106
You have an Azure subscription that contains the following resources:
* An Azure Active Directory (Azure AD) tenant that contains a security group named Group1.
* An Azure Synapse Analytics SQL pool named Pool1.
You need to control the access of Group1 to specific columns and rows in a table in Pool1 Which Transact-SQL commands should you use? To answer, select the appropriate options in the answer area.
NOTE: Each appropriate options in the answer area.
Answer:
Explanation:
NEW QUESTION # 107
You need to build a solution to ensure that users can query specific files in an Azure Data Lake Storage Gen2 account from an Azure Synapse Analytics serverless SQL pool.
Which three 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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation
Graphical user interface, text, application, email Description automatically generated
Step 1: Create an external data source
You can create external tables in Synapse SQL pools via the following steps:
* CREATE EXTERNAL DATA SOURCE to reference an external Azure storage and specify the credential that should be used to access the storage.
* CREATE EXTERNAL FILE FORMAT to describe format of CSV or Parquet files.
* CREATE EXTERNAL TABLE on top of the files placed on the data source with the same file format.
Step 2: Create an external file format object
Creating an external file format is a prerequisite for creating an external table.
Step 3: Create an external table
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables
NEW QUESTION # 108
You build an Azure Data Factory pipeline to move data from an Azure Data Lake Storage Gen2 container to a database in an Azure Synapse Analytics dedicated SQL pool.
Data in the container is stored in the following folder structure.
/in/{YYYY}/{MM}/{DD}/{HH}/{mm}
The earliest folder is /in/2021/01/01/00/00. The latest folder is /in/2021/01/15/01/45.
You need to configure a pipeline trigger to meet the following requirements:
Existing data must be loaded.
Data must be loaded every 30 minutes.
Late-arriving data of up to two minutes must he included in the load for the time at which the data should have arrived.
How should you configure the pipeline trigger? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger
NEW QUESTION # 109
You are designing an Azure Databricks cluster that runs user-defined local processes. You need to recommend a cluster configuration that meets the following requirements:
* Minimize query latency.
* Maximize the number of users that can run queues on the cluster at the same time for Reduce overall costs without compromising other requirements Which cluster type should you recommend?
- A. Standard with Autoscaling
- B. High Concurrency with Autoscaling
- C. Standard with Auto termination
- D. High Concurrency with Auto Termination
Answer: B
Explanation:
A High Concurrency cluster is a managed cloud resource. The key benefits of High Concurrency clusters are that they provide fine-grained sharing for maximum resource utilization and minimum query latencies.
Databricks chooses the appropriate number of workers required to run your job. This is referred to as autoscaling. Autoscaling makes it easier to achieve high cluster utilization, because you don't need to provision the cluster to match a workload.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/clusters/configure
NEW QUESTION # 110
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