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NEW QUESTION: 1
Which command will update the spatial extent associated with each raster object (column
GEOR) in table IMAGES?
A. UPDATE images aset a.geor.spatialExtent = sdo_geor.generateSpatialExtent(geor)
B. UPDATE imagesset geor.spatialExtent = sdo_geor.generateSpatialExtent(geor)
C. sdo_geor_admin.registerGeoRasterObjects('IMAGES', 'GEOR')
D. sdo_geor.generateSpatialExtent('IMAGES', 'GEOR')
E. UPDATE images aset a.geor.spatialExtent =
sdo_geor_utl.calcOptimizedBlockSize(geor)
Answer: A
Explanation:
The following example updates the spatial extent geometry of a specified GeoRaster object in the CITY_IMAGES table to the generated spatial extent (reflecting the model coordinate system) of that object:
UPDATE city_images c
SET c.image.spatialExtent = sdo_geor.generateSpatialExtent(image)
WHERE c.image_id = 100;
COMMIT;
References:http://docs.oracle.com/cd/B28359_01/appdev.111/b28398/geor_operations.ht m#CIHGGAIG

NEW QUESTION: 2
You need to implement the e-commerce checkout API.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Create an Azure Function using the HTTP POST function template.
B. In the Azure Function App, enable Cross-Origin Resource Sharing (CORS) with all origins permitted.
C. Create an Azure Function using the Generic webhook function template.
D. Set the function template's Mode property to Webhook and the Webhook type property to Generic JSON.
E. In the Azure Function App, enable Managed Service Identity (MSI).
F. Set the function template's Mode property to Webhook and the Webhook type property to GitHub.
Answer: A,D,E
Explanation:
Explanation/Reference:
Explanation:
Scenario: E-commerce application sign-ins must be secured by using Azure App Service authentication and Azure Active Directory (AAD).
D: A managed identity from Azure Active Directory allows your app to easily access other AAD-protected resources such as Azure Key Vault.
Incorrect Answers:
C: CORS is an HTTP feature that enables a web application running under one domain to access resources in another domain.
References:
https://docs.microsoft.com/en-us/azure/app-service/overview-managed-identity

NEW QUESTION: 3
CORRECT TEXT
Problem Scenario 76 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.orders
table=retail_db.order_items
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Columns of order table : (orderid , order_date , ordercustomerid, order_status}
.....
Please accomplish following activities.
1 . Copy "retail_db.orders" table to hdfs in a directory p91_orders.
2 . Once data is copied to hdfs, using pyspark calculate the number of order for each status.
3 . Use all the following methods to calculate the number of order for each status. (You need to know all these functions and its behavior for real exam)
- countByKey()
-groupByKey()
- reduceByKey()
-aggregateByKey()
- combineByKey()
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Import Single table
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail dba - password=cloudera -table=orders --target-dir=p91_orders
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the
MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Read the data from one of the partition, created using above command, hadoop fs
-cat p91_orders/part-m-00000
Step 3: countByKey #Number of orders by status allOrders = sc.textFile("p91_orders")
#Generate key and value pairs (key is order status and vale as an empty string keyValue = aIIOrders.map(lambda line: (line.split(",")[3], ""))
#Using countByKey, aggregate data based on status as a key
output=keyValue.countByKey()Jtems()
for line in output: print(line)
Step 4 : groupByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split)",")[3], 1))
#Using countByKey, aggregate data based on status as a key output=
keyValue.groupByKey().map(lambda kv: (kv[0], sum(kv[1]}}}
tor line in output.collect(): print(line}
Step 5 : reduceByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(","}[3], 1))
#Using countByKey, aggregate data based on status as a key output=
keyValue.reduceByKey(lambda a, b: a + b)
tor line in output.collect(): print(line}
Step 6: aggregateByKey
#Generate key and value pairs (key is order status and vale as an one keyValue = allOrders.map(lambda line: (line.split(",")[3], line}} output=keyValue.aggregateByKey(0, lambda a, b: a+1, lambda a, b: a+b} for line in output.collect(): print(line}
Step 7 : combineByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(",")[3], line))
output=keyValue.combineByKey(lambda value: 1, lambda ace, value: acc+1, lambda ace, value: acc+value) tor line in output.collect(): print(line)
#Watch Spark Professional Training provided by www.ABCTECH.com to understand more on each above functions. (These are very important functions for real exam)

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