Databricks Associate-Developer-Apache-Spark-3.5 Q&A - in .pdf

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  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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  • Q & A: 365 Questions and Answers
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NEW QUESTION: 1
CORRECT TEXT
Problem Scenario 81 : You have been given MySQL DB with following details. You have been given following product.csv file product.csv productID,productCode,name,quantity,price
1001,PEN,Pen Red,5000,1.23
1002,PEN,Pen Blue,8000,1.25
1003,PEN,Pen Black,2000,1.25
1004,PEC,Pencil 2B,10000,0.48
1005,PEC,Pencil 2H,8000,0.49
1006,PEC,Pencil HB,0,9999.99
Now accomplish following activities.
1 . Create a Hive ORC table using SparkSql
2 . Load this data in Hive table.
3 . Create a Hive parquet table using SparkSQL and load data in it.
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create this tile in HDFS under following directory (Without header}
/user/cloudera/he/exam/task1/productcsv
Step 2 : Now using Spark-shell read the file as RDD
// load the data into a new RDD
val products = sc.textFile("/user/cloudera/he/exam/task1/product.csv")
// Return the first element in this RDD
prod u cts.fi rst()
Step 3 : Now define the schema using a case class
case class Product(productid: Integer, code: String, name: String, quantity:lnteger, price:
Float)
Step 4 : create an RDD of Product objects
val prdRDD = products.map(_.split(",")).map(p =>
Product(p(0).tolnt,p(1),p(2),p(3}.tolnt,p(4}.toFloat))
prdRDD.first()
prdRDD.count()
Step 5 : Now create data frame val prdDF = prdRDD.toDF()
Step 6 : Now store data in hive warehouse directory. (However, table will not be created } import org.apache.spark.sql.SaveMode prdDF.write.mode(SaveMode.Overwrite).format("orc").saveAsTable("product_orc_table") step 7: Now create table using data stored in warehouse directory. With the help of hive.
hive
show tables
CREATE EXTERNAL TABLE products (productid int,code string,name string .quantity int, price float}
STORED AS ore
LOCATION 7user/hive/warehouse/product_orc_table';
Step 8 : Now create a parquet table
import org.apache.spark.sql.SaveMode
prdDF.write.mode(SaveMode.Overwrite).format("parquet").saveAsTable("product_parquet_ table")
Step 9 : Now create table using this
CREATE EXTERNAL TABLE products_parquet (productid int,code string,name string
.quantity int, price float}
STORED AS parquet
LOCATION 7user/hive/warehouse/product_parquet_table';
Step 10 : Check data has been loaded or not.
Select * from products;
Select * from products_parquet;

NEW QUESTION: 2
You want to add a new GUID column named ProductGUID to a table named dbo.Product that already contains data.
ProductGUID will have a constraint to ensure that it always has a value when new rows are inserted into dbo.Product.
You need to ensure that the new column is assigned a GUID for existing rows.
Which four Transact-SQL statements should you use to develop the solution? (To answer, move the appropriate Transact-SQL segments from the list of SQL-segments to the answer area and arrange them in the correct order.)

Answer:
Explanation:

Explanation:
Box 1:
Existing BookGuid can be null.
Box 2:
Box 3: new()
Generate a new global value
Box 4:
Actually, in the real world, you don't have to use WITH VALUES at the end of the statement and it works just as well. But because the question specifically states which FOUR TSQL statements to use, we have to include it.

NEW QUESTION: 3
Your network contains a System Center 2012 Configuration Manager environment.
You have the following query:
Select
R .Name,
U .UserName
From
SMS_R_System R
Join SMS_R_User U
On R.LastLogonUserName = U.UserName
You need to ensure that all of the client computers are listed in the query results.
Which join type should you use?
A. FULL
B. INNER
C. RIGHT
D. LEFT
Answer: D
Explanation:
Explanation/Reference:
Explanation:
The LEFTJOIN keyword returns all rows from the left table (table_name1), even if there are no matches in the right table (table_name2).

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