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Some minds work best on paper. Print the Cloudera CCA Spark and Hadoop Developer PDF from SurePassExams, annotate every page, and let 96 practice questions become a notebook you'll actually review.
Cloudera CCA175 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Transform, Stage, and Store Data | - Load data from HDFS for use in Spark applications - Perform standard ETL processes using the Spark API - Write results back into HDFS using Spark |
| Topic 2: Data Analysis with Spark | - Use Spark SQL to interact with the metastore - Generate reports by querying loaded data - Filter, aggregate, join, rank, and sort datasets |
| Topic 3: Configuration and Environment | - Familiarity with Spark runtime settings and memory options - Supply command-line options to change application configuration |
CCA175 FAQ: Read Before You Register
The Cloudera CCA Spark and Hadoop Developer blueprint divides into 3 domains — including Transform, Stage, and Store Data, Data Analysis with Spark, Configuration and Environment. Those weights tell you where the points live; plan revision time in the same proportions. The full subtopic list appears above.
No formal prerequisites; recommended programming and Hadoop/Spark experience Eligibility rules change occasionally, so confirm them on the official page (official CCA175 exam page) before booking.
Yes — the free Cloudera CCA Spark and Hadoop Developer demo at SurePassExams shows real questions and answers before you pay. Every purchase includes 365 days of free updates, renewable later at 50% off.
The official format: 120 minutes, 8–12 performance-based tasks questions. That ratio is the real challenge, so practice pacing — time-box each question, mark the stubborn ones, and run full simulations in the SurePassExams PC test engine until the clock stops being scary.
The Cloudera CCA Spark and Hadoop Developer is Cloudera's official exam for the Cloudera Certified credential (Associate level). In the job market it works as portable proof of skill — more interviews, better offers. It pairs naturally with Cloudera CCA Data Analyst, Cloudera CCA Administrator.
Two promises. Delivery: payment triggers an automatic email within a minute — instant download, unlimited devices, 24/7 help if nothing arrives within 2 hours. Refund: take the corresponding CCA175 exam within 60 days of purchase, and if you fail, send a scanned enrollment slip plus the official Score Report PDF within 2 days of the exam for a full refund, processed within 7 days. Excluded: exams within 3 days of purchase, candidate names that don't match the payer, and free or expired products. Prefer a swap? Exchange for two equal-value products free.
USD $295 (approx.) per attempt; 70% to pass. Since a retake bills the full fee again, rehearse with the 96 practice questions from SurePassExams until your mock scores clear the bar with room to spare.
Cloudera CCA Spark and Hadoop Developer Sample Questions:
CORRECT TEXT
Problem Scenario 9 : You have been given following mysql database details as well as other info.
user=retail_dba
password=cloudera
database=retail_db
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following.
1. Import departments table in a directory.
2. Again import departments table same directory (However, directory already exist hence it should not overrride and append the results)
3. Also make sure your results fields are terminated by '|' and lines terminated by '\n\
Correct Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solutions :
Step 1 : Clean the hdfs file system, if they exists clean out.
hadoop fs -rm -R departments
hadoop fs -rm -R categories
hadoop fs -rm -R products
hadoop fs -rm -R orders
hadoop fs -rm -R order_items
hadoop fs -rm -R customers
Step 2 : Now import the department table as per requirement.
sqoop import \
-connect jdbc:mysql://quickstart:330G/retaiI_db \
--username=retail_dba \
-password=cloudera \
-table departments \
-target-dir=departments \
-fields-terminated-by '|' \
-lines-terminated-by '\n' \
-ml
Step 3 : Check imported data.
hdfs dfs -Is departments
hdfs dfs -cat departments/part-m-00000
Step 4 : Now again import data and needs to appended.
sqoop import \
-connect jdbc:mysql://quickstart:3306/retail_db \
--username=retail_dba \
-password=cloudera \
-table departments \
-target-dir departments \
-append \
-tields-terminated-by '|' \
-lines-termtnated-by '\n' \
-ml
Step 5 : Again Check the results
hdfs dfs -Is departments
hdfs dfs -cat departments/part-m-00001
CORRECT TEXT
Problem Scenario 3: You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.categories
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following activities.
1. Import data from categories table, where category=22 (Data should be stored in categories subset)
2. Import data from categories table, where category>22 (Data should be stored in categories_subset_2)
3. Import data from categories table, where category between 1 and 22 (Data should be stored in categories_subset_3)
4. While importing catagories data change the delimiter to '|' (Data should be stored in categories_subset_S)
5. Importing data from catagories table and restrict the import to category_name,category id columns only with delimiter as '|'
6 . Add null values in the table using below SQL statement ALTER TABLE categories modify category_department_id int(11); INSERT INTO categories values
(eO.NULL.'TESTING');
7. Importing data from catagories table (In categories_subset_17 directory) using '|' delimiter and categoryjd between 1 and 61 and encode null values for both string and non string columns.
8. Import entire schema retail_db in a directory categories_subset_all_tables
Correct Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution:
Step 1: Import Single table (Subset data} Note: Here the ' is the same you find on - key sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail_dba - password=cloudera -table=categories ~warehouse-dir= categories_subset --where
\'category_id\'=22 --m 1
Step 2 : Check the output partition
hdfs dfs -cat categoriessubset/categories/part-m-00000
Step 3 : Change the selection criteria (Subset data)
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail_dba - password=cloudera -table=categories ~warehouse-dir= categories_subset_2 --where
\'category_id\'\>22 -m 1
Step 4 : Check the output partition
hdfs dfs -cat categories_subset_2/categories/part-m-00000
Step 5 : Use between clause (Subset data)
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail_dba - password=cloudera -table=categories ~warehouse-dir=categories_subset_3 --where
"\'category_id\' between 1 and 22" --m 1
Step 6 : Check the output partition
hdfs dfs -cat categories_subset_3/categories/part-m-00000
Step 7 : Changing the delimiter during import.
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail dba - password=cloudera -table=categories -warehouse-dir=:categories_subset_6 --where
"/'categoryjd /' between 1 and 22" -fields-terminated-by='|' -m 1
Step 8 : Check the.output partition
hdfs dfs -cat categories_subset_6/categories/part-m-00000
Step 9 : Selecting subset columns
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail_dba - password=cloudera -table=categories --warehouse-dir=categories subset col -where
"/'category id/' between 1 and 22" -fields-terminated-by=T -columns=category name,category id --m 1
Step 10 : Check the output partition
hdfs dfs -cat categories_subset_col/categories/part-m-00000
Step 11 : Inserting record with null values (Using mysql} ALTER TABLE categories modify category_department_id int(11); INSERT INTO categories values ^NULL/TESTING'); select" from categories;
Step 12 : Encode non string null column
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail dba - password=cloudera -table=categories --warehouse-dir=categortes_subset_17 -where
"\"category_id\" between 1 and 61" -fields-terminated-by=,|' --null-string-N' -null-non- string=,N' --m 1
Step 13 : View the content
hdfs dfs -cat categories_subset_17/categories/part-m-00000
Step 14 : Import all the tables from a schema (This step will take little time) sqoop import-all-tables -connect jdbc:mysql://quickstart:3306/retail_db -- username=retail_dba -password=cloudera -warehouse-dir=categories_si
Step 15 : View the contents
hdfs dfs -Is categories_subset_all_tables
Step 16 : Cleanup or back to originals.
delete from categories where categoryid in (59,60);
ALTER TABLE categories modify category_department_id int(11) NOTNULL;
ALTER TABLE categories modify category_name varchar(45) NOT NULL;
desc categories;
CORRECT TEXT
Problem Scenario 92 : You have been given a spark scala application, which is bundled in jar named hadoopexam.jar.
Your application class name is com.hadoopexam.MyTask
You want that while submitting your application should launch a driver on one of the cluster node.
Please complete the following command to submit the application.
spark-submit XXX -master yarn \
YYY SSPARK HOME/lib/hadoopexam.jar 10
Correct Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution
XXX: -class com.hadoopexam.MyTask
YYY : --deploy-mode cluster
CORRECT TEXT
Problem Scenario 47 : You have been given below code snippet, with intermediate output.
val z = sc.parallelize(List(1,2,3,4,5,6), 2)
// lets first print out the contents of the RDD with partition labels
def myfunc(index: Int, iter: lterator[(lnt)]): lterator[String] = {
iter.toList.map(x => "[partID:" + index + ", val: " + x + "]").iterator
}
//In each run , output could be different, while solving problem assume belowm output only.
z.mapPartitionsWithlndex(myfunc).collect
res28: Array[String] = Array([partlD:0, val: 1], [partlD:0, val: 2], [partlD:0, val: 3], [partlD:1, val: 4], [partlD:1, val: S], [partlD:1, val: 6])
Now apply aggregate method on RDD z , with two reduce function , first will select max value in each partition and second will add all the maximum values from all partitions.
Initialize the aggregate with value 5. hence expected output will be 16.
Correct Answer:
z.aggregate(5)(math.max(_, J, _ + _)
CORRECT TEXT
Problem Scenario 94 : You have to run your Spark application on yarn with each executor
20GB and number of executors should be 50. Please replace XXX, YYY, ZZZ export HADOOP_CONF_DIR=XXX
./bin/spark-submit \
-class com.hadoopexam.MyTask \
xxx\
-deploy-mode cluster \ # can be client for client mode
YYY\
2 22 \
/path/to/hadoopexam.jar \
1 000
Correct Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution
XXX: -master yarn
YYY : -executor-memory 20G
ZZZ: -num-executors 50
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