Meet the lion on the road to Cloudera CCA Spark and Hadoop Developer: a hard exam guarding a valuable Cloudera credential. TestPassed hands CCA175 candidates 96 verified practice questions, the practical way past the beast.
Cloudera CCA175 Exam Overview:
| Certification Vendor: | Cloudera |
|---|---|
| Exam Name: | CCA Spark and Hadoop Developer Exam |
| Exam Number: | CCA175 |
| Available Languages: | English |
| Related Certifications: | CCA Data Analyst CCP Data Engineer |
| Exam Price: | USD 295 |
| Exam Format: | Performance-based, Live cluster environment, Hands-on tasks |
| Passing Score: | 70% |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 8-12 |
| Recommended Training: | Cloudera Developer Training for Spark and Hadoop |
| Exam Registration: | Cloudera Certification Portal |
| Sample Questions: |  |
| Exam Way: | Online proctored, delivered remotely via secure browser |
| Pre Condition: | No formal prerequisites; recommended experience with Scala/Python, HDFS, Hive, Sqoop, Flume, and Spark |
| Official Syllabus URL: | https://www.cloudera.com/about/training/certification/cdhhdp-certification/cca-spark.html |
Cloudera CCA175 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Transform, Stage, and Store Data | 35% | - Read/write various file formats (CSV, JSON, Avro, Parquet, etc.) - Write transformed results back to HDFS - Load data from HDFS into Spark - Perform ETL operations using Spark API |
| Data Analysis | 35% | - Create and query temporary views and tables - Use Spark SQL and interact with Hive metastore - Join multiple datasets - Filter and sort datasets - Perform aggregations and calculations |
| Data Ingestion | 20% | - Import/export data between relational databases and HDFS - Ingest data into HDFS using Sqoop - Ingest data into HDFS using Flume |
| Application Configuration and Execution | 10% | - Submit Spark applications via spark-submit - Set memory, cores, and execution parameters - Use Spark Shell (Scala/PySpark) for interactive analysis |
CCA175 Exam: The FAQ Candidates Actually Need
Cloudera CCA Spark and Hadoop Developer is an official Cloudera certification exam, listed under the code CCA175. Passing it earns you the Cloudera Certified Associate (CCA) Spark and Hadoop Developer certification, positioned at the Associate level. It also connects to CCP Data Engineer, CCA Data Analyst. For anyone aiming at a leadership track in IT, this credential is one of the clearest markers of verified skill.
Passing Cloudera CCA Spark and Hadoop Developer takes 70%, and the official registration fee is USD 295. Retakes charge the full USD 295 again, which is why accuracy in preparation matters more than volume. Validate your readiness with repeated TestPassed practice scores above the requirement before committing to a date.
The Cloudera CCA Spark and Hadoop Developer exam contains 8-12 questions to answer within 120 minutes. The SOFT engine from TestPassed imitates the real test scene on your computer and uses special methods to help you master questions and answers, so the official time limit becomes a practiced routine rather than a surprise.
No formal prerequisites; recommended experience with Scala/Python, HDFS, Hive, Sqoop, Flume, and Spark
Vendor requirements are revised periodically, so confirm the current conditions before registering via the official exam page.
Sign-up for Cloudera CCA Spark and Hadoop Developer goes through the official channels below.
One planning note: the exam is delivered Online proctored, delivered remotely via secure browser.
Cloudera recommends the following training for Cloudera CCA Spark and Hadoop Developer candidates.
Reinforce whichever training you pick with the 96 practice questions in the TestPassed CCA175 package, edited by experts who follow real test changes firsthand.
Three versions share the same verified content: the PDF prints unlimited copies for paper study, the SOFT engine imitates the real test scene on Windows PCs, and the APP version runs on all electronic products, which is why the majority of examinees choose it. A free demo lets you try before deciding, and after purchase updates are free for 365 days, extendable afterward at a 50% discount.
Worry-free shopping means a 100% money-back guarantee with stated conditions. Take the Cloudera CCA Spark and Hadoop Developer exam within 60 days of purchase; if you fail, you may claim a full refund, provided the exam matches your product. Attempts within 3 days of purchase are ineligible, as are downloaded-but-unused products, free materials, and expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. Alternatively, exchange for two other exam products of equal value, free, keeping the update service on your original purchase.
Delivery is immediate: files unlock for download at payment and are emailed to you within one minute. If nothing arrives within 2 hours, check spam and contact our 7/24 customer attendants, who reply within two hours. Installation is unlimited, and credit card payment keeps your money safe.
Cloudera CCA Spark and Hadoop Developer is structured into 4 official domains. The leading ones are Transform, Stage, and Store Data (35%), Data Ingestion (20%), and Data Analysis (35%). The full topic breakdown appears above; accurate preparation starts with an accurate map.
Cloudera CCA Spark and Hadoop Developer Sample Questions:
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Problem Scenario 36 : You have been given a file named spark8/data.csv (type,name).
data.csv
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1. Load this file from hdfs and save it back as (id, (all names of same type)) in results directory. However, make sure while saving it should be
Correct Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create file in hdfs (We will do using Hue). However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : Load data.csv file from hdfs and create PairRDDs
val name = sc.textFile("spark8/data.csv")
val namePairRDD = name.map(x=> (x.split(",")(0),x.split(",")(1)))
Step 3 : Now swap namePairRDD RDD.
val swapped = namePairRDD.map(item => item.swap)
Step 4 : Now combine the rdd by key.
val combinedOutput = namePairRDD.combineByKey(List(_), (x:List[String], y:String) => y ::
x, (x:List[String], y:List[String]) => x ::: y)
Step 5 : Save the output as a Text file and output must be written in a single file.
:ombinedOutput.repartition(1).saveAsTextFile("spark8/result.txt")
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Problem Scenario 20 : 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. Write a Sqoop Job which will import "retaildb.categories" table to hdfs, in a directory name "categories_targetJob".
Correct Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Connecting to existing MySQL Database mysql -user=retail_dba -- password=cloudera retail_db
Step 2 : Show all the available tables show tables;
Step 3 : Below is the command to create Sqoop Job (Please note that - import space is mandatory) sqoop job -create sqoopjob \ -- import \
-connect "jdbc:mysql://quickstart:3306/retail_db" \
-username=retail_dba \
-password=cloudera \
-table categories \
-target-dir categories_targetJob \
-fields-terminated-by '|' \
-lines-terminated-by '\n'
Step 4 : List all the Sqoop Jobs sqoop job --list
Step 5 : Show details of the Sqoop Job sqoop job --show sqoopjob
Step 6 : Execute the sqoopjob sqoopjob --exec sqoopjob
Step 7 : Check the output of import job
hdfs dfs -Is categories_target_job
hdfs dfs -cat categories_target_job/part*
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Problem Scenario 75 : 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
Please accomplish following activities.
1. Copy "retail_db.order_items" table to hdfs in respective directory p90_order_items .
2. Do the summation of entire revenue in this table using pyspark.
3. Find the maximum and minimum revenue as well.
4. Calculate average revenue
Columns of ordeMtems table : (order_item_id , order_item_order_id ,
order_item_product_id, order_item_quantity,order_item_subtotal,order_
item_subtotal,order_item_product_price)
Correct Answer:
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=order_items --target -dir=p90 ordeMtems --m 1
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 p90_order_items/part-m-00000
Step 3 : In pyspark, get the total revenue across all days and orders. entire TableRDD = sc.textFile("p90_order_items")
#Cast string to float
extractedRevenueColumn = entireTableRDD.map(lambda line: float(line.split(",")[4]))
Step 4 : Verify extracted data
for revenue in extractedRevenueColumn.collect():
print revenue
#use reduce'function to sum a single column vale
totalRevenue = extractedRevenueColumn.reduce(lambda a, b: a + b)
Step 5 : Calculate the maximum revenue
maximumRevenue = extractedRevenueColumn.reduce(lambda a, b: (a if a>=b else b))
Step 6 : Calculate the minimum revenue
minimumRevenue = extractedRevenueColumn.reduce(lambda a, b: (a if a<=b else b))
Step 7 : Caclculate average revenue
count=extractedRevenueColumn.count()
averageRev=totalRevenue/count
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Problem Scenario 41 : You have been given below code snippet.
val aul = sc.parallelize(List (("a" , Array(1,2)), ("b" , Array(1,2)))) val au2 = sc.parallelize(List (("a" , Array(3)), ("b" , Array(2))))
Apply the Spark method, which will generate below output.
Array[(String, Array[lnt])] = Array((a,Array(1, 2)), (b,Array(1, 2)), (a(Array(3)), (b,Array(2)))
Correct Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution:
au1.union(au2)
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Problem Scenario 93 : You have to run your Spark application with locally 8 thread or locally on 8 cores. Replace XXX with correct values.
spark-submit --class com.hadoopexam.MyTask XXX \ -deploy-mode cluster
SSPARK_HOME/lib/hadoopexam.jar 10
Correct Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution
XXX: -master local[8]
Notes : The master URL passed to Spark can be in one of the following formats:
Master URL Meaning
local Run Spark locally with one worker thread (i.e. no parallelism at all}.
local[K] Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).
local[*] Run Spark locally with as many worker threads as logical cores on your machine.
spark://HOST:PORT Connect to the given Spark standalone cluster master. The port must be whichever one your master is configured to use, which is 7077 by default.
mesos://HOST:PORT Connect to the given Mesos cluster. The port must be whichever one your is configured to use, which is 5050 by default. Or, for a Mesos cluster using
ZooKeeper, use mesos://zk://.... To submit with --deploy-mode cluster, the HOST:PORT should be configured to connect to the MesosClusterDispatcher.
yarn Connect to a YARN cluster in client or cluster mode depending on the value of - deploy-mode. The cluster location will be found based on the HADOOP CONF DIR or
YARN CONF DIR variable.
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