Cloudera CCA Spark and Hadoop Developer Exam CCA175 Dumps in PDF

Free Cloudera CCA175 Real Questions (page: 19)

Problem Scenario 51 : You have been given below code snippet.
val a = sc.parallelize(List(1, 2, 1, 3), 1)
val b = a.map((_, "b"))
val c = a.map((_, "c"))
Operation_xyz
Write a correct code snippet for Operationxyz which will produce below output.
Output:
Array[(lnt, (lterable[String], lterable[String]))] = Array(
(2, (ArrayBuffer(b), ArrayBuffer(c))),
(3, (ArrayBuffer(b), ArrayBuffer(c))),
(1, (ArrayBuffer(b, b), ArrayBuffer(c, c)))
)

  1. See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Solution :
b.cogroup(c).collect
cogroup [Pair], groupWith [Pair]
A very powerful set of functions that allow grouping up to 3 key-value RDDs together using their keys.
Another example
val x = sc.parallelize(List((1, "apple"), (2, "banana"), (3, "orange"), (4, "kiwi")), 2) val y = sc.parallelize(List((5, "computer"), (1, "laptop"), (1, "desktop"), (4, "iPad")), 2)
x.cogroup(y).collect
Array[(lnt, (lterable[String], lterable[String]))] = Array( (4, (ArrayBuffer(kiwi), ArrayBuffer(iPad))),
(2, (ArrayBuffer(banana), ArrayBuffer())),
(3, (ArrayBuffer(orange), ArrayBuffer())),
(1 , (ArrayBuffer(apple), ArrayBuffer(laptop, desktop))), (5, {ArrayBuffer(), ArrayBuffer(computer))))



Problem Scenario 24 : You have been given below comma separated employee information.
Data Set:
name, salary, sex, age
alok, 100000, male, 29
jatin, 105000, male, 32
yogesh, 134000, male, 39
ragini, 112000, female, 35
jyotsana, 129000, female, 39
valmiki, 123000, male, 29
Requirements:
Use the netcat service on port 44444, and nc above data line by line. Please do the following activities.

1. Create a flume conf file using fastest channel, which write data in hive warehouse
directory, in a table called flumemaleemployee (Create hive table as well tor given data).
2. While importing, make sure only male employee data is stored.

  1. See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Step 1: Create hive table for flumeemployee.'
CREATE TABLE flumemaleemployee
(
name string,
salary int,
sex string,
age int
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ', ';
Step 2: Create flume configuration file, with below configuration for source, sink and channel and save it in flume4.conf.
#Define source , sink, channel and agent.
agent1 .sources = source1
agent1 .sinks = sink1
agent1 .channels = channel1
# Describe/configure source1
agent1 .sources.source1.type = netcat
agent1 .sources.source1.bind = 127.0.0.1
agent1.sources.sourcel.port = 44444
#Define interceptors
agent1.sources.source1.interceptors=il
agent1 .sources.source1.interceptors.i1.type=regex_filter agent1 .sources.source1.interceptors.i1.regex=female agent1 .sources.source1.interceptors.i1.excludeEvents=true
## Describe sink1
agent1 .sinks, sinkl.channel = memory-channel
agent1.sinks.sink1.type = hdfs
agent1 .sinks, sinkl. hdfs. path = /user/hive/warehouse/flumemaleemployee hdfs-agent.sinks.hdfs-write.hdfs.writeFormat=Text
agentl .sinks.sink1.hdfs.fileType = Data Stream
# Now we need to define channel1 property.
agent1.channels.channel1.type = memory
agent1.channels.channell.capacity = 1000
agent1.channels.channel1.transactionCapacity = 100
# Bind the source and sink to the channel
agent1 .sources.source1.channels = channel1
agent1 .sinks.sink1.channel = channel1
Step 3: Run below command which will use this configuration file and append data in hdfs.

Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/flumeconf/flume4.conf --name agentl
Step 4: Open another terminal and use the netcat service, nc localhost 44444
Step 5: Enter data line by line.
alok, 100000, male, 29
jatin, 105000, male, 32
yogesh, 134000, male, 39
ragini, 112000, female, 35
jyotsana, 129000, female, 39
valmiki.123000.male.29
Step 6: Open hue and check the data is available in hive table or not.
Step 7: Stop flume service by pressing ctrl+c
Step 8: Calculate average salary on hive table using below query. You can use either hive command line tool or hue. select avg(salary) from flumeemployee;



Problem Scenario 27 : You need to implement near real time solutions for collecting information when submitted in file with below information.
Data
echo "IBM, 100, 20160104" >> /tmp/spooldir/bb/.bb.txt
echo "IBM, 103, 20160105" >> /tmp/spooldir/bb/.bb.txt
mv /tmp/spooldir/bb/.bb.txt /tmp/spooldir/bb/bb.txt
After few mins
echo "IBM, 100.2, 20160104" >> /tmp/spooldir/dr/.dr.txt
echo "IBM, 103.1, 20160105" >> /tmp/spooldir/dr/.dr.txt
mv /tmp/spooldir/dr/.dr.txt /tmp/spooldir/dr/dr.txt
Requirements:
You have been given below directory location (if not available than create it) /tmp/spooldir . You have a finacial subscription for getting stock prices from BloomBerg as well as
Reuters and using ftp you download every hour new files from their respective ftp site in directories /tmp/spooldir/bb and /tmp/spooldir/dr respectively.
As soon as file committed in this directory that needs to be available in hdfs in /tmp/flume/finance location in a single directory.
Write a flume configuration file named flume7.conf and use it to load data in hdfs with following additional properties .

1. Spool /tmp/spooldir/bb and /tmp/spooldir/dr
2. File prefix in hdfs sholuld be events
3. File suffix should be .log
4. If file is not commited and in use than it should have _ as prefix.
5. Data should be written as text to hdfs

  1. See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Solution :
Step 1: Create directory mkdir /tmp/spooldir/bb mkdir /tmp/spooldir/dr
Step 2: Create flume configuration file, with below configuration for
agent1.sources = source1 source2
agent1 .sinks = sink1
agent1.channels = channel1
agent1 .sources.source1.channels = channel1
agentl .sources.source2.channels = channell agent1 .sinks.sinkl.channel = channell agent1 .sources.source1.type = spooldir
agent1 .sources.sourcel.spoolDir = /tmp/spooldir/bb
agent1 .sources.source2.type = spooldir
agent1 .sources.source2.spoolDir = /tmp/spooldir/dr
agent1 .sinks.sink1.type = hdfs
agent1 .sinks.sink1.hdfs.path = /tmp/flume/finance
agent1-sinks.sink1.hdfs.filePrefix = events
agent1.sinks.sink1.hdfs.fileSuffix = .log
agent1 .sinks.sink1.hdfs.inUsePrefix = _
agent1 .sinks.sink1.hdfs.fileType = Data Stream
agent1.channels.channel1.type = file
Step 4: Run below command which will use this configuration file and append data in hdfs.
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/fIumeconf/fIume7.conf --name agent1
Step 5: Open another terminal and create a file in /tmp/spooldir/ echo "IBM, 100, 20160104" » /tmp/spooldir/bb/.bb.txt
echo "IBM, 103, 20160105" » /tmp/spooldir/bb/.bb.txt mv /tmp/spooldir/bb/.bb.txt /tmp/spooldir/bb/bb.txt
After few mins
echo "IBM, 100.2, 20160104" » /tmp/spooldir/dr/.dr.txt echo "IBM, 103.1, 20160105" »/tmp/spooldir/dr/.dr.txt mv /tmp/spooldir/dr/.dr.txt /tmp/spooldir/dr/dr.txt



Problem Scenario 60 : You have been given below code snippet.
val a = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"}, 3}
val b = a.keyBy(_.length)
val c = sc.parallelize(List("dog", "cat", "gnu", "salmon", "rabbit", "turkey", "woif", "bear", "bee"), 3)
val d = c.keyBy(_.length)
operation1
Write a correct code snippet for operationl which will produce desired output, shown below.
Array[(lnt, (String, String))] = Array((6, (salmon, salmon)), (6, (salmon, rabbit)), (6, (salmon, turkey)), (6, (salmon, salmon)), (6, (salmon, rabbit)),
(6, (salmon, turkey)), (3, (dog, dog)), (3, (dog, cat)), (3, (dog, gnu)), (3, (dog, bee)), (3, (rat, dog)), (3, (rat, cat)), (3, (rat, gnu)), (3, (rat, bee)))

  1. See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

solution:
b.join(d).collect
join [Pair]: Performs an inner join using two key-value RDDs. Please note that the keys must be generally comparable to make this work. keyBy : Constructs two-component tuples (key-value pairs) by applying a function on each data item. The result of the function becomes the data item becomes the key and the original value of the newly created tuples.



Problem Scenario 90 : You have been given below two files
course.txt
id, course
1, Hadoop
2, Spark
3, HBase
fee.txt
id, fee
2, 3900
3, 4200
4, 2900
Accomplish the following activities.

1. Select all the courses and their fees , whether fee is listed or not.
2. Select all the available fees and respective course. If course does not exists still list the
fee
3. Select all the courses and their fees , whether fee is listed or not. However, ignore
records having fee as null.

  1. See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Solution :

Step 1:
hdfs dfs -mkdir sparksql4
hdfs dfs -put course.txt sparksql4/
hdfs dfs -put fee.txt sparksql4/
Step 2: Now in spark shell
// load the data into a new RDD
val course = sc.textFile("sparksql4/course.txt")
val fee = sc.textFile("sparksql4/fee.txt")
// Return the first element in this RDD
course.fi rst()
fee.fi rst()
//define the schema using a case class case class Course(id: Integer, name: String) case class Fee(id: Integer, fee: Integer)
// create an RDD of Product objects
val courseRDD = course.map(_.split(", ")).map(c => Course(c(0).tolnt, c(1))) val feeRDD =fee.map(_.split(", ")).map(c => Fee(c(0}.tolnt, c(1}.tolnt)) courseRDD.first()
courseRDD.count(}
feeRDD.first()
feeRDD.countQ
// change RDD of Product objects to a DataFrame val courseDF = courseRDD.toDF(} val feeDF = feeRDD.toDF{)
// register the DataFrame as a temp table courseDF. registerTempTable("course") feeDF.
registerTempTablef'fee")
// Select data from table
val results = sqlContext.sql(......SELECT' FROM course """ ) results. showQ
val results = sqlContext.sql(......SELECT' FROM fee......) results. showQ
val results = sqlContext.sql(......SELECT * FROM course LEFT JOIN fee ON course.id = fee.id......)
results-showQ
val results ="sqlContext.sql(......SELECT * FROM course RIGHT JOIN fee ON course.id = fee.id "MM )
results. showQ
val results = sqlContext.sql(......SELECT' FROM course LEFT JOIN fee ON course.id = fee.id where fee.id IS NULL"
results. show()



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A
AI Tutor Explanation
4/21/2026 8:48:36 AM

Question 3:

  • False is the correct answer (Option B).

Why:
  • In Snowflake, a database is a metadata object that exists within a single Snowflake account. Accounts are isolated—there isn’t one database that lives in multiple accounts.
  • You can access data across accounts via data sharing or database replication, but these create separate database objects in the other accounts (e.g., a database in the consumer account created from a share), not a single shared database across accounts.

So a single database cannot exist in more than one Snowflake account.

A
Anonymous User
4/16/2026 10:54:18 AM

Question 1:

  • Correct answer: E — date = sys.argv[1]
  • Why this is correct:
- When a Databricks Job passes parameters to a notebook, those parameters are supplied to the notebook's Python process as command-line arguments. The first argument after the script name is sys.argv[1], so date = sys.argv[1] captures the passed date value directly.
  • How it compares to other options:
- date = spark.conf.get("date") reads from Spark config, not from job parameters. - input() waits for user input at runtime, which isn’t how job parameters are provided. - date = dbutils.notebooks.getParam("date") would work if the notebook were invoked via dbutils.notebook.run with parameters, not

A
Anonymous User
4/15/2026 4:42:07 AM

Question 528:

  • Correct answer: NSG flow logs for NSG1 (Option B)

  • Why:
- Traffic Analytics uses NSG flow logs to analyze traffic patterns. You must have NSG flow logs enabled for the NSGs you want to monitor. - An Azure Log Analytics workspace is also required to store and query the traffic data. - Network Watcher must be available in the subscription for traffic analytics to function.
  • What to configure (brief steps):
- Ensure Network Watcher is enabled in the East US region (for the subscription/region). - Enable NSG flow logs on NSG1. - Ensure a Log Analytics workspace exists and is accessible (read/write) so Traffic Analytics can store and query logs.
  • Why other options aren’t correct:
- “Diagnostic settings for VM1” or “Diagnostic settings for NSG1” alone don’t guarantee flow logs are captured and sent to Log Analytics, which Traffic Analytics relies on. - “Insights for VM1” is not how Traffic Analytics collects traffic data.

A
Anonymous User
4/15/2026 2:43:53 AM

Question 23:
The correct answer is Domain admin (option B), not Fabric admin.

  • Domain admin provides domain-level management: create domains/subdomains and assign workspaces within those domains, which matches the tasks while following least privilege.
  • Fabric admin is global-level access and is more privileges than needed for this scenario (it would grant broader control across the Fabric environment).

A
Anonymous User
4/14/2026 12:31:34 PM

Question 2:
For question 2, the key concept is the Longest Prefix Match. Routers pick the route whose subnet mask is the most specific (largest prefix length) that still matches the destination IP.
From the options:

  • A) 10.10.10.0/28 ? 10.10.10.0–10.10.10.15
  • B) 10.10.13.0/25 ? 10.10.13.0–10.10.13.127
  • C) 10.10.13.144/28 ? 10.10.13.144–10.10.13.159
  • D) 10.10.13.208/29 ? 10.10.13.208–10.10.13.215

The destination Host A’s IP must fall within 10.10.13.208–10.10.13.215 for the /29 to be the best match. Since /29 is the longest prefix among the matching options, Router1 will use 10.10.13.208/29.
Thus, the correct answer is D.

S
srameh
4/14/2026 10:09:29 AM

Question 3:

  • Correct answer: Phase 4, Post Accreditation

  • Explanation:
- In DITSCAP, the four phases are: - Phase 1: Definition (concept and requirements) - Phase 2: Verification (design and testing) - Phase 3: Validation (fielding and evaluation) - Phase 4: Post Accreditation (ongoing operations and lifecycle management) - The description—continuing operation of an accredited IT system and addressing changing threats throughout its life cycle—fits the Post Accreditation phase, which covers operations, maintenance, monitoring, and reauthorization as threats and environment evolve.

O
onibokun10
4/13/2026 7:50:14 PM

Question 129:
Correct answer: CNAME

  • A CNAME record creates an alias for a domain, so newapplication.comptia.org will resolve to whatever IP address www.comptia.org resolves to. This ensures both names point to the same resource without duplicating the IP.
  • Why not the others:
- SOA defines authoritative information for a zone. - MX specifies mail exchange servers. - NS designates name servers for a zone.
  • Notes: The alias name (newapplication.comptia.org) should not have other records if you use a CNAME for it, and CNAMEs aren’t used for the zone apex (root) domain. This scenario uses a subdomain, so a CNAME is appropriate.

A
Anonymous User
4/13/2026 6:29:58 PM

Question 1:

  • Correct answer: C

  • Why this is best:
- Uses OS Login with IAM, so SSH access is granted via Google accounts rather than distributing per-user SSH keys. - Granting the compute.osAdminLogin role to a Google group gives admin access to all team members in a centralized, auditable way. - Access is auditable: Cloud Audit Logs show who accessed which VM, satisfying the security requirement to determine who accessed a given instance.
  • How it works:
- Enable OS Login on the project/instances (enable-oslogin metadata). - Add the team’s

A
Anonymous User
4/13/2026 1:00:51 PM

Question 2:

  • Answer: D. Azure Advisor

  • Why: To view security-related recommendations for resources in the Compute and Apps area (including App Service Web Apps and Functions), you use Azure Advisor. Advisor surfaces personalized best-practice recommendations across resources, including security, and shows which resources are affected and the severity.

  • Why not the others:
- Azure Log Analytics is for ad-hoc querying of telemetry, not for viewing security recommendations. - Azure Event Hubs is for streaming telemetry data, not for security recommendations.
  • Quick tip: In the portal, navigate to Azure Advisor and check the Security recommendations for App Services to see actionable items and affe

D
Don
4/11/2026 5:36:42 AM

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M
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4/8/2026 6:37:56 AM

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A
Anon
4/6/2026 5:22:54 PM

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L
LRK
3/22/2026 2:38:08 PM

For Question 7 - while the answer description indicates the correct answer, the option no. mentioned is incorrect. Nice and Comprehensive. Thankyou

R
Rian
3/19/2026 9:12:10 AM

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G
Gerrard
3/18/2026 6:58:37 AM

The DP-900 exam can be tricky if you aren't familiar with Microsoft’s specific cloud terminology. I used the practice questions from free-braindumps.com and found them incredibly helpful. The site breaks down core data concepts and Azure services in a way that actually mirrors the real test. As a resutl I passed my exam.

V
Vineet Kumar
3/6/2026 5:26:16 AM

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J
Joe
1/20/2026 8:25:24 AM

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N
NJ
12/24/2025 10:39:07 AM

Helpful to test your preparedness before giving exam

A
Ashwini
12/17/2025 8:24:45 AM

Really helped

J
Jagadesh
12/16/2025 9:57:10 AM

Good explanation

S
shobha
11/29/2025 2:19:59 AM

very helpful

P
Pandithurai
11/12/2025 12:16:21 PM

Question 1, Ans is - Developer,Standard,Professional Direct and Premier

E
Einstein
11/8/2025 4:13:37 AM

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D
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10/31/2025 4:06:16 PM

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T
Thor
10/21/2025 5:16:29 AM

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V
Vladimir
9/25/2025 9:11:14 AM

173 question is A not D

K
khaos
9/21/2025 7:07:26 AM

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K
Katiso Lehasa
9/15/2025 11:21:52 PM

Thanks for the practice questions they helped me a lot.

E
Einstein
9/2/2025 7:42:00 PM

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V
vito
8/22/2025 4:16:51 AM

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M
Matt
7/31/2025 11:44:40 PM

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O
OLERATO
7/1/2025 5:44:14 AM

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A
Adekunle willaims
6/9/2025 7:37:29 AM

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A
Alex
5/24/2025 12:54:15 AM

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