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Cloudera CCA175 : CCA Spark and Hadoop Developer Exam

CCA175

Exam Code: CCA175

Exam Name: CCA Spark and Hadoop Developer Exam

Updated: Aug 20, 2026

Q & A: 96 Questions and Answers

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Cloudera Certified Advanced Architect- Data Engineer (CCA175) is Cloudera's first certification to apply Spark and Hadoop to Java development. After the application of the certification, the technology will be applied to other Cloudera products besides big data. Content of Cloudera Certified Advanced Architect- Data Engineer (CCA175) certification is high level of expertise in Apache Spark and data processing. Change in IT requirements has made it necessary for your success in Cloudera Certified Advanced Architect- Data Engineer. We have designed the CCA Spark and Hadoop Developer (CCA175) questions with your success in mind. The certification exam is based on Cloudera technologies. Updates in the technology and trends and high level of expertise in the industry make it necessary for your success in Cloudera Certified Advanced Architect- Data Engineer exam. We have designed CCA Spark and Hadoop Developer (CCA175) questions with your success in mind.

Certificate for CCA Spark and Hadoop Developer (CCA175) will be awarded for the proven skills in Spark, Java, and Java MapReduce. Scored by Cloudera, the exam will be based on Apache Spark with programming in Java. Preparing for CCA175 exam will take you one step closer to achieving your goals. The CCA175 exam is based on experience in Apache Spark. Specialized skills in the AWS infrastructure will be tested by CCA175 exam questions. Tables and columns data structures, JavaScript, and functional programming will be tested by CCA175 exam questions.

Reference: https://www.cloudera.com/about/training/certification/cdhhdp-certification/cca-spark.html

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Cloudera CCA175 Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Processing with Apache Spark- Spark SQL
  • 1. Querying structured data
    • 2. User-defined functions (UDFs)
      - Spark core operations
      • 1. DataFrame and Dataset APIs
        • 2. RDD transformations and actions
          Topic 2: Data Analysis with Hive and Impala- Hive querying
          • 1. Partitioning and bucketing
            • 2. HiveQL basics
              - Impala performance querying
              • 1. Interactive SQL queries
                Topic 3: Data Storage and File Systems- File formats
                • 1. Compression techniques
                  • 2. Parquet and ORC usage
                    - HDFS fundamentals
                    • 1. Data replication and blocks
                      • 2. File operations in HDFS
                        Topic 4: Data Ingestion and ETL- Data transformation
                        • 1. Cleaning and filtering datasets
                          • 2. Joining and aggregating data
                            - Data ingestion methods
                            • 1. Batch ingestion pipelines
                              • 2. Flume and Sqoop concepts

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