CCA Data Analyst (CCA159) Practice Exam

The Cloudera Certified Associate Data Analyst (CCA159) exam validates your proficiency in using Apache Hive and Apache Impala, two foundational tools within the Cloudera Data Platform (CDP) for big data analytics. Earning this certification demonstrates your ability to extract, transform, and analyze large datasets stored in distributed storage systems like HDFS (Hadoop Distributed File System).

Who Should Take This Exam?

The CCA159 exam is ideal for individuals seeking to:

• Launch their career in big data analytics: Gain a solid foundation in big data tools and demonstrate your skills to potential employers.
• Advance their data analyst skillset: Enhance their knowledge of Apache Hive and Impala for big data processing on the Cloudera platform.
• Transition into big data from SQL backgrounds: Leverage existing SQL knowledge to learn big data query languages like HiveQL (Hive Query Language) and Impala SQL.

Prerequisites

• 3+ years of experience developing and deploying applications using relational databases (SQL experience is valuable).
• Basic understanding of data warehousing concepts: Familiarity with data extraction, transformation, and loading (ETL) processes would be beneficial.
• Linux command-line experience: Basic proficiency in navigating the Linux command line is helpful for interacting with big data systems.

Roles and Responsibilities

• Cloudera Data Analyst (Entry Level): Extracting, transforming, and analyzing data using Hive and Impala on the Cloudera platform.
• Big Data Analyst (CDP Skills): Demonstrating proficiency in big data tools and techniques relevant to the Cloudera ecosystem.
• SQL Developer (Big Data Focus): Developing data processing pipelines using SQL-like languages for big data on Cloudera.
• Data Engineer (Junior Level): Contributing to big data infrastructure and data pipelines involving Hive and Impala.

Exam Details 

• Time Limit: 120 minutes
• Passing Score: 70%
• Language: English
• Price: USD $295

Skills Required 

1. Prepare the Data

• Use Extract, Transfer, Load (ETL) processes to prepare data for queries.
• Import data from a MySQL database into HDFS using Sqoop
• Export data to a MySQL database from HDFS using Sqoop
• Move data between tables in the metastore
• Transform values, columns, or file formats of incoming data before analysis

2. Provide Structure to the Data

• Use Data Definition Language (DDL) statements to create or alter structures in the metastore for use by Hive and Impala.
• Create tables using a variety of data types, delimiters, and file formats
• Create new tables using existing tables to define the schema
• Improve query performance by creating partitioned tables in the metastore
• Alter tables to modify existing schema
• Create views in order to simplify queries

3. Data Analysis

• Use Query Language (QL) statements in Hive and Impala to analyze data on the cluster.
• Prepare reports using SELECT commands including unions and subqueries
• Calculate aggregate statistics, such as sums and averages, during a query
• Create queries against multiple data sources by using join commands
• Transform the output format of queries by using built-in functions
• Perform queries across a group of rows using windowing functions