CCA Data Analyst

CCA Data Analyst Exam (CCA159) 

• Number of Questions: 8–12 performance-based (hands-on) tasks on a Cloudera Enterprise cluster. See below for full cluster configuration
• Time Limit: 120 minutes
• Passing Score: 70%
• Language: English

Exam Question Format

You are given eight to twelve customer problems with a unique large data set, a CDH cluster, and 120 minutes. For each problem, you must implement a technical solution with a high degree of precision that meets all the requirements. You must possess enough knowledge to analyze the problem and arrive at an optimal approach given the time allowed. You need to know what you should do and then do it on a live cluster, including a time limit and while being watched by a proctor.

Evaluation, Score Reporting, and Certificate

Your exam is graded immediately upon submission and you are e-mailed a score report within three days of your exam. Your score report displays the problem number for each problem you attempted and a grade on that problem. If you fail a problem, the score report includes the criteria you failed (e.g., “Records contain incorrect data” or “Incorrect file format”). We do not report more information in order to protect the exam content. Read more about reviewing exam content on the FAQ.

If you pass the exam, you receive a second e-mail within a week of your exam with your digital certificate as a PDF and your license number.

Audience and Prerequisites

Candidates for CCA Data Analyst can be SQL developers, data analysts, business intelligence specialists, developers, system architects, and database administrators. There are no prerequisites.

The CCA Data Analyst exam was created to identify talented SQL developers looking to stand out and be recognized by employers looking for these skills. It is recommended that those looking to achieve this certification start by taking Cloudera’s Data Analyst training course, which has the same objectives as the exam.

Required Skills

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

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

Watch a free OnDemand course to help prepare for your certification

Have questions? Read our Certification FAQ

Contact us at certification@cloudera.com