Cloudera CDP Data Analyst study guide
Cloudera CDP Data Analyst · Work through your exam, one topic at a time.
The Cloudera CDP Data Analyst credential is designed for analysts who work with Cloudera Data Platform (CDP) to query, prepare, and visualize data. It suits professionals who already understand SQL and data analysis concepts and need to demonstrate competence with CDP-specific tools such as Apache Hive, Apache Impala, Apache Kudu, Apache Spark, and Cloudera Data Engineering. The Cloudera CDP Data Analyst study guide has 8 sections and about 3,633 words.
01Data Analysis Foundations on CDP
This domain tests whether you can choose the right CDP service, storage format, and table type for a workload, and whether you understand that Hive, Impala, Spark, and Kudu all resolve tables through one shared Hive Metastore. Storage : HDFS or Ozone files, or Kudu/HBase for operational data.
459 words, about 2 min02SQL Querying with Apache Hive and Apache Impala
Hive and Impala share the Hive Metastore and a largely common SQL dialect, but their execution models, metadata-freshness rules, and write capabilities differ. Exam items concentrate on those differences: refresh semantics, partition DDL, dynamic partition inserts, join strategies, and analytic functions.
444 words, about 2 min03Data Exploration and Preparation
This domain covers profiling unfamiliar tables, cleaning types and nulls, deduplicating, and producing trustworthy curated tables. Exam items are practical: given a messy dataset description, which check or transformation reveals the defect and which silently corrupts results.
441 words, about 2 min04Analytics on Apache Kudu and Apache HBase
Kudu and HBase are CDP's operational stores, and the exam tests the design constraints that make them fast or unusable: primary keys and partitioning in Kudu, rowkey design in HBase, and how Impala pushes predicates down to Kudu. A primary key is mandatory and its columns must be declared first and cannot be null.
462 words, about 2 min05Apache Spark and Cloudera Data Engineering for Analysts
This domain tests Spark's execution model and how Cloudera Data Engineering (CDE) packages Spark work for production: lazy evaluation, shuffles, join strategies, file sizing, and the job/session lifecycle. Transformations ( select , filter , join ) are lazy ; nothing runs until an action ( count , write , show , collect ).
454 words, about 2 min06Data Visualization and BI Integration
This domain tests how BI tools connect to CDP, where aggregation actually happens, and why dashboards return different numbers than SQL run directly. Expect questions on Impala/Hive connectivity, Knox, Kerberos, direct-query versus extract, and Cloudera Data Visualization.
456 words, about 2 min07Security, Governance, and Metadata (Ranger, Atlas, SDX)
This domain tests how CDP enforces authorization consistently across engines and how metadata and lineage are captured. Questions usually describe a user, a group, and a table, then ask what the user actually sees.
454 words, about 2 min08Performance Tuning and Troubleshooting Queries
This domain tests whether you can diagnose a slow query from evidence - plan, statistics, partition counts, file sizes - rather than guessing. Expect scenario items: a query was fast, a load changed, now it is slow; which fix applies.
463 words, about 2 min