Question 1

How many questions are on the CDP Data Analyst exam?

Accepted Answer

Our record lists 50 questions for the CDP Data Analyst. The vendor does not publish a question count, so treat that as a working figure rather than an official specification.

Question 2

How long do you have to complete the CDP Data Analyst exam?

Accepted Answer

Our record lists 120 minutes, roughly 144 seconds per question. The vendor does not publish a duration for this exam, so use it as a planning figure.

Question 3

What score do you need to pass the CDP Data Analyst exam?

Accepted Answer

Our record lists 60% as the pass mark for the CDP Data Analyst. Most certification bodies do not publish a numeric cut score, so treat this as our benchmark rather than an official threshold.

Question 4

What are the domains on the CDP Data Analyst exam?

Accepted Answer

There are 4 domains: Data Analysis, Data Preparation, Data Visualization, Sharing and Managing Results. The official weights are Data Analysis 40%, Data Preparation 20%, Data Visualization 20%, Sharing and Managing Results 20%.

Question 5

How should I use the domain weights when revising?

Accepted Answer

Weight by percentage, not by domain. 40% is the one where lost points cost you the most, and Certilum scores you per domain so you can see exactly where that is.

Question 6

Does Certilum have practice questions for the CDP Data Analyst exam?

Accepted Answer

Yes: 50 questions in our bank for this certification, each tagged to the exam domain it belongs to, with the official domain weights applied.

Specification	Value
Questions	50
Time limit	120 minutes (144s per question)
Passing score	60%
Domains	4
Level	Associate
Question bank here	50 questions
Source	Certilum's own record — the vendor does not publish this figure

CDP Data Analyst Exam Format

Exam facts at a glance

Official domain weights

More about the CDP Data Analyst exam

Related certifications

Frequently asked questions

Domain	Weight	Topics	Practice Qs
Data Analysis	40%	Writing complex queries with aggregations, window functions, and joins Performing exploratory data analysis (EDA) using statistical summaries Implementing business rules and conditional logic in queries +3 more	20
Data Preparation	20%	Importing data from various sources (HDFS, Hive, HBase, Kafka) Cleaning and transforming data using and Python Handling missing values, duplicates, and data type conversions +2 more	10
Data Visualization	20%	Creating visualizations using Cloudera Data Visualization (CDV) or Apache Superset Selecting appropriate chart types for different data stories Building dashboards with filters and drill-down capabilities +2 more	10
Sharing and Managing Results	20%	Exporting analysis results to different formats (CSV, Parquet, PDF) Automating report generation using scripts and scheduling tools Managing data access and security in a CDP environment +2 more	10