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100+ Free Cloudera CDP Data Analyst Practice Questions

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Which component holds the schema definitions (databases, tables, columns, partitions) that Hive and Impala share in CDP?

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2026 Statistics

Key Facts: Cloudera CDP Data Analyst Exam

50

Number of Questions

Cloudera (CDP-4001 exam guide)

120 min

Time Limit

Cloudera (CDP-4001 exam guide)

60%

Passing Score

Cloudera (CDP-4001 exam guide)

~$300

Exam Fee (USD, approximate)

Third-party sources (Cloudera lists no fixed price)

20% + 20%

Hive/Impala and Aggregate Statistics Weight

Cloudera (CDP-4001 blueprint)

Online proctored

Delivery Format

Cloudera (via QuestionMark)

Cloudera's CDP Data Analyst exam (CDP-4001) has 50 multiple-choice questions, a 120-minute limit, and a 60% passing score, delivered online and proctored through QuestionMark. The fee is roughly $300 USD (third-party sources list about $330). The blueprint weights Use Apache Hive and Impala at 20%, Calculate aggregate statistics at 20%, Hive and Impala Optimization at 12%, and Cloudera Data Visualizations, Apache Ranger and Atlas, Data Management and Storage, and Cloudera Data Warehouse at 10% each, with Apache Hive and Impala SQL at 8%.

Sample Cloudera CDP Data Analyst Practice Questions

Try these sample questions to test your Cloudera CDP Data Analyst exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1In Impala, which behavior do aggregate functions such as AVG() and SUM() exhibit when a column contains NULL values?
A.They return NULL for the entire result if any row is NULL
B.They ignore NULL values and aggregate only the non-NULL rows
C.They treat NULL as zero before aggregating
D.They raise a runtime error unless a WHERE clause filters NULLs
Explanation: Impala aggregate functions ignore NULL values rather than returning a NULL result. For example, AVG() computes the average over only the non-NULL rows, and COUNT(col_name) counts only rows where the column is non-NULL. This matches standard SQL aggregation semantics.
2A data analyst needs the number of distinct customer IDs in an orders table using Impala. Which expression returns that value?
A.COUNT(customer_id)
B.SUM(DISTINCT customer_id)
C.DISTINCT COUNT(customer_id)
D.COUNT(DISTINCT customer_id)
Explanation: COUNT(DISTINCT customer_id) returns the number of unique non-NULL values in the column. Plain COUNT(customer_id) counts all non-NULL rows including duplicates, so it overstates the count when customers repeat across orders.
3When you write SELECT region, SUM(amount) FROM sales GROUP BY region in Hive, what does the GROUP BY clause accomplish?
A.It collapses rows into one row per distinct region so SUM is computed per group
B.It sorts the output rows by region in ascending order
C.It removes duplicate region values from the result without aggregating
D.It filters out regions whose total amount is below a threshold
Explanation: GROUP BY partitions the rows into groups that share the same region value, and the aggregate SUM(amount) is then computed independently for each group, returning one row per distinct region. Any non-aggregated column in the SELECT list must appear in the GROUP BY clause.
4Which clause must you use to filter the results of a GROUP BY query based on the value of an aggregate function such as SUM(amount) > 1000?
A.WHERE
B.HAVING
C.FILTER
D.QUALIFY
Explanation: HAVING filters groups after aggregation, so conditions on aggregate functions like SUM(amount) > 1000 belong there. WHERE is evaluated before grouping and cannot reference aggregate results.
5A report needs the highest and lowest transaction values per store. Which pair of Impala aggregate functions provides these directly?
A.MAX() and MIN()
B.TOP() and BOTTOM()
C.FIRST() and LAST()
D.CEIL() and FLOOR()
Explanation: MAX() returns the largest value and MIN() returns the smallest value within each group, so SELECT store, MAX(amount), MIN(amount) FROM t GROUP BY store gives the highest and lowest transaction per store. These are standard SQL aggregate functions supported by both Hive and Impala.
6Which statement correctly describes the difference between COUNT(*) and COUNT(column_name) in Hive and Impala?
A.They always return the same value regardless of NULLs
B.COUNT(*) counts all rows including those with NULLs; COUNT(column_name) counts only rows where that column is non-NULL
C.COUNT(*) counts distinct rows only; COUNT(column_name) counts all rows
D.COUNT(*) is invalid syntax and must be written as COUNT(1)
Explanation: COUNT(*) counts every row in the group regardless of NULLs, while COUNT(column_name) counts only the rows where that specific column has a non-NULL value. When a column contains NULLs, the two counts diverge.
7An analyst wants a running monthly total of sales without collapsing individual rows. Which feature supports this in Hive and Impala?
A.A correlated subquery in the WHERE clause
B.A LATERAL VIEW EXPLODE on the month column
C.A GROUP BY ROLLUP on month
D.An analytic (window) function with SUM() OVER (ORDER BY month)
Explanation: Analytic, or window, functions such as SUM(amount) OVER (ORDER BY month) compute a cumulative value across an ordered window while preserving each input row. Unlike a plain GROUP BY, the original rows are not collapsed, which is exactly what a running total requires.
8Which Impala aggregate function returns the arithmetic mean of a numeric column?
A.AVG()
B.MEDIAN()
C.MODE()
D.MEAN()
Explanation: AVG() computes the arithmetic mean by summing the non-NULL values and dividing by the count of non-NULL values. Impala does not provide built-in MEDIAN(), MODE(), or MEAN() aggregates under those names.
9In the query SELECT dept, COUNT(*) FROM employees GROUP BY dept HAVING COUNT(*) >= 5, what is returned?
A.Every department with its employee count, sorted descending
B.The first five departments encountered in the table
C.Only departments that have at least five employees, with their counts
D.A single row with the total number of departments
Explanation: GROUP BY produces one row per department with its employee count, and HAVING COUNT(*) >= 5 keeps only the groups whose count is five or greater. The result is the set of departments meeting the size threshold along with their counts.
10What is the logical order of evaluation for a SELECT statement that contains WHERE, GROUP BY, HAVING, and ORDER BY clauses in Hive/Impala?
A.GROUP BY, then WHERE, then HAVING, then ORDER BY
B.WHERE, then GROUP BY, then HAVING, then ORDER BY
C.ORDER BY, then WHERE, then GROUP BY, then HAVING
D.HAVING, then WHERE, then GROUP BY, then ORDER BY
Explanation: Rows are first filtered by WHERE, then grouped by GROUP BY, then groups are filtered by HAVING, and finally the result set is sorted by ORDER BY. Understanding this order explains why WHERE cannot reference aggregates and HAVING can.

About the Cloudera CDP Data Analyst Exam

Exam CDP-4001 earns the Cloudera CDP Data Analyst certification, validating the SQL and platform skills a data analyst needs on the Cloudera Data Platform. The blueprint centers on using Apache Hive and Impala to query data, calculating aggregate statistics, combining datasets with joins and unions, and creating tables and views. It also covers Hive/Impala optimization (predicate pushdown, bucketing, file formats, and COMPUTE STATS), data management and storage in HDFS (managed versus external tables and partitioning), the Cloudera Data Warehouse service (Virtual Warehouses and the Database Catalog), Cloudera Data Visualization dashboards, and governance with Apache Ranger access policies and Apache Atlas lineage. The 50-question exam is delivered online and proctored through QuestionMark, with no reference materials allowed.

Questions

50 scored questions

Time Limit

120 minutes

Passing Score

60%

Exam Fee

~$300 (Cloudera)

Cloudera CDP Data Analyst Exam Content Outline

20%

Use Apache Hive and Impala

Identify databases and tables in Impala, format and convert data types with CAST and built-in functions, join tables with inner, left/right/full outer, semi, and cross joins, combine datasets with UNION/UNION ALL/INTERSECT/EXCEPT, and work with primary and foreign keys in a star schema.

20%

Calculate aggregate statistics

Use aggregate functions such as COUNT, SUM, AVG, MIN, MAX, COUNT DISTINCT, NDV, and STDDEV with GROUP BY and HAVING, understand that aggregates ignore NULLs, and apply window functions and ROLLUP for running totals and subtotals.

12%

Hive and Impala Optimization

Push filter conditions (predicate pushdown), use bucketing for high-cardinality columns, choose columnar file formats like Parquet, and run COMPUTE STATS so the cost-based planner estimates cardinalities and chooses efficient join orders.

10%

Use Cloudera Data Visualizations

Build datasets on Hive/Impala connections, classify fields as dimensions versus measures, choose the right visual type, and arrange visuals into dashboards for collaborative self-service analytics.

10%

Use Apache Ranger and Atlas

Inspect upstream and downstream data lineage in Apache Atlas, define resource-based and tag-based access and masking policies in Apache Ranger, and understand how a data steward classifies assets to drive governance.

10%

Data Management and Storage

Understand how data is stored and replicated in HDFS, store query results into tables or directories, distinguish managed (internal) from external tables, and use partitioning for partition pruning.

10%

Cloudera Data Warehouse

Manage Virtual Warehouses (compute) and the Database Catalog (storage) that are decoupled by design, activate environments, and run queries in Cloudera Data Explorer (formerly Hue).

8%

Use Apache Hive and Impala SQL

Create new tables and views using CREATE TABLE AS SELECT and CREATE VIEW, set file formats with STORED AS, and refresh Impala metadata with INVALIDATE METADATA and REFRESH.

How to Pass the Cloudera CDP Data Analyst Exam

What You Need to Know

  • Passing score: 60%
  • Exam length: 50 questions
  • Time limit: 120 minutes
  • Exam fee: ~$300

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

Cloudera CDP Data Analyst Study Tips from Top Performers

1Master aggregate semantics: know that COUNT, SUM, and AVG ignore NULLs, the difference between COUNT(*) and COUNT(column) and COUNT(DISTINCT column), and when to use GROUP BY versus HAVING.
2Drill every join type (inner, left/right/full outer, semi, cross) and the union family (UNION, UNION ALL, INTERSECT, EXCEPT), including the column-count and data-type rules for set operations.
3Practice optimization concepts: predicate pushdown, partition pruning, bucketing, columnar Parquet storage, and running COMPUTE STATS so the planner picks good join orders.
4Know data management: managed versus external tables and what DROP TABLE does to each, partitioning DDL, and how to store query results into tables or HDFS directories.
5Understand the Cloudera Data Warehouse model: Virtual Warehouses are decoupled compute, the Database Catalog is storage backed by the Hive Metastore, and Cloudera Data Explorer (Hue) is the SQL editor.
6Learn the governance split: Apache Atlas provides metadata, classification, and lineage, while Apache Ranger enforces resource-based and tag-based access and masking policies driven by data-steward classifications.

Frequently Asked Questions

What are the exam facts for Cloudera CDP-4001?

CDP-4001 is the Cloudera CDP Data Analyst exam with 50 multiple-choice questions, a 120-minute time limit, and a 60% passing score. It is delivered online and proctored through QuestionMark, and no reference materials are allowed during the exam.

How much does the CDP-4001 exam cost?

Cloudera does not list a fixed price on the official CDP-4001 exam page, but third-party sources report a fee of around $300 to $330 USD. Confirm the current fee when you register through Cloudera.

Which topics carry the most weight on CDP-4001?

Use Apache Hive and Impala and Calculate aggregate statistics each carry 20% of the exam. Hive and Impala Optimization is 12%, and Cloudera Data Visualizations, Apache Ranger and Atlas, Data Management and Storage, and Cloudera Data Warehouse are 10% each, with Apache Hive and Impala SQL at 8%.

Do I need to know both Hive and Impala?

Yes. The exam tests querying with both Apache Hive and Apache Impala, including joins, unions, aggregate functions, table and view creation, and performance features such as COMPUTE STATS and partition pruning that apply across both engines.

What governance tools does CDP-4001 cover?

The exam covers Apache Atlas for data lineage and classification and Apache Ranger for access policies and data masking, including tag-based policies where a data steward classifies sensitive data such as PII and Ranger enforces access across services.

Is the exam multiple choice or hands-on?

CDP-4001 is a multiple-choice exam delivered online and proctored, unlike Cloudera's older hands-on CCA exams. It tests conceptual and SQL knowledge rather than performing tasks on a live cluster.