COMPTIA DATA+ EXAM PREP QUESTIONS
AND SOLUTIONS 2026 FULL SOLUTION
GRADED A+
◍ Chi-Square Test. Answer: hypothesis testing method for whether
your data is as expected.
If you have a single measurement variable, you use a Chi-square
goodness of fit test. If you have two measurement variables, you use a
Chi-square test of independence.
◍ XML. Answer: Structured text all in <>
◍ Online transactional processing (OLTP). Answer: live capture of
transaction and event info to:
1) process info according to defined business rules
2) store info
3) update existing info if changed
Typically, OLTP databases are in 3NF.
◍ Online analytical processing (OLAP). Answer: using queries to
investigate, analyze, & report hypothesized relationships among
stored data
databases that power OLAP systems have a denormalized design.
Instead of having data distributed across multiple tables,
,denormalization results in wider tables than those found in an OLTP
database. It is more efficient for analytical queries to read large
amounts of data for a single table instead of incurring the cost of
joining multiple tables together.
◍ Extract, transform, load (ETL). Answer: The process by which raw
data is extracted from various sources, transformed into a format to
support the analysis to be performed, and loaded into the data
warehouse.
◍ Extract, load, transform (ELT). Answer: An alternative to ETL used
with data lakes, where the data is not transformed on entry to the data
lake, but stored in its original raw format
◍ Delta Load (incremental load). Answer: delta between target and
source data is dumped at regular intervals. The last extract date is
stored so that only records added after this date are loaded.
Incremental loads come in two flavors that vary based on the volume
of data you're loading:
Streaming incremental load - better for loading small data volumes
Batch incremental load - better for loading large data volumes
understand the time available for performing delta loads into your
data warehouse. Regardless of how long your batch window is, think
carefully about moving current data into the data warehouse without
losing history.
, ◍ Non-Parametric Data. Answer: Data that does not fit a known or
well-understood distribution
Usually ordinal or interval data
For real-valued data, nonparametric statistical methods are required in
applied machine learning when you are trying to make claims on data
that does not fit the familiar Gaussian distribution.
◍ Normalize Ratings Data. Answer: normalization of ratings means
adjusting values measured on different scales to a notionally common
scale, often prior to averaging
◍ Descriptive Statistical Methods. Answer: • Measures of central
tendency
• Measures of dispersion
• Frequencies/percentages
• Percent change
• Percent difference
• Confidence interval
◍ Inferential Statistical Methods. Answer: provide predictions about
characteristics of a population based on information in a sample from
that population
◍ Measures of Central Tendency. Answer: mean, median, mode
AND SOLUTIONS 2026 FULL SOLUTION
GRADED A+
◍ Chi-Square Test. Answer: hypothesis testing method for whether
your data is as expected.
If you have a single measurement variable, you use a Chi-square
goodness of fit test. If you have two measurement variables, you use a
Chi-square test of independence.
◍ XML. Answer: Structured text all in <>
◍ Online transactional processing (OLTP). Answer: live capture of
transaction and event info to:
1) process info according to defined business rules
2) store info
3) update existing info if changed
Typically, OLTP databases are in 3NF.
◍ Online analytical processing (OLAP). Answer: using queries to
investigate, analyze, & report hypothesized relationships among
stored data
databases that power OLAP systems have a denormalized design.
Instead of having data distributed across multiple tables,
,denormalization results in wider tables than those found in an OLTP
database. It is more efficient for analytical queries to read large
amounts of data for a single table instead of incurring the cost of
joining multiple tables together.
◍ Extract, transform, load (ETL). Answer: The process by which raw
data is extracted from various sources, transformed into a format to
support the analysis to be performed, and loaded into the data
warehouse.
◍ Extract, load, transform (ELT). Answer: An alternative to ETL used
with data lakes, where the data is not transformed on entry to the data
lake, but stored in its original raw format
◍ Delta Load (incremental load). Answer: delta between target and
source data is dumped at regular intervals. The last extract date is
stored so that only records added after this date are loaded.
Incremental loads come in two flavors that vary based on the volume
of data you're loading:
Streaming incremental load - better for loading small data volumes
Batch incremental load - better for loading large data volumes
understand the time available for performing delta loads into your
data warehouse. Regardless of how long your batch window is, think
carefully about moving current data into the data warehouse without
losing history.
, ◍ Non-Parametric Data. Answer: Data that does not fit a known or
well-understood distribution
Usually ordinal or interval data
For real-valued data, nonparametric statistical methods are required in
applied machine learning when you are trying to make claims on data
that does not fit the familiar Gaussian distribution.
◍ Normalize Ratings Data. Answer: normalization of ratings means
adjusting values measured on different scales to a notionally common
scale, often prior to averaging
◍ Descriptive Statistical Methods. Answer: • Measures of central
tendency
• Measures of dispersion
• Frequencies/percentages
• Percent change
• Percent difference
• Confidence interval
◍ Inferential Statistical Methods. Answer: provide predictions about
characteristics of a population based on information in a sample from
that population
◍ Measures of Central Tendency. Answer: mean, median, mode
