PALANTIR DATA ENGINEERING
CERTIFICATION EXAM(ACTUAL EXAM)
WITH CORRECT ACTUAL QUESTIONS AND
CORRECTLY WELL DEFINED ANSWERS
LATEST ALREADY GRADED A+ 2026
12. Which of the following practices help in minimizing
breaking changes when modifying dataset schemas? Select
two.
Modifying existing columns to repurpose them for new data
types.
Avoiding any changes to the schema to prevent breaking
changes.
Creating new columns instead of modifying or deleting
existing ones.
Deleting old columns immediately after adding new ones.
Announcing deprecation of old columns and providing
instructions to data consumers.
Creating new columns instead of modifying or deleting
existing ones.
,Announcing deprecation of old columns and providing
instructions to data consumers.
16. You have set up a post-condition Data Expectation on
the output of a transform in Foundry. If this expectation fails
during a build, what will occur?
The build is automatically aborted, and the output is not
written.
The input dataset is aborted to prevent issues.
The build is resumed with a warning.
The failed expectation is ignored, and the build continues.
The build is automatically aborted, and the output is not
written.
17. In a Foundry Transform, you need to perform random
access to a file to read specific lines multiple times. Given
that FileSystem.open() does not support random access,
what is a recommended workaround?
Split the file into smaller chunks and process them
separately.
Buffer the entire file into memory using io.StringIO or a
temporary file.
Enable random access by configuring FileSystem to support
seek and tell.
Use multiple FileSystem.open() calls to access different parts
of the file.
,Buffer the entire file into memory using io.StringIO or a
temporary file.
24. When defining a Transform with multiple outlets, how
should you write the compute function to utilize the filtered
DataFrame only once for generating all outputs?
Filter the DataFrame separately for each output within the
compute function.
Leverage the TransformContext to manage DataFrame
filtering.
Filter the DataFrame once and assign it to a variable, then
use that variable to generate each output.
Use multiple compute functions, each handling a different
output.
Filter the DataFrame once and assign it to a variable, then use
that variable to generate each output.
26. You are debugging a Python transform in Foundry and
find that a breakpoint within an internal library is colored
grey. What should you do to effectively debug this location?
Enable internal libraries debugging in the debugger settings.
Ignore the breakpoint and proceed with execution.
Use print statements instead of breakpoints.
Remove the breakpoint as it cannot be used.
Enable internal libraries debugging in the debugger settings.
, 28. What are the roles of 'intermediate' datasets in a
Foundry data pipeline schedule?
Datasets not built by the schedule but used as inputs.
Datasets built by the schedule and used by other datasets
within the same schedule.
Find datasets consumed by external applications.
Datasets built by the schedule that are not used by any
other datasets in the schedule.
Datasets built by the schedule and used by other datasets
within the same schedule.
30. Which of the following steps are necessary to set up test
coverage reporting in your Python repository using PyTest?
Apply the 'com.palantir.conda.pep8' Gradle plugin.
Add 'pytest-cov' to the test requirements in meta.yml.
Create a pytest.ini file with coverage options.
Set the 'coverage-report' parameter in build.gradle.
Add 'pytest-cov' to the test requirements in meta.yml.
Create a pytest.ini file with coverage options.
31. You are working on a PySpark transformation in Foundry
and need to rename all columns of a DataFrame from
uppercase to lowercase. The current implementation uses a
for loop to iterate over the columns, which is causing
performance issues. According to the PySpark style guide,
what is the recommended approach to rename the columns
CERTIFICATION EXAM(ACTUAL EXAM)
WITH CORRECT ACTUAL QUESTIONS AND
CORRECTLY WELL DEFINED ANSWERS
LATEST ALREADY GRADED A+ 2026
12. Which of the following practices help in minimizing
breaking changes when modifying dataset schemas? Select
two.
Modifying existing columns to repurpose them for new data
types.
Avoiding any changes to the schema to prevent breaking
changes.
Creating new columns instead of modifying or deleting
existing ones.
Deleting old columns immediately after adding new ones.
Announcing deprecation of old columns and providing
instructions to data consumers.
Creating new columns instead of modifying or deleting
existing ones.
,Announcing deprecation of old columns and providing
instructions to data consumers.
16. You have set up a post-condition Data Expectation on
the output of a transform in Foundry. If this expectation fails
during a build, what will occur?
The build is automatically aborted, and the output is not
written.
The input dataset is aborted to prevent issues.
The build is resumed with a warning.
The failed expectation is ignored, and the build continues.
The build is automatically aborted, and the output is not
written.
17. In a Foundry Transform, you need to perform random
access to a file to read specific lines multiple times. Given
that FileSystem.open() does not support random access,
what is a recommended workaround?
Split the file into smaller chunks and process them
separately.
Buffer the entire file into memory using io.StringIO or a
temporary file.
Enable random access by configuring FileSystem to support
seek and tell.
Use multiple FileSystem.open() calls to access different parts
of the file.
,Buffer the entire file into memory using io.StringIO or a
temporary file.
24. When defining a Transform with multiple outlets, how
should you write the compute function to utilize the filtered
DataFrame only once for generating all outputs?
Filter the DataFrame separately for each output within the
compute function.
Leverage the TransformContext to manage DataFrame
filtering.
Filter the DataFrame once and assign it to a variable, then
use that variable to generate each output.
Use multiple compute functions, each handling a different
output.
Filter the DataFrame once and assign it to a variable, then use
that variable to generate each output.
26. You are debugging a Python transform in Foundry and
find that a breakpoint within an internal library is colored
grey. What should you do to effectively debug this location?
Enable internal libraries debugging in the debugger settings.
Ignore the breakpoint and proceed with execution.
Use print statements instead of breakpoints.
Remove the breakpoint as it cannot be used.
Enable internal libraries debugging in the debugger settings.
, 28. What are the roles of 'intermediate' datasets in a
Foundry data pipeline schedule?
Datasets not built by the schedule but used as inputs.
Datasets built by the schedule and used by other datasets
within the same schedule.
Find datasets consumed by external applications.
Datasets built by the schedule that are not used by any
other datasets in the schedule.
Datasets built by the schedule and used by other datasets
within the same schedule.
30. Which of the following steps are necessary to set up test
coverage reporting in your Python repository using PyTest?
Apply the 'com.palantir.conda.pep8' Gradle plugin.
Add 'pytest-cov' to the test requirements in meta.yml.
Create a pytest.ini file with coverage options.
Set the 'coverage-report' parameter in build.gradle.
Add 'pytest-cov' to the test requirements in meta.yml.
Create a pytest.ini file with coverage options.
31. You are working on a PySpark transformation in Foundry
and need to rename all columns of a DataFrame from
uppercase to lowercase. The current implementation uses a
for loop to iterate over the columns, which is causing
performance issues. According to the PySpark style guide,
what is the recommended approach to rename the columns
