Free Practice Questions for Palantir Foundry Data Engineer Certification Certification
- Guide checked for updates:
- 9 Oct 2026
- Question bank created:
- 10 Apr 2026
- Question bank last updated:
- 11 Sep 2026
Study with 350 exam-style practice questions designed to help you prepare for the Palantir Foundry Data Engineer Certification.
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Key information about Palantir Foundry Data Engineer Certification
- Multiple choice
- Ordering
- Matching
Multiple choice (select one, select multiple, discrete options), Hotspot questions (click on image), matching questions
70%
6+ months of experience on the Palantir Foundry platform; general knowledge of distributed computing frameworks (e.g., Spark) and programming languages (e.g., Python, SQL) recommended but not required.
Individuals capable of executing in data integration and ontology development projects, responsible for producing or maintaining high-quality data assets in Foundry, with 6+ months of platform experience.
120
60
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Data Pipeline Development in Foundry
1.1: Process tabular data in Transforms
Process tabular data in Transforms. Learning resources — Documentation: Data Integration; Tools: Code Repositories, Python Transforms, Contour. Self-service learning: Video: How to Write Data Transforms.
1.2: Process unstructured data in Transforms
Process unstructured data in Transforms. Learning resources — Documentation: Building Pipelines: Unstructured Data, Transforms Reference; Tools: Code Repositories, Code Workbook. Self-service learning: Video: Parsing Excel Files.
1.3: Configure pipeline for production use
Configure pipeline for production use. Learning resources — Documentation: Building Pipelines; Tools: Data Health, Data Lineage.
1.4: Apply general best practices during pipeline development
Apply general best practices during pipeline development. Learning resources — Documentation: Code Repositories: Project References, Unit Tests; Transforms: PySpark Style Guide, Using Libraries.
2: Data Pipeline Maintenance in Foundry
2.1: Debug an issue in a production pipeline
Debug an issue in a production pipeline. Learning resources — Documentation: Tools: Data Lineage; Code Repositories: Debug Transforms; Optimizing Pipelines: Debug Job, Troubleshoot OOM Errors.
2.2: Contribute changes to a production pipeline
Contribute changes to a production pipeline. Learning resources — Documentation: Data Integration: Data Pipeline, Branching; Code Repositories: Branches, Share Python Libraries; Building Pipelines: Release Process.
2.3: Set up support structure for production pipeline
Set up support structure for production pipeline. Learning resources — Documentation: Maintaining Pipelines; Tools: Data Health. Self-service learning: Video: Pipeline Monitoring Playlist.
2.4: Improve performance of production pipeline
Improve performance of production pipeline. Learning resources — Documentation: Incremental Pipelines: Overview, Syncs, Transforms, Examples; Optimizing Pipelines. Self-service learning: Video: Incremental Data Transforms. Reference Workflows: Advanced incremental data processing with PySpark in Code Repositories.
3: Data Connection and Integration in Foundry
3.1: Ingest tabular data from an external source to Foundry
Ingest tabular data from an external source to Foundry. Learning resources — Documentation: Tools: Data Connection; Data Connection Sources: Optimize JDBC Syncs. Reference Workflows: Connecting to AWS S3; Connecting to Postgres.
3.2: Ingest unstructured data from an external source to Foundry
Ingest unstructured data from an external source to Foundry. Learning resources — Documentation: Tools: Data Connection.
3.3: Identify general data connection capabilities useful for a given project
Identify general data connection capabilities useful for a given project. Learning resources — Documentation: Data Connection: Agent Setup, Direct Connections; Manual Data Upload, Fusion Dataset Syncs; Platform Interoperability.
4: Ontology Design and Development in Foundry
4.1: Design an ontology based on application requirements and available data
Design an ontology based on application requirements and available data. Learning resources — Documentation: Ontology.
4.2: Implement pipelines backing ontology objects and links
Implement pipelines backing ontology objects and links. Learning resources — Documentation: Object & Link Types. Self-service learning: Deep Dive: Creating your First Ontology.
4.3: Provide data engineering context useful for use case development
Provide data engineering context useful for use case development. Learning resources — Documentation: Solution Design. Self-service learning: Video: Foundry Reference Project Playlist.
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