Free Practice Questions for Palantir Foundry Data Engineer Certification Certification
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)
Domain 1: DATA PIPELINE DEVELOPMENT IN FOUNDRY
Subdomain 1.1: Process tabular data in Transforms
Can you develop transforms on structured (tabular) and unstructured datasets in Foundry? Do you know how to follow the recommended best practices when building data pipelines?
Subdomain 1.2: Process unstructured data in Transforms
Can you develop transforms on structured (tabular) and unstructured datasets in Foundry? Do you know how to follow the recommended best practices when building data pipelines?
Subdomain 1.3: Configure pipeline for production use
Can you develop transforms on structured (tabular) and unstructured datasets in Foundry? Do you know how to follow the recommended best practices when building data pipelines?
Subdomain 1.4: Apply general best practices during pipeline development
Can you develop transforms on structured (tabular) and unstructured datasets in Foundry? Do you know how to follow the recommended best practices when building data pipelines?
Domain 2: DATA PIPELINE MAINTENANCE IN FOUNDRY
Subdomain 2.1: Debug an issue in a production pipeline
Do you know how to effectively investigate and fix common issues in data pipelines? Can you contribute logic changes and performance improvements to transform pipelines feeding mission-critical workflows? Are you familiar with recommended support structures?
Subdomain 2.2: Contribute changes to a production pipeline
Do you know how to effectively investigate and fix common issues in data pipelines? Can you contribute logic changes and performance improvements to transform pipelines feeding mission-critical workflows? Are you familiar with recommended support structures?
Subdomain 2.3: Set up support structure for production pipeline
Do you know how to effectively investigate and fix common issues in data pipelines? Can you contribute logic changes and performance improvements to transform pipelines feeding mission-critical workflows? Are you familiar with recommended support structures?
Subdomain 2.4: Improve performance of production pipeline
Do you know how to effectively investigate and fix common issues in data pipelines? Can you contribute logic changes and performance improvements to transform pipelines feeding mission-critical workflows? Are you familiar with recommended support structures?
Domain 3: DATA CONNECTION AND INTEGRATION IN FOUNDRY
Subdomain 3.1: Ingest tabular data from an external source to Foundry
Are you familiar with architecture and capabilities of Data Connection? Can you set up sources and syncs ingesting tabular data or raw files from external systems to Foundry?
Subdomain 3.2: Ingest unstructured data from an external source to Foundry
Are you familiar with architecture and capabilities of Data Connection? Can you set up sources and syncs ingesting tabular data or raw files from external systems to Foundry?
Subdomain 3.3: Identify general data connection capabilities useful for a given project
Are you familiar with architecture and capabilities of Data Connection? Can you set up sources and syncs ingesting tabular data or raw files from external systems to Foundry?
Domain 4: ONTOLOGY DESIGN AND DEVELOPMENT IN FOUNDRY
Subdomain 4.1: Design an ontology based on application requirements and available data
Can you provide data engineering context during ontology design and implement pipelines backing ontology objects and links based on application requirements?
Subdomain 4.2: Implement pipelines backing ontology objects and links
Can you provide data engineering context during ontology design and implement pipelines backing ontology objects and links based on application requirements?
Subdomain 4.3: Provide data engineering context useful for use case development
Can you provide data engineering context during ontology design and implement pipelines backing ontology objects and links based on application requirements?
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