Exam Name: Databricks Certified Data Engineer Associate Exam
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Associate Exam |
| Exam Number: | DE-A |
| Real Exam Qty: | 45 scored multiple-choice questions |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Exam Price: | USD 200 |
| Related Certifications: | Databricks Certified Data Engineer Professional |
| Exam Format: | Proctored online or test center, Scenario-based questions, Multiple-choice |
| Passing Score: | Approximately ~70% (not officially fixed/publicly guaranteed) |
| Exam Duration: | 90 minutes |
| Recommended Training: | Official Data Engineering with Databricks Course Databricks Data Engineering Learning Path (Databricks Academy) |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored exam or test center |
| Pre Condition: | No formal prerequisites required; recommended ~6 months hands-on experience with Databricks, Spark SQL, and PySpark. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-associate |
Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Productionizing Data Pipelines | - Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs - Pipeline deployment and operationalization |
| Databricks Lakehouse Platform Fundamentals | - Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts |
| Data Ingestion and ELT Development | - Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations |
| Data Governance and Quality | - Unity Catalog basics - Data quality concepts and management - Data access control and governance |
| Data Processing and Transformations | - Apache Spark SQL operations (joins, aggregations, filtering) - User-defined functions (UDFs) - PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization) |
Databricks Certified Data Engineer Associate Essentials: What Candidates Ask
- Databricks Lakehouse Platform Fundamentals ()
- Data Governance and Quality ()
- Data Ingestion and ELT Development ()
Databricks Certified Data Engineer Associate Sample Questions:
A data engineering team needs to incrementally ingest customer transactions from a SaaS app into Data Intelligence Platform with:
- Built-in change data capture (including updates and deletes)
- Automatic schema evolution
- Serverless execution with retries and minimal maintenance
- OAuth support and basic monitoring
Which solution meets all of these requirements?
- A. Schedule batch jobs per region that call SaaS REST APIs and write directly to silver tables with custom CDC and schema evolution, and use system tables for ad hoc basic monitoring.
- B. Use a Lakeflow Connect standard connector, implement MERGE-based CDC, schema evolution, and retries in workflows, and set up a dedicated monitoring dashboard.
- C. Use a Lakeflow Connect managed connector for the SaaS app with built-in CDC, schema evolution, OAuth, and serverless compute with retries, then process downstream with Lakeflow Spark Declarative Pipelines.
- D. Use Auto Loader on exported SaaS files and an SDP that calls the SaaS APIs, handling CDC, schema changes, OAuth, and retries in your own code.
Correct Answer: C 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository.
A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?
- A. Clone
- B. Commit
- C. Pull
- D. Merge
- E. Push
Correct Answer: C 🗳️
A data engineer wants to create an external table in Databricks that references data stored in an Azure Data Lake Storage (ADLS) location. The goal is to enable Databricks to access and query this external data without moving it into the Databricks-managed storage. Which step should the data engineer take to successfully create the external table?
- A. Use the CREATE MANAGED TABLE statement and specify the LOCATION clause with the path to the external data.
- B. CREATE EXTERNAL TABLE statement without specifying a LOCATION clause.
- C. CREATE UNMANAGED TABLE statement without specifying a LOCATION clause.
- D. Use the CREATE TABLE statement and specify the LOCATION clause with the path to the external data.
Correct Answer: D 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
A data engineer uses COPY INTO to load CSV files from an external location into a Delta table.
The job is accidentally triggered twice within the same hour.
What is the expected outcome?
- A. The target table is overwritten with only the newest file.
- B. The second run loads only files that were not already ingested, because COPY INTO tracks loaded files idempotently.
- C. Records are duplicated because COPY INTO always reloads all files in the source path.
- D. The second run fails with a duplicate-file error and rolls back the first run.
Correct Answer: B 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
Which of the following is stored in the Databricks customer's cloud account?
- A. Repos
- B. Cluster management metadata
- C. Notebooks
- D. Data
- E. Databricks web application
Correct Answer: D 🗳️
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