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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Spark SQL and DataFrames	15-20%	- Aggregate and group data - Join and union DataFrames - Write and execute Spark SQL queries - Handle null values and data quality
Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Implement user-defined functions (UDFs) - Use PySpark for data processing
Lakehouse Platform Concepts	10-15%	- Understand the Lakehouse architecture and its benefits - Explain data governance and security concepts - Describe key Databricks Lakehouse platform components
Apache Spark Data Processing Fundamentals	20-25%	- Use Spark SQL for data processing - Create and use Spark DataFrames - Apply transformations and actions on DataFrames - Work with structured data types (arrays, maps, structs)
Delta Lake Fundamentals	20-25%	- Write to and read from Delta tables - Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Create and manage Delta tables
Data Pipeline Architecture	15-20%	- Implement incremental data processing - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns

>> Databricks-Certified-Data-Engineer-Associate Fragenkatalog <<

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Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q279-Q284):

279. Frage
A data engineer is cleaning a Bronze table that receives the same customer records from multiple source systems. Duplicate rows have the same customer_id and email values but different ingestion_timestamp values. The Silver table should contain only one record for each unique combination of customer_id and email.
Which PySpark operation correctly deduplicates the records based on the business keys?

A. df.dropDuplicates(["customer_id", "email"])
B. df.groupBy("customer_id", "email").agg(max("ingestion_timestamp").alias("latest_ts"))
C. df.distinct()
D. df.select("customer_id", "email").distinct()

Antwort: A

280. Frage
A data engineering team ingests customer transaction data from three enterprise sources (SQL database, S3, and Kafka stream) into Unity Catalog tables. The data must maintain lineage and support compliance audits requiring historical snapshots. Which Lakeflow Connect configuration satisfies both the governance and audit requirements?

A. Separate connectors per source with CDC, individual Unity Catalog schemas per source, and table versioning enabled for audit compliance.
B. Single connector with batch scheduling, shared Unity Catalog schema, and incremental snapshots stored externally.
C. Single Lakeflow connector with all three sources, direct Delta writes, and disabled table versioning to reduce storage costs.
D. Separate connectors with full-load sync only, archival of historical data, and external tables for audit queries.

Antwort: A

Begründung:
Separate source-specific connectors support incremental ingestion or CDC into Unity Catalog- managed Delta tables, preserving governance and lineage. Retaining table versions/history enables historical audit queries; a single connector cannot combine unrelated SQL, S3, and Kafka sources.

281. Frage
A data engineer is transforming a bronze table containing API response data into a silver table.
The bronze table has a user_profile column of type STRING that contains JSON data. An example value is:

The silver table needs to make this data easily queryable for analytics without requiring JSON parsing in every downstream query.
Which approach standardizes this column for the silver table?

Antwort: D

Begründung:
It uses valid JSONPath expressions ($.field) to extract each attribute into a separate silver-table column and casts age to INT, making the data typed and directly queryable. The other options use invalid paths, treat a string as a struct, or retain an inadequately typed nested structure.

282. Frage
A data engineer is running a Spark job that processes data stored in Delta format. The engineer wants to reduce the number of files by compacting smaller files into larger ones to improve query performance. Which Delta Lake command performs this operation?

A. MERGE
B. DELETE
C. OPTIMIZE
D. VACUUM

Antwort: C

283. Frage
A data engineer is developing a small proof of concept in a notebook. When running the entire notebook, cluster usage spikes. The data engineer wants to keep the development experience and get real-time results.
Which cluster meets these requirements?

A. Job Cluster with Photon enabled and autoscaling
B. All-Purpose Cluster with autoscaling
C. All-Purpose Cluster with a large fixed memory size
D. Job Cluster with autoscaling enabled

Antwort: B

Begründung:
For interactive notebook development, Databricks distinguishes between all-purpose compute and job compute. All-purpose compute is designed for analysis, notebook development, and interactive workloads, while job compute is intended for automated scheduled or triggered jobs. Because the engineer is actively developing a proof of concept in a notebook and wants real-time feedback, an all-purpose cluster is the right compute type. Adding autoscaling makes it better suited to the observed usage spikes, because the cluster can expand when notebook execution demands more resources and scale down afterward, reducing waste compared with a large fixed-size cluster. That makes option B the best answer. Option A keeps the interactive development model, but the fixed large size is less efficient. Options C and D use job clusters, which are better aligned with production jobs rather than iterative notebook-based development. Databricks documentation consistently positions all-purpose compute for collaborative and interactive development, and autoscaling is a standard mechanism to handle variable workloads more efficiently.
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284. Frage
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