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

Databricks Certified Data Engineer Professional Sample Questions:

1. A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?

A) import dlt
@dlt.table
def raw_customers():
return spark.read.format("parquet").load("s3://my-bucket/raw-customers/")
B) import dlt
@dlt.table
def raw_customers():
return spark.read.format("csv").load("s3://my-bucket/raw-customers/")
C) import dlt
@dlt.table
def raw_customers():
return spark.read.json("s3://my-bucket/raw-customers/")
D) import dlt
@dlt.view
def raw_customers():
return spark.format.json("s3://my-bucket/raw-customers/")

2. A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

A) Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
B) Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
C) Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"
D) Use Auto Loader in batch mode with a daily job to overwrite the Delta table.

3. The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A) The RDD Block Name included the '' annotation signaling failure to cache
B) On Heap Memory Usage is within 75% of off Heap Memory usage
C) Size on Disk is> 0
D) The number of Cached Partitions> the number of Spark Partitions
E) Size on Disk is < Size in Memory

4. What is the first line of a Databricks Python notebook when viewed in a text editor?

A) // Databricks notebook source
B) # MAGIC %python
C) %python
D) # Databricks notebook source
E) -- Databricks notebook source

5. A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

A) Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
B) Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
C) Periodically run OPTIMIZE table_name.
D) Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
E) Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.

Solutions: