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

Section	Objectives
Data Modeling and Transformation	- Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques
Production Pipelines and Orchestration	- Error handling and recovery strategies - Databricks Workflows - Job scheduling and monitoring
Delta Lake and Data Management	- Delta Lake transactions and ACID properties - Time travel and versioning - Schema evolution and enforcement
Data Ingestion and Processing	- Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns
Databricks Lakehouse Platform Architecture	- Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold)

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q111-Q116):

NEW QUESTION # 111
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A. date = dbutils.notebooks.getParam("date")
B. input_dict = input()
date= input_dict["date"]
C. date = spark.conf.get("date")
D. import sys
date = sys.argv[1]
E. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")

Answer: E

Explanation:
The code block that should be used to create the date Python variable used in the above code block is:
dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date") This code block uses the dbutils.widgets API to create and get a text widget named "date" that can accept a string value as a parameter. The default value of the widget is "null", which means that if no parameter is passed, the date variable will be "null". However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter.
For example, if the parameter is "2021-11-01", the date variable will be "2021-11-01". This way, the notebook can use the date variable to load data from the specified path.

NEW QUESTION # 112
Which of the following is true of Delta Lake and the Lakehouse?

A. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
B. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
C. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
D. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
E. Z-order can only be applied to numeric values stored in Delta Lake tables

Answer: C

Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.

NEW QUESTION # 113
A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?

A. A separate data synchronization service must be deployed.
B. Federation creates local copies that must be manually refreshed.
C. Federation provides read-only access that reflects the current state of source systems.
D. Federation implements change data capture (CDC) from all sources.

Answer: C

Explanation:
Lakehouse Federation allows Databricks to query and manage external data sources through a single governance layer, without moving or copying data. The documentation specifies that
"Federated queries provide read-only access to data, reflecting the current state of the underlying source system." This ensures consistency across teams since all users access the same source of truth directly from the external system through Unity Catalog. Federation does not perform CDC replication or local caching; it queries live data on demand. Hence, option A accurately represents how Lakehouse Federation maintains consistency across federated sources.

NEW QUESTION # 114
A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

A. streamingDF.join(userLookup, ["user_id"], how="outer")
B. userLookup.join(streamingDF, ["user_id"], how="right")
C. streamingDF.join(userLookup, ["userid"], how="inner")
D. userLookup.join(streamingDF, ["userid"], how="inner")
E. streamingDF.join(userLookup, ["user_id"], how="left")

Answer: A

Explanation:
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#support- matrix-for-joins-in-streaming-queries

NEW QUESTION # 115
A table is registered with the following code:
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Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
E. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.

Answer: A

Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.

NEW QUESTION # 116
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