Databricks - Databricks-Certified-Professional-Data-Engineer - Databricks Certified Professional Data Engineer Exam–Updated Reliable Test Guide

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

Section	Weight	Objectives
Data Governance	7%	- Policy enforcement - Unity Catalog management - Data lineage and metadata tracking
Data Transformation, Cleansing, and Quality	10%	- Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data
Cost & Performance Optimisation	13%	- Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching
Data Sharing and Federation	5%	- Unity Catalog data sharing - Cross-workspace and cross-cloud access
Data Modelling	6%	- Medallion Architecture implementation - Schema design and management - Delta Lake table design
Monitoring and Alerting	10%	- Pipeline observability and logging - Performance and health monitoring - Setting up alerts and notifications
Debugging and Deploying	10%	- Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices
Developing Code for Data Processing using Python and SQL	22%	- Batch and incremental processing logic - Integration with Databricks APIs and tools - Data transformation and aggregation
Data Ingestion & Acquisition	7%	- Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion
Ensuring Data Security and Compliance	10%	- Data encryption and masking - Access control and permissions - Compliance standards implementation

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q127-Q132):

NEW QUESTION # 127
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
B. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
D. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

Answer: D

Explanation:
Explanation
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section.https://docs.databricks.com/en/delta/tune-file-size.html#autotune-table 'Autotune file size based on workload'

NEW QUESTION # 128
Which of the following commands results in the successful creation of a view on top of the delta stream(stream on delta table)?

A. Spark.read.format("delta").table("sales").trigger("stream").createOrReplaceTempView("streaming_vw")
B. Spark.readStream.format("delta").table("sales").createOrReplaceTempView("streaming_vw")
C. You can not create a view on streaming data source.
D. Spark.read.format("delta").table("sales").mode("stream").createOrReplaceTempView("streaming_vw")
E. Spark.read.format("delta").table("sales").createOrReplaceTempView("streaming_vw")
F. Spark.read.format("delta").stream("sales").createOrReplaceTempView("streaming_vw")

Answer: B

Explanation:
Explanation
The answer is
Spark.readStream.table("sales").createOrReplaceTempView("streaming_vw") When you load a Delta table as a stream source and use it in a streaming query, the query processes all of the data present in the table as well as any new data that arrives after the stream is started.
You can load both paths and tables as a stream, you also have the ability to ignore deletes and changes(updates, Merge, overwrites) on the delta table.
Here is more information,
https://docs.databricks.com/delta/delta-streaming.html#delta-table-as-a-source

NEW QUESTION # 129
You were asked to write python code to stop all running streams, which of the following command can be used to get a list of all active streams currently running so we can stop them, fill in the blank.
1.for s in _______________:
2. s.stop()

A. getActiveStreams()
B. Spark.getActiveStreams()
C. activeStreams()
D. spark.streams.getActive
E. spark.streams.active

Answer: E

NEW QUESTION # 130
Which of the following techniques structured streaming uses to ensure recovery of failures during stream processing?

A. Checkpointing and write-ahead logging
B. Write ahead logging and watermarking
C. Checkpointing and Watermarking
D. Checkpointing and Idempotent sinks
E. Delta time travel
F. The stream will failover to available nodes in the cluster

Answer: A

Explanation:
Explanation
The answer is Checkpointing and write-ahead logging.
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.

NEW QUESTION # 131
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
B. Both commands will fail. No new variables, tables, or views will be created.
C. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
D. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
E. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.

Answer: A

Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified Reference: [Databricks Certified Data Engineer Professional], under "Language Interoperability" section; Databricks Documentation, under "Mix languages" section.

NEW QUESTION # 132
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