Databricks - Databricks-Certified-Professional-Data-Engineer - Test Databricks Certified Professional Data Engineer Exam Collection Pdf

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

Section	Weight	Objectives
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Jobs and job scheduling 3. Task dependencies and orchestration
Data Ingestion	15-20%	- Batch ingestion methods 1. DBR autoloader 2. Integration with external systems 3. Spark APIs for ingestion - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Differences between data lake, data warehouse, and lakehouse 3. Data governance fundamentals
Delta Lake	20-25%	- Delta Lake operations 1. Merge, update, delete operations 2. Delta Live Tables 3. Schema evolution and enforcement - Delta Lake fundamentals 1. Time travel and data versioning 2. Optimize and Z-order 3. ACID transactions
Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Built-in and user-defined functions 2. Performance optimization techniques 3. Spark APIs in Python - Spark DataFrames and Spark SQL 1. Window functions 2. DataFrame operations and transformations 3. Spark SQL queries and functions

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q126-Q131):

NEW QUESTION # 126
A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security ?

A. Use a service principal with OAuth token federation.
B. Use a shared user account and its OAuth client secret.
C. Use a service principal and its Personal Access Token.
D. Use a service principal ID and its OAuth client secret.

Answer: A

Explanation:
The most secure and enterprise-recommended authentication method for Databricks automation is OAuth token federation with service principals .
This configuration allows service principals (non-human identities) to authenticate using temporary OAuth access tokens from a trusted identity provider (such as Azure AD or AWS IAM federation). These tokens are short-lived and scoped, significantly reducing credential exposure risks.
By contrast, static client secrets (B) or PATs (C) are long-lived and require periodic manual rotation, increasing security vulnerability. Shared user accounts (D) violate least-privilege and auditability principles.
Therefore, A provides the strongest, most compliant authentication model for automated CLI and CI/CD workflows.

NEW QUESTION # 127
What statement is true regarding the retention of job run history?

A. It is retained until you export or delete job run logs
B. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
C. It is retained for 60 days, after which logs are archived
D. t is retained for 60 days, during which you can export notebook run results to HTML
E. It is retained for 90 days or until the run-id is re-used through custom run configuration

Answer: D

NEW QUESTION # 128
Your team has hundreds of jobs running but it is difficult to track cost of each job run, you are asked to provide a recommendation on how to monitor and track cost across various workloads

A. Use Tags, during job creation so cost can be easily tracked
B. Use a single cluster for all the jobs, so cost can be easily tracked
C. Use workspace admin reporting
D. Create jobs in different workspaces, so we can track the cost easily
E. Use job logs to monitor and track the costs

Answer: A

Explanation:
Explanation
The answer is Use Tags, during job creation so cost can be easily tracked Review below link for more details
https://docs.databricks.com/administration-guide/account-settings/usage-detail-tags-aws.html Here is a view how tags get propagated from pools to clusters and clusters without pools, Diagram Description automatically generated

NEW QUESTION # 129
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 overall size of data in the table
B. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
C. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
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 # 130
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 2)

A. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
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. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.

Answer: C,E

Explanation:
Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.
Reference Source: Databricks Delta Lake Guide - "Use Liquid Clustering for Tables" and "OPTIMIZE Command for File Compaction and Data Layout."
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NEW QUESTION # 131
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