시험패스에유효한Databricks-Certified-Professional-Data-Engineer인증시험덤프공부최신버전덤프샘풀문제다운

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

SectionWeightObjectives
Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Differences between data lake, data warehouse, and lakehouse
  • 2. Data governance fundamentals
  • 3. Bronze, silver, gold data layers
Delta Lake20-25%- Delta Lake fundamentals
  • 1. Time travel and data versioning
  • 2. Optimize and Z-order
  • 3. ACID transactions
- Delta Lake operations
  • 1. Schema evolution and enforcement
  • 2. Delta Live Tables
  • 3. Merge, update, delete operations
Data Ingestion15-20%- Streaming ingestion
  • 1. Kafka integration
  • 2. Structured streaming fundamentals
- Batch ingestion methods
  • 1. Integration with external systems
  • 2. Spark APIs for ingestion
  • 3. DBR autoloader
Data Processing with Spark25-30%- Python and SQL for data engineering
  • 1. Built-in and user-defined functions
  • 2. Spark APIs in Python
  • 3. Performance optimization techniques
- Spark DataFrames and Spark SQL
  • 1. Spark SQL queries and functions
  • 2. DataFrame operations and transformations
  • 3. Window functions
Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Jobs and job scheduling
  • 2. Monitoring and alerting
  • 3. Task dependencies and orchestration

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최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q110-Q115):

질문 # 110
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

정답：C

설명：
Explanation
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.


질문 # 111
At the end of the inventory process a file gets uploaded to the cloud object storage, you are asked to build a process to ingest data which of the following method can be used to ingest the data incrementally, the schema of the file is expected to change overtime ingestion process should be able to handle these changes automatically. Below is the auto loader command to load the data, fill in the blanks for successful execution of the below code.
1.spark.readStream
2..format("cloudfiles")
3..option("cloudfiles.format","csv)
4..option("_______", 'dbfs:/location/checkpoint/')
5..load(data_source)
6..writeStream
7..option("_______",' dbfs:/location/checkpoint/')
8..option("mergeSchema", "true")
9..table(table_name))

정답：E

설명：
Explanation
The answer is cloudfiles.schemalocation, checkpointlocation
When reading the data cloudfiles.schemalocation is used to store the inferred schema of the incoming data.
When writing a stream to recover from failures checkpointlocation is used to store the offset of the byte that was most recently processed.


질문 # 112
A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns . The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB.
How should the data engineer meet this requirement?

정답：B

설명：
The Databricks documentation describes Liquid Clustering as the recommended data layout optimization for evolving workloads. Unlike traditional partitioning or Z-ordering, Liquid Clustering dynamically maintains data organization without rewriting existing files when clustering keys change. It handles data skew automatically and supports flexible re-clustering based on query patterns. Partitioning and Z-ordering require full data rewrites whenever key structures change, making them expensive for tables with frequently evolving access patterns. For tables under a few terabytes, Liquid Clustering offers the best balance between scalability, adaptability, and maintenance efficiency. Thus, option C aligns with Databricks' best practice for modern adaptive layout optimization.


질문 # 113
The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

정답：D

설명：
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API.
However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database. References:
* Grant privileges on a database: https://docs.databricks.com/en/security/auth-authz/table-acls/grant- privileges-database.html
* Privileges you can grant on Hive metastore objects: https://docs.databricks.com/en/security/auth-authz
/table-acls/privileges.html


질문 # 114
You are currently asked to work on building a data pipeline, you have noticed that you are currently working on a very large scale ETL many data dependencies, which of the following tools can be used to address this problem?

정답：B

설명：
Explanation
The answer is, DELTA LIVE TABLES
DLT simplifies data dependencies by building DAG-based joins between live tables. Here is a view of how the dag looks with data dependencies without additional meta data,
1.create or replace live view customers
2.select * from customers;
3.
4.create or replace live view sales_orders_raw
5.select * from sales_orders;
6.
7.create or replace live view sales_orders_cleaned
8.as
9.select sales.* from
10.live.sales_orders_raw s
11. join live.customers c
12.on c.customer_id = s.customer_id
13.where c.city = 'LA';
14.
15.create or replace live table sales_orders_in_la
16.selects from sales_orders_cleaned;
Above code creates below dag

Documentation on DELTA LIVE TABLES,
https://databricks.com/product/delta-live-tables
https://databricks.com/blog/2022/04/05/announcing-generally-availability-of-databricks-delta-live-tables-dlt.htm DELTA LIVE TABLES, addresses below challenges when building ETL processes
1.Complexities of large scale ETL
a.Hard to build and maintain dependencies
b.Difficult to switch between batch and stream
2.Data quality and governance
a.Difficult to monitor and enforce data quality
b.Impossible to trace data lineage
3.Difficult pipeline operations
a.Poor observability at granular data level
b.Error handling and recovery is laborious


질문 # 115
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