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

Section	Weight	Objectives
Topic 1: Delta Lake	20-25%	- Delta Lake fundamentals 1. Time travel and data versioning 2. Optimize and Z-order 3. ACID transactions - Delta Lake operations 1. Delta Live Tables 2. Schema evolution and enforcement 3. Merge, update, delete operations
Topic 2: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Task dependencies and orchestration 2. Monitoring and alerting 3. Jobs and job scheduling
Topic 3: Data Ingestion	15-20%	- Batch ingestion methods 1. Integration with external systems 2. Spark APIs for ingestion 3. DBR autoloader - Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration
Topic 4: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Data governance fundamentals 2. Differences between data lake, data warehouse, and lakehouse 3. Bronze, silver, gold data layers
Topic 5: Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. Window functions 2. DataFrame operations and transformations 3. Spark SQL queries and functions - Python and SQL for data engineering 1. Spark APIs in Python 2. Performance optimization techniques 3. Built-in and user-defined functions

>> Databricks-Certified-Professional-Data-Engineer基礎問題集 <<

試験の準備方法-信頼的なDatabricks-Certified-Professional-Data-Engineer基礎問題集試験-一番優秀なDatabricks-Certified-Professional-Data-Engineer受験方法

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q27-Q32):

質問 # 27
A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
B. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
C. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
D. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
E. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

正解：E

解説：
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema: user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column. When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Data skipping" section.

質問 # 28
Which of the following table constraints that can be enforced on Delta lake tables are supported?

A. Unique, Not Null, Check Constraints
B. Default, Not Null, Check Constraints
C. Primary key, Not Null, Check Constraints
D. Primary key, foreign key, Not Null, Check Constraints
E. Not Null, Check Constraints

正解：E

解説：
Explanation
The answer is Not Null, Check Constraints
https://docs.microsoft.com/en-us/azure/databricks/delta/delta-constraints
* CREATE TABLE events( id LONG,
* date STRING,
* location STRING,
* description STRING
* ) USING DELTA;
ALTER TABLE events CHANGE COLUMN id SET NOT NULL;
ALTER TABLE events ADD CONSTRAINT dateWithinRange CHECK (date > '1900-01-01'); Note: Databricks as of DBR 11.1 added support for Primary Key and Foreign Key when Unity Catalog is enabled but this is for information purposes only these are not actually enforced. You may ask then why are we defining these if they are not enforced, so especially these information constraints are very helpful if you have a BI tool that can benefit from knowing the relationship between the tables, so it will be easy when creating reports/dashboards or understanding the data model when using any Data modeling tool.
Primary and Foreign Key
Graphical user interface, text, application, email Description automatically generated

質問 # 29
You are trying to create an object by joining two tables that and it is accessible to data scientist's team, so it does not get dropped if the cluster restarts or if the notebook is detached. What type of object are you trying to create?

A. External view
B. View
C. Global Temporary view
D. Temporary view
E. Global Temporary view with cache option

正解：B

解説：
Explanation
Answer is View, A view can be used to join multiple tables but also persist into meta stores so others can accesses it

質問 # 30
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Skew caused by more data being assigned to a subset of spark-partitions.
B. Spill resulting from attached volume storage being too small.
C. Credential validation errors while pulling data from an external system.
D. Task queueing resulting from improper thread pool assignment.
E. Network latency due to some cluster nodes being in different regions from the source data

正解：A

解説：
Explanation
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.

質問 # 31
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 to reduce duration of MERGE operations
C. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
D. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction

正解：B

解説：
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 Reference: [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'

質問 # 32
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