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

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

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q39-Q44):

質問 # 39
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

正解：C

解説：
https://docs.databricks.com/en/delta/delta-change-data-feed.html


質問 # 40
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

正解：B

解説：
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.
Reference:
Software Testing Fundamentals: Unit Testing


質問 # 41
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

正解：B

解説：
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
* Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned.
* Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
* Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.
:
Delta Lake Documentation on Table Partitioning: Optimizing Layout with Partitioning


質問 # 42
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFramedf. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFramedfhas the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

正解：E

解説：
Explanation
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five-minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window.
The alias function is used to rename the struct column as "time". Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under
"WINDOW" section.


質問 # 43
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, usingdisplay()calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

正解：A

解説：
This is the correct answer because it explains which of the following adjustments will get a more accurate measure of how code is likely to perform in production. The adjustment is that calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. When developing code in Databricks notebooks, one should be aware of how Spark handles transformations and actions. Transformations are operations that create a new DataFrame or Dataset from an existing one, such as filter, select, or join. Actions are operations that trigger a computation on a DataFrame or Dataset and return a result to the driver program or write it to storage, such as count, show, or save. Calling display() on a DataFrame or Dataset is also an action that triggers a computation and displays the result in a notebook cell. Spark uses lazy evaluation for transformations, which means that they are not executed until an action is called. Spark also uses caching to store intermediate results in memory or disk for faster access in subsequent actions. Therefore, calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. To get a more accurate measure of how code is likely to perform in production, one should avoid calling display() too often or clear the cache before running each cell. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Lazy evaluation" section; Databricks Documentation, under "Caching" section.


質問 # 44
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