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

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

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最新的 Databricks Certification Databricks-Certified-Professional-Data-Engineer 免費考試真題 (Q13-Q18):

問題 #13
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
B. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
C. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
D. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
E. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.

答案：C

解題說明：
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

問題 #14
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
C. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
D. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
E. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.

答案：B

解題說明：
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:
* Constraints : https://docs.delta.io/latest/delta-constraints.html
* ACID Transactions : https://docs.delta.io/latest/delta-intro.html#acid-transactions

問題 #15
A table named user_ltv is being used to create a view that will be used by data analysis on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:

An analyze who is not a member of the auditing group executing the following query:

Which result will be returned by this query?

A. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
B. All records from all columns will be displayed with the values in user_ltv.
C. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
D. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.

答案：C

解題說明：
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

問題 #16
Which approach demonstrates a modular and testable way to use DataFrame.transform for ETL code in PySpark?

A. def transform_data(input_df):
# transformation logic here
return output_df
test_input = spark.createDataFrame([(1, "a")], ["id", "value"])
assertDataFrameEqual(transform_data(test_input), expected)
B. class Pipeline:
def transform(self, df):
return df.withColumn("value_upper", upper(col("value")))
pipeline = Pipeline()
assertDataFrameEqual(pipeline.transform(test_input), expected)
C. def upper_transform(df):
return df.withColumn("value_upper", upper(col("value")))
actual = test_input.transform(upper_transform)
assertDataFrameEqual(actual, expected)
D. def upper_value(df):
return df.withColumn("value_upper", upper(col("value")))
def filter_positive(df):
return df.filter(df["id"] > 0)
pipeline_df = df.transform(upper_value).transform(filter_positive)

答案：D

解題說明：
Comprehensive and Detailed
Databricks and Apache Spark recommend building modular and reusable ETL transformations by leveraging the DataFrame.transform() API. This method allows you to chain multiple transformation functions in a clean and testable way.
Option A: Encapsulating the logic in a class (Pipeline) works, but it reduces modularity and flexibility. It does not show the true intended use of DataFrame.transform() which is chaining functional transformations.
Option B: This is the correct approach. It defines small, reusable functions (upper_value, filter_positive) that each take a DataFrame and return a transformed DataFrame. By chaining them with df.transform(func), you can compose ETL pipelines in a clear and declarative manner. This enables unit testing of individual functions and makes the ETL pipeline modular, testable, and production-ready.
Option C: This shows a single transformation wrapped in a function and tested, but it lacks pipeline composition - it is not demonstrating modular chaining across multiple transformations.
Option D: This simply defines a transformation function with hardcoded logic. It does not leverage DataFrame.transform() nor demonstrate modularity through composition.
Therefore, Option B is the best demonstration of how to use DataFrame.transform() in PySpark ETL pipelines.
Databricks documentation explicitly highlights that DataFrame.transform() allows developers to "chain together reusable functions in a readable and modular way, improving testability and maintainability of ETL code." This makes B the correct and officially supported pattern.

問題 #17
Which of the following is true of Delta Lake and the Lakehouse?

A. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
B. Z-order can only be applied to numeric values stored in Delta Lake tables
C. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
D. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
E. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.

答案：E

解題說明：
https://docs.delta.io/2.0.0/table-properties.html
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters 1 . Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer 1 . By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan 1 . This can significantly improve the query performance and reduce the I/O cost.
The other options are false because:
* Parquet compresses data column by column, not row by row 2 . This allows for better compression ratios, especially for repeated or similar values within a column 2 .
* Views in the Lakehouse do not maintain a valid cache of the most recent versions of source tables at all times 3 . Views are logical constructs that are defined by a SQL query on one or more base tables 3
. Views are not materialized by default, which means they do not store any data, but only the query definition 3 . Therefore, views always reflect the latest state of the source tables when queried 3 .
However, views can be cached manually using the CACHE TABLE or CREATE TABLE AS SELECT commands.
* Primary and foreign key constraints can not be leveraged to ensure duplicate values are never entered into a dimension table. Delta Lake does not support enforcing primary and foreign key constraints on tables. Constraints are logical rules that define the integrity and validity of the data in a table. Delta Lake relies on the application logic or the user to ensure the data quality and consistency.
* Z-order can be applied to any values stored in Delta Lake tables, not only numeric values. Z-order is a technique to optimize the layout of the data files by sorting them on one or more columns. Z-order can improve the query performance by clustering related values together and enabling more efficient data skipping. Z-order can be applied to any column that has a defined ordering, such as numeric, string, date, or boolean values.
References: Data Skipping , Parquet Format , Views , [Caching], [Constraints] , [Z-Ordering]

問題 #18
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