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

SectionWeightObjectives
Topic 1: Delta Lake20-25%- 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
Topic 2: Data Processing with Spark25-30%- Spark DataFrames and Spark SQL
  • 1. Window functions
  • 2. Spark SQL queries and functions
  • 3. DataFrame operations and transformations
- Python and SQL for data engineering
  • 1. Spark APIs in Python
  • 2. Performance optimization techniques
  • 3. Built-in and user-defined functions
Topic 3: Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Data governance fundamentals
  • 2. Differences between data lake, data warehouse, and lakehouse
  • 3. Bronze, silver, gold data layers
Topic 4: 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
Topic 5: Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Task dependencies and orchestration
  • 2. Monitoring and alerting
  • 3. Jobs and job scheduling

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q201-Q206):

質問 # 201
A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions. The requirements are:
* Grant the data-engineers group CAN_MANAGE access to the job.
* Ensure the auditors' group can view the job but not modify/run it.
* Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?

正解：D

解説：
Databricks Asset Bundles (DABs) allow jobs, clusters, and permissions to be defined as code in YAML configuration files. According to the Databricks documentation on job permissions and bundle deployment, when defining permissions within a job resource, they must be scoped directly under that specific job's definition. This ensures that permissions are applied only to the intended job resource and not inadvertently propagated to other jobs or resources.
In this scenario, the data engineer must grant the data-engineers group CAN_MANAGE access, allowing them to configure, edit, and manage the job, while the auditors group should only have CAN_VIEW, giving them read-only access to see configurations and results without the ability to modify or execute. Importantly, no additional groups should be granted permissions, in order to follow the principle of least privilege.
Options A and B introduce unnecessary or unintended groups (like admin-team in A) or define permissions outside of the job scope (as in B). Option C improperly separates the permissions block outside the job resource, which is not aligned with Databricks bundle best practices.
Option D is the correct approach because it defines the job resource my-job with its name, tasks, clusters, and the exact intended permissions (CAN_MANAGE for data-engineers and CAN_VIEW for auditors). This aligns with Databricks' principle of least privilege and ensures compliance with governance standards in Unity Catalog-enabled workspaces.
Reference: Databricks Asset Bundles documentation - Managing Jobs and Permissions


質問 # 202
Drop the customers database and associated tables and data, all of the tables inside the database are managed tables. Which of the following SQL commands will help you accomplish this?

正解：D

解説：
Explanation
The answer is DROP DATABASE customers CASCADE
Drop database with cascade option drops all the tables, since all of the tables inside the database are managed tables we do not need to perform any additional steps to clean the data in the storage.


質問 # 203
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

正解：D

解説：
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.
Reference:
Databricks documentation on DLT pipelines: https://docs.databricks.com/data-engineering/delta-live-tables/delta-live-tables-overview.html


質問 # 204
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 # 205
The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

Which code block will output DataFrame with the schema ' ' customer_id LONG, predictions DOUBLE ' ' ?

正解：B

解説：
Given the information that the model is registered with MLflow and assuming predict is the method used to apply the model to a set of columns, we use the model.predict() function to apply the model to the DataFrame df using the specified columns . The model.predict() function is designed to take in a DataFrame and a list of column names as arguments, applying the trained model to these features to produce a predictions column. When working with PySpark, this predictions column needs to be selected alongside the customer_id to create a new DataFrame with the schema customer_id LONG, predictions DOUBLE .
References:
MLflow documentation on using Python function models: https://www.mlflow.org/docs/latest/models.
html#python-function-python
PySpark MLlib documentation on model prediction: https://spark.apache.org/docs/latest/ml-pipeline.
html#pipeline


質問 # 206
......

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