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

Section	Weight	Objectives
Databricks Lakehouse Platform	24%	- Unity Catalog - Lakehouse Architecture - Delta Lake - Data Management
Data Processing	28%	- ETL Pipelines - Data Transformation - Spark SQL - Structured Streaming
Data Quality and Governance	12%	- Data Lineage - Data Quality - Governance
Data Modeling and Storage	20%	- Storage Optimization - Data Modeling - File Formats
Monitoring and Troubleshooting	16%	- Performance Optimization - Monitoring - Troubleshooting

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q210-Q215):

質問 # 210
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

A. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
B. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
C. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
E. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.

正解：C

質問 # 211
The data science team has created and logged a production model 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, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

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

A. df.map(lambda x:model(x[columns])).select("customer_id, predictions")
B. model.predict(df, columns)
C. df.select("customer_id", model(*columns).alias("predictions"))
D. df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
E. df.apply(model, columns).select("customer_id, predictions")

正解：C

解説：
This code block applies the Spark UDF created from the MLflow model to the DataFrame df by selecting the existing customer_id column and the new column produced by the model, which is aliased to predictions. The model(*columns) part is where the UDF is applied to the columns specified in the columns list, and alias("predictions") is used to name the output column of the model's predictions. This will result in a DataFrame with the desired schema: "customer_id LONG, predictions DOUBLE".

質問 # 212
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

A. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
B. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
C. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
D. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.

正解：D

解説：
Databricks documentation explains that Lakeflow Declarative Pipelines build upon Structured Streaming but add higher-level orchestration and automation capabilities. They automatically manage dependencies, materialization, and recovery across multi-stage data flows without requiring external orchestration tools such as Airflow or Azure Data Factory. In contrast, Structured Streaming operates at a lower level, where developers must manually handle orchestration, retries, and dependencies between streaming jobs. Both support Delta Lake outputs and schema evolution; however, Lakeflow Declarative Pipelines simplify management by declaratively defining transformations and data quality expectations. Hence, the correct distinction is A -- automated orchestration and management in Lakeflow Declarative Pipelines.

質問 # 213
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.
Original query:
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Proposed query:

Proposed query:
.start("/item_agg")
Which step must also be completed to put the proposed query into production?

A. Remove .option (mergeSchema', true') from the streaming write
B. Run REFRESH TABLE delta, /item_agg'
C. Specify a new checkpointlocation
D. Increase the shuffle partitions to account for additional aggregates
E. Register the data in the "/item_agg" directory to the Hive metastore

正解：C

解説：
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.

質問 # 214
A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?

A. Define a list variable within a Notebook to loop through the report types to download them, and print the download results. Execute it as a Notebook tasks.
B. Use a foreach task with a list of report types as its inputs.
C. Use a Delta Lake table to track each report download status as 10 rows, and use it as a source table to execute the download function as a Pandas UDF.
D. Define ten Notebook tasks to clearly track which report download failed.

正解：B

解説：
A foreach task allows the job to dynamically iterate over a configurable list of report types, execute downloads in parallel, and track the success or failure of each item independently. This enables scalable throughput, partial retries for failed downloads, and easy updates when the list of report types changes, without hardcoding tasks or introducing unnecessary complexity.

質問 # 215
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