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| Section | Objectives |
|---|---|
| Security, Governance, Monitoring, and Optimization | - Monitor and optimize Spark workloads - Cost optimization and performance tuning - Implement Unity Catalog governance and access control |
| Data Modeling and Storage | - Design scalable data lakehouse architectures - Delta Lake table design and optimization - Schema evolution and data partitioning strategies |
| Data Ingestion and Transformation | - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines - Ingest data using Apache Spark and Databricks |
| Production Pipelines and Orchestration | - Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling |
>> Databricks Databricks-Certified-Professional-Data-Engineer入門知識 <<
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質問 # 168
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:
A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?
正解：D
解説：
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint
質問 # 169
One of the queries in the Databricks SQL Dashboard takes a long time to refresh, which of the be-low steps can be taken to identify the root cause of this issue?
正解：A
解説：
Explanation
The answer is, Use Query History, to view queries and select query, and check the query profile to see time spent in each step.
Here is the view of the query profile, for more info use the link
https://docs.microsoft.com/en-us/azure/databricks/sql/admin/query-profile As you can see here Databricks SQL query profile is much different to Spark UI and provides much more clear information on how time is being spent on different queries and time it spent on each step.
Graphical user interface, application Description automatically generated
質問 # 170
You were asked to create a notebook that can take department as a parameter and process the data accordingly, which is the following statements result in storing the notebook parameter into a py-thon variable
正解：B
解説：
Explanation
The answer is department = dbutils.widget.get("department")
Refer to additional documentation here
https://docs.databricks.com/notebooks/widgets.html
質問 # 171
Which of the following SQL statements can replace python variables in Databricks SQL code, when the notebook is set in SQL mode?
1.%python
2.table_name = "sales"
3.schema_name = "bronze"
4.
5.%sql
6.SELECT * FROM ____________________
正解：A
解説：
Explanation
The answer is, SELECT * FROM ${schema_name}.${table_name}
%python
table_name = "sales"
schema_name = "bronze"
%sql
SELECT * FROM ${schema_name}.${table_name}
${python variable} -> Python variables in Databricks SQL code
質問 # 172
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：
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 1 second. 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.https://www.databricks.com/blog/2017/05/08/event-time-aggregation- watermarking-apache-sparks-structured-streaming.html
質問 # 173
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