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| Section | Objectives |
|---|---|
| Topic 1: Data Modeling and Storage | - Design scalable data lakehouse architectures - Delta Lake table design and optimization - Schema evolution and data partitioning strategies |
| Topic 2: Production Pipelines and Orchestration | - Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling |
| Topic 3: Data Ingestion and Transformation | - Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines |
| Topic 4: Security, Governance, Monitoring, and Optimization | - Cost optimization and performance tuning - Implement Unity Catalog governance and access control - Monitor and optimize Spark workloads |
>> Databricks-Certified-Professional-Data-Engineer対応問題集 <<
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質問 # 49
What is the main difference between the bronze layer and silver layer in a medallion architecture?
正解：B
解説：
Explanation
Medallion Architecture - Databricks
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence
質問 # 50
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
Which command allows manual confirmation that these three requirements have been met?
正解：B
解説：
This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified Reference: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.
質問 # 51
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?
正解：A
解説：
Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. References:
* Table Access Control: https://docs.databricks.com/security/access-control/table-acls/index.html
* DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table
質問 # 52
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：B
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.
:
Databricks documentation on DLT pipelines: https://docs.databricks.com/data-engineering/delta-live-tables
/delta-live-tables-overview.html
質問 # 53
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.
Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
正解：C
解説：
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified Reference: [Databricks Certified Data Engineer Professional], under "Language Interoperability" section; Databricks Documentation, under "Mix languages" section.
質問 # 54
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