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

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

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q215-Q220):

質問 # 215
What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

A. Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
B. PII masking is only required for direct identifiers.
C. Dynamic data masking is applied only at rest, so it does not affect query performance.
D. Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.

正解：D

解説：
Consistent PII masking in distributed batch and streaming pipelines requires centrally defined and governed masking logic. Standardizing masking functions and enforcing them through Unity Catalog ensures that the same rules are applied uniformly across all datasets and execution paths, preventing inconsistencies across nodes and workloads.

質問 # 216
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
B. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
C. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
D. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
E. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.

正解：C

解説：
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

質問 # 217
Which statement regarding stream-static joins and static Delta tables is correct?

A. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
B. Stream-static joins cannot use static Delta tables because of consistency issues.
C. The checkpoint directory will be used to track state information for the unique keys present in the join.
D. The checkpoint directory will be used to track updates to the static Delta table.
E. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.

正解：E

解説：
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch.

質問 # 218
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. Whenever a database is being created, make sure that the location keyword is used
B. Whenever a table is being created, make sure that the location keyword is used.
C. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
D. When the workspace is being configured, make sure that external cloud object storage has been mounted.
E. When tables are created, make sure that the external keyword is used in the create table statement.

正解：B

解説：
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.

質問 # 219
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
B. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
C. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
D. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.

正解：A

解説：
In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.

質問 # 220
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