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

Section	Weight	Objectives
Topic 1: Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality
Topic 2: Data Modeling and Storage	20%	- Data Modeling - File Formats - Storage Optimization
Topic 3: Monitoring and Troubleshooting	16%	- Monitoring - Troubleshooting - Performance Optimization
Topic 4: Data Processing	28%	- Structured Streaming - Spark SQL - ETL Pipelines - Data Transformation
Topic 5: Databricks Lakehouse Platform	24%	- Data Management - Lakehouse Architecture - Unity Catalog - Delta Lake

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q130-Q135):

質問 # 130
Which statement describes Delta Lake Auto Compaction?

A. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
B. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
C. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
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D. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.

正解：E

解説：
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB. Auto Compaction only compacts files that have not been compacted previously.

質問 # 131
A Delta Lake table was created with the below query:
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Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A. All related files and metadata are dropped and recreated in a single ACID transaction.
B. A new Delta transaction log Is created for the renamed table.
C. The table reference in the metastore is updated and all data files are moved.
D. The table name change is recorded in the Delta transaction log.
E. The table reference in the metastore is updated and no data is changed.

正解：E

解説：
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which 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 from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly.

質問 # 132
What statement is true regarding the retention of job run history?

A. It is retained until you export or delete job run logs
B. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
C. It is retained for 60 days, during which you can export notebook run results to HTML
D. It is retained for 60 days, after which logs are archived
E. It is retained for 90 days or until the run-id is re-used through custom run configuration

正解：C

質問 # 133
A Data Engineer is building a fraud detection pipeline that calls out to Open AI, via a Python library, and needs to include an access token when using the API. Which Databricks CLI command should the Data Engineer use to create the secret?

A. databricks secrets put-secret KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
B. databricks secrets put-secret SCOPE KEY; dbutils.secrets.get (SCOPE, KEY)
C. databricks tokens put-token SCOPE KEY; dbutils.tokens.get (SCOPE, KEY)
D. databricks tokens put-token KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)

正解：B

解説：
The Databricks CLI command databricks secrets put-secret <scope> <key> is used to securely store sensitive values such as API access tokens in a secret scope. The secret can then be retrieved at runtime using dbutils.secrets.get(scope, key), ensuring credentials are not hard- coded and remain protected.

質問 # 134
A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

A. Partition the table by date.
B. Overwrite the table instead of Merge.
C. Use ZORDER on high-cardinality columns.
D. Apply liquid clustering using the merge join keys.
E. Enable deletion vectors on the table if not already enabled.

正解：D、E

解説：
Liquid clustering on the merge join keys improves data locality and reduces the amount of data scanned during MERGE operations, which is especially effective for large, frequently updated tables. Enabling deletion vectors avoids rewriting entire Parquet files for updates and deletes, significantly reducing I/O and improving MERGE performance on Unity Catalog-managed tables.

質問 # 135
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