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Databricks Certified Professional Data Engineer Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
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 invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to addCHECKconstraints 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?

A. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
B. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
C. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
D. The activity details table already exists; CHECK constraints can only be added during initial table creation.
E. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

Answer: E

Explanation:
Explanation
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 References:
[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

NEW QUESTION # 52
A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization.
Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose 2 answers)

A. Use Auto Loader and set cloudFiles.format to "TEXT".
B. Use Auto Loader and set cloudFiles.format to "BINARYFILE".
C. Use Auto Loader and set cloudFiles.format to "IMAGE".
D. Move files to a volume and read with SQL editor.
E. Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").

Answer: B,E

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks Auto Loader supports ingestion of binary file formats using the cloudFiles.format option. For ingesting JPEG or PNG image files, the correct setting is "BINARYFILE", which loads the raw binary content and file metadata into a DataFrame. Additionally, when processing files from object storage, it is best practice to apply pathGlobFilter to limit ingestion to specific file types and reduce unnecessary scanning of non-image files. Options like "IMAGE" or "TEXT" are invalid, and using volumes with SQL editors does not provide incremental ingestion. Therefore, combining Auto Loader with cloudFiles.format="BINARYFILE" and pathGlobFilter ensures scalable, incremental ingestion of image data into Unity Catalog tables.

NEW QUESTION # 53
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of the Job's schedule and configuration?

A. They can download the XML description of the Job from the Job's page
B. They can download the JSON equivalent of the job from the Job's page.
C. They can link the Job to notebooks that are a part of a Databricks Repo.
D. They can submit the Job once on an all-purpose cluster.
E. They can submit the Job once on a Job cluster.

Answer: D

NEW QUESTION # 54
The default threshold of VACUUM is 7 days, internal audit team asked to certain tables to maintain at least
365 days as part of compliance requirement, which of the below setting is needed to implement.

A. ALTER TABLE table_name set EXENDED TBLPROPERTIES (delta.vaccum.duration= 'interval 365 days')
B. MODIFY TABLE table_name set TBLPROPERTY (delta.maxRetentionDays = 'inter-val 365 days')
C. ALTER TABLE table_name set EXENDED TBLPROPERTIES (del-ta.deletedFileRetentionDuration=
'interval 365 days')
D. ALTER TABLE table_name set TBLPROPERTIES (del-ta.deletedFileRetentionDuration= 'interval 365 days')

Answer: D

Explanation:
Explanation
1.ALTER TABLE table_name SET TBLPROPERTIES ( property_key [ = ] property_val [, ...] ) TBLPROPERTIES allow you to set key-value pairs Table properties and table options (Databricks SQL) | Databricks on AWS

NEW QUESTION # 55
A Delta Lake table was created with the below query:

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

Answer: D

Explanation:
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. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.

NEW QUESTION # 56
......

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