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Databricks Certified Professional Data Engineer Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto- Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
B. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
C. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
D. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
E. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024
/512), and then write to parquet.

Answer: B

Explanation:
For this scenario where a one-TB JSON dataset needs to be converted into Parquet format without employing Delta Lake's auto-sizing features, the goal is to avoid unnecessary data shuffles and yet ensure optimal file sizes for the output Parquet files. Here's a breakdown of why option A is most suitable:
* Setting maxPartitionBytes:The spark.sql.files.maxPartitionBytes configuration controls the size of blocks that Spark reads from the data source (in this case, the JSON files) but also influences the output size of files when data is written without repartition or coalesce operations. Setting this parameter to
512 MB directly addresses the requirement to manage the output file size effectively.
* Data Ingestion and Processing:
* Ingesting Data:Load the JSON dataset into a DataFrame.
* Applying Transformations:Perform any required narrow transformations that do not involve shuffling data (like filtering or adding new columns).
* Writing to Parquet:Directly write the transformed DataFrame to Parquet files. The setting for maxPartitionBytes ensures that each part-file is approximately 512 MB, meeting the requirement for part-file size without additional steps to repartition or coalesce the data.
* Performance Consideration:This approach is optimal because:
* It avoids the overhead of shuffling data, which can be significant, especially with large datasets.
* It directly ties the read/write operations to a configuration that matches the target output size, making it efficient in terms of both computation and I/O operations.
* Alternative Options Analysis:
* Option B and D:Involves repartitioning, which would trigger a shuffle of the data, contradicting the requirement to avoid shuffling for performance reasons.
* Option C:Uses coalesce, which is less intensive than repartition but can still lead to uneven partition sizes and does not directly control the output file size as effectively as setting maxPartitionBytes.
* Option E:Setting shuffle partitions to 512 doesn't directly control the output file size for writing to Parquet and could lead to smaller files depending on the dataset's partitioning post- transformations.
References
* Apache Spark Configuration
* Writing to Parquet Files in Spark

NEW QUESTION # 64
What is the purpose of a gold layer in Multi-hop architecture?

A. Preserves grain of original data, without any aggregations
B. Optimizes ETL throughput and analytic query performance
C. Data quality checks and schema enforcement
D. Eliminate duplicate records
E. Powers ML applications, reporting, dashboards and adhoc reports.

Answer: E

Explanation:
Explanation
The answer is Powers ML applications, reporting, dashboards and adhoc reports.
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1.Powers Ml applications, reporting, dashboards, ad hoc analytics
2.Refined views of data, typically with aggregations
3.Reduces strain on production systems
4.Optimizes query performance for business-critical data
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.

NEW QUESTION # 65
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When a database is being created, make sure that the LOCATION keyword is used.
B. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
C. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
D. When the workspace is being configured, make sure that external cloud object storage has been mounted.
E. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.

Answer: C

Explanation:
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored. For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION '/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the '/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.
Reference:
https://docs.databricks.com/delta/delta-batch.html#create-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-a-table

NEW QUESTION # 66
An external object storage container has been mounted to the location/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of thefinancegroup, which statement describes how thetx_sales table will be created?

A. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
B. A managed table will be created in the DBFS root storage container.
C. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
D. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
E. An external table will be created in the storage container mounted to /mnt/finance eda bucket.

Answer: D

Explanation:
https://docs.databricks.com/en/lakehouse/data-objects.html

NEW QUESTION # 67
What is the best way to query external csv files located on DBFS Storage to inspect the data using SQL?

A. You can not query external files directly, us COPY INTO to load the data into a table first
B. SELECT * FROM 'dbfs:/location/csv_files/' FORMAT = 'CSV'
C. SELECT CSV. * from 'dbfs:/location/csv_files/'
D. SELECT * FROM CSV. 'dbfs:/location/csv_files/'
E. SELECT * FROM 'dbfs:/location/csv_files/' USING CSV

Answer: D

Explanation:
Explanation
Answer is, SELECT * FROM CSV. 'dbfs:/location/csv_files/'
you can query external files stored on the storage using below syntax
SELECT * FROM format.`/Location`
format - CSV, JSON, PARQUET, TEXT

NEW QUESTION # 68
......

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CERTIFICATION DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER TEST QUESTIONS - RELIABLE DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER EXAM BOOTCAMP

Certification Databricks Databricks-Certified-Professional-Data-Engineer Test Questions - Reliable Databricks-Certified-Professional-Data-Engineer Exam Bootcamp