Databricks-Certified-Professional-Data-Engineer Exam Preparation | Databricks-Certified-Professional-Data-Engineer Certification Torrent

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
Which statement describes Delta Lake Auto Compaction?

A. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
B. 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.
C. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
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.

Answer: E

Explanation:
Explanation
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. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.

NEW QUESTION # 69
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 abronzetable created with the propertydelta.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, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
B. 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.
C. 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.
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 entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

Answer: E

Explanation:
Explanation
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.

NEW QUESTION # 70
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 the finance group, which statement describes how the tx_sales table will be created?

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

Answer: E

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

NEW QUESTION # 71
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?

A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
B. Both commands will fail. No new variables, tables, or views will be created.
C. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
E. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.

Answer: A

Explanation:
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.

NEW QUESTION # 72
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Which step must also be completed to put the proposed query into production?

A. Remove .option (mergeSchema', true') from the streaming write
B. Specify a new checkpointlocation
C. Run REFRESH TABLE delta, /item_agg'
D. Increase the shuffle partitions to account for additional aggregates

Answer: B

Explanation:
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.
References:
* Databricks documentation on Structured Streaming:
https://docs.databricks.com/spark/latest/structured-streaming/index.html
* Databricks documentation on streaming checkpoints:
https://docs.databricks.com/spark/latest/structured-streaming/production.html#checkpointing

NEW QUESTION # 73
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Databricks-Certified-Professional-Data-Engineer Exam Preparation | Databricks-Certified-Professional-Data-Engineer Certification Torrent

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q68-Q73):

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