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Databricks Certified Data Engineer Professional Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
B. No; the change data feed only tracks inserts and updates not deleted records.
C. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
D. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
E. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.

Answer: A

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

NEW QUESTION # 89
A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

A. Total Disk Space remains constant
B. Overall cluster CPU utilization is around 25%
C. Network I/O never spikes
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
D. Bytes Received never exceeds 80 million bytes per second
E. The five Minute Load Average remains consistent/flat

Answer: B

Explanation:
This is the correct answer because it indicates a bottleneck caused by code executing on the driver. A bottleneck is a situation where the performance or capacity of a system is limited by a single component or resource. A bottleneck can cause slow execution, high latency, or low throughput. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, one can look for indicators that show how the cluster resources are being utilized, such as CPU, memory, disk, or network. If the overall cluster CPU utilization is around 25%, it means that only one out of the four nodes (driver + 3 executors) is using its full CPU capacity, while the other three nodes are idle or underutilized. This suggests that the code executing on the driver is taking too long or consuming too much CPU resources, preventing the executors from receiving tasks or data to process. This can happen when the code has driver-side operations that are not parallelized or distributed, such as collecting large amounts of data to the driver, performing complex calculations on the driver, or using non-Spark libraries on the driver.

NEW QUESTION # 90
Which statement describes Delta Lake optimized writes?

A. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
B. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
C. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
D. Optimized writes logical partitions instead of directory partitions partition boundaries are only Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from represented in metadata fewer small files are written.

Answer: C

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.

NEW QUESTION # 91
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Choose the response that correctly fills in the blank within the code block to complete this task.

A. to_interval("event_time", "5 minutes").alias("time")
B. window("event_time", "10 minutes").alias("time")
C. "event_time"
D. lag("event_time", "10 minutes").alias("time")
E. window("event_time", "5 minutes").alias("time")

Answer: E

Explanation:
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five- minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time".

NEW QUESTION # 92
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
B. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
C. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
D. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
E. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

Answer: B

Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints. Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.

NEW QUESTION # 93
......

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q88-Q93):

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