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Databricks Certified Data Engineer Professional Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?

A. Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
B. Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.
C. Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.
D. Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.
E. Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.

Answer: D

Explanation:
High Concurrency clusters in Databricks are designed for multiple concurrent users and workloads. They provide fine-grained sharing of cluster resources and are optimized for operations such as running multiple parallel queries and updates. This would be suitable for a solution that involves many pipelines with parallel updates, especially with high volume and high velocity data.

NEW QUESTION # 65
A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT,
latitude FLOAT, post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
B. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
C. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
D. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
E. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

Answer: A

Explanation:
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema: user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column.
When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs.

NEW QUESTION # 66
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store
If this statement is executed by a workspace admin, which result will occur?

A. Data will be marked as deleted but still recoverable with Time Travel.
B. An error will occur because Delta Lake prevents the deletion of production data.
C. The table will be removed from the catalog and the data will be deleted.
D. The table will be removed from the catalog but the data will remain in storage.
E. Nothing will occur until a COMMIT command is executed.

Answer: C

Explanation:
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage.

NEW QUESTION # 67
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude =
45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
C. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
D. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
E. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.

Answer: B

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from constraint, and the target table will not be updated.

NEW QUESTION # 68
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
B. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
C. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
D. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
E. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.

Answer: A

Explanation:
The adjustment that will meet the requirement of processing records in less than 10 seconds is to decrease the trigger interval to 5 seconds. This is because triggering batches more frequently may prevent records from backing up and large batches from causing spill. Spill is a phenomenon where the data in memory exceeds the available capacity and has to be written to disk, which can slow down the processing and increase the execution time. By reducing the trigger interval, the streaming query can process smaller batches of data more quickly and avoid spill. This can also improve the latency and throughput of the streaming job.

NEW QUESTION # 69
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Databricks Certified Data Engineer Professional Exam Sample Questions (Q64-Q69):

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