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Databricks is a leading company in the field of data engineering and machine learning. The company offers a wide range of services and tools to help organizations manage and analyze their data more effectively. One of the key offerings from Databricks is the Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the skills and knowledge of data engineers who work with Databricks.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q77-Q82):

NEW QUESTION # 77
You are working on a process to load external CSV files into a delta table by leveraging the COPY INTO command, but after running the command for the second time no data was loaded into the table name, why is that?
1.COPY INTO table_name
2.FROM 'dbfs:/mnt/raw/*.csv'
3.FILEFORMAT = CSV

A. COPY INTO did not detect new files after the last load
B. COPY INTO only works one time data load
C. Use incremental = TRUE option to load new files
D. COPY INTO does not support incremental load, use AUTO LOADER
E. Run REFRESH TABLE sales before running COPY INTO

Answer: A

Explanation:
Explanation
The answer is COPY INTO did not detect new files after the last load,
COPY INTO keeps track of files that were successfully loaded into the table, the next time when the COPY INTO runs it skips them.
FYI, you can change this behavior by using COPY_OPTIONS 'force'= 'true', when this option is enabled all files in the path/pattern are loaded.
1.COPY INTO table_identifier
2. FROM [ file_location | (SELECT identifier_list FROM file_location) ]
3. FILEFORMAT = data_source
4. [FILES = [file_name, ... | PATTERN = 'regex_pattern']
5. [FORMAT_OPTIONS ('data_source_reader_option' = 'value', ...)]
6. [COPY_OPTIONS 'force' = ('false'|'true')]

NEW QUESTION # 78
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. 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.
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. 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.
D. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
E. 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.

Answer: D

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 time1. 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 job2.
The other options are not correct, because:
* Option A is incorrect because triggering batches more frequently does not allow idle executors to begin processing the next batch while longer running tasks from previous batches finish. In fact, the opposite is true. Triggering batches more frequently may cause concurrent batches to compete for the same resources and cause contention and backpressure2. This can degrade the performance and stability of the streaming job.
* Option B is incorrect because increasing the trigger interval to 30 seconds is not a good practice to ensure no records are dropped. Increasing the trigger interval means that the streaming query will process larger batches of data less frequently, which can increase the risk of spill, memory pressure, and timeouts12. This can also increase the latency and reduce the throughput of the streaming job.
* Option C is incorrect because the trigger interval can be modified without modifying the checkpoint directory. The checkpoint directory stores the metadata and state of the streaming query, such as the offsets, schema, and configuration3. Changing the trigger interval does not affect the state of the streaming query, and does not require a new checkpoint directory. However, changing the number of shuffle partitions may affect the state of the streaming query, and may require a new checkpoint directory4.
* Option D is incorrect because using the trigger once option and configuring a Databricks job to execute the query every 10 seconds does not ensure that all backlogged records are processed with each batch. The trigger once option means that the streaming querywill process all the available data in the source and then stop5. However, this does not guarantee that the query will finish processing within 10 seconds, especially if there are a lot of records in the source. Moreover, configuring a Databricks job to execute the query every 10 seconds may cause overlapping or missed batches, depending on the execution time of the query.
References: Memory Management Overview, Structured Streaming Performance Tuning Guide, Checkpointing, Recovery Semantics after Changes in a Streaming Query, Triggers

NEW QUESTION # 79
You are currently working to ingest millions of files that get uploaded to the cloud object storage for consumption, and you are asked to build a process to ingest this data, the schema of the file is expected to change over time, and the ingestion process should be able to handle these changes automatically. Which of the following method can be used to ingest the data incrementally?

A. AUTO LOADER
B. AUTO APPEND
C. Checkpoint
D. COPY INTO
E. Structured Streaming

Answer: A

Explanation:
Explanation
The answer is AUTO LOADER,
Use Auto Loader instead of the COPY INTO SQL command when:
*You want to load data from a file location that contains files in the order of millions or higher. Auto Loader can discover files more efficiently than the COPY INTO SQL command and can split file processing into multiple batches.
*COPY INTO only directory listing but AUTO LOADER supports File notification method where the Auto Loader continues to ingest files as they arrive in cloud object storage lever-aging cloud provider(Queues and triggers) and Spark's structured streaming.
*Your data schema evolves frequently. Auto Loader provides better support for schema in-ference and evolution. See Configuring schema inference and evolution in Auto Loader.

NEW QUESTION # 80
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?

A. The total count of records is calculated from the parquet file metadata
B. The total count of records is calculated from the Hive metastore
C. The total count of rows is calculated by scanning all data files
D. The total count of rows will be returned from cached results unless REFRESH is run
E. The total count of records is calculated from the Delta transaction logs

Answer: E

Explanation:
Explanation
https://delta.io/blog/2023-04-19-faster-aggregations-metadata/#:~:text=You%20can%20get%20the%20number,a

NEW QUESTION # 81
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series ofVACUUMcommands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
B. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
C. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
D. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
E. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.

Answer: D

Explanation:
Explanation
This is the correct answer because Delta Lake's delete statements do not physically remove the data files that contain the deleted records, but only mark them as logically deleted in the transaction log. These files are still accessible with time travel until they are permanently deleted by the vacuum command. The default data retention threshold for vacuum is 7 days, but in this case it is overridden by setting it to 24 hours in each vacuum command. Therefore, deleted records may be accessible with time travel for around 24 hours after they are deleted, until they are vacuumed. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Vacuum" section.

NEW QUESTION # 82
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