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Examcollection Databricks-Certified-Professional-Data-Engineer Free Dumps & Databricks-Certified-Professional-Data-Engineer Reliable Dumps

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The Databricks-Certified-Professional-Data-Engineer exam dumps are real and updated Databricks-Certified-Professional-Data-Engineer exam questions that are verified by subject matter experts. They work closely and check all Databricks-Certified-Professional-Data-Engineer exam dumps one by one. They maintain and ensure the top standard of ValidTorrent Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer) exam questions all the time. The Databricks-Certified-Professional-Data-Engineer practice test is being offered in three different formats. These Databricks-Certified-Professional-Data-Engineer exam questions formats are PDF dumps files, web-based practice test software, and desktop practice test software.

Databricks Certified Professional Data Engineer Exam covers a wide range of topics related to data engineering using Databricks, including data ingestion, data transformation, data storage, and data orchestration. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's proficiency in using Databricks tools and technologies such as Delta Lake, Apache Spark, and Databricks Runtime. Successful completion of the exam demonstrates that the candidate has the skills and knowledge required to design, build, and manage efficient and scalable data pipelines using Databricks. Databricks Certified Professional Data Engineer Exam certification also enhances the candidate's credibility and marketability in the job market, as it is recognized by leading organizations in the industry.

The Databricks Certified Professional Data Engineer Exam certification exam is a computer-based test that consists of multiple-choice questions. Candidates have two hours to complete the exam, and they must achieve a minimum score of 70% to pass. Databricks-Certified-Professional-Data-Engineer Exam is proctored, and candidates must have a reliable internet connection and a computer with a webcam and microphone to take the test.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q117-Q122):

NEW QUESTION # 117
When defining external tables using formats CSV, JSON, TEXT, BINARY any query on the exter-nal tables caches the data and location for performance reasons, so within a given spark session any new files that may have arrived will not be available after the initial query. How can we address this limitation?

A. CLEAR CACH table_name
B. UNCACHE TABLE table_name
C. CACHE TABLE table_name
D. REFRESH TABLE table_name
E. BROADCAST TABLE table_name

Answer: D

Explanation:
Explanation
The answer is REFRESH TABLE table_name
REFRESH TABLE table_name will force Spark to refresh the availability of external files and any changes.
When spark queries an external table it caches the files associated with it, so that way if the table is queried again it can use the cached files so it does not have to retrieve them again from cloud object storage, but the drawback here is that if new files are available Spark does not know until the Refresh command is ran.

NEW QUESTION # 118
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
B. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
C. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
D. Databricks has autotuned to a smaller target file size based on the amount of data in each partition

Answer: A

Explanation:
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section. https://docs.databricks.com/en/delta/tune-file-size.
html#autotune-table 'Autotune file size based on workload'

NEW QUESTION # 119
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?

A. The number of times the job definition has been run in the workspace.
B. The job_id is returned in this field.
C. The globally unique ID of the newly triggered run.
D. The job_id and number of times the job has been are concatenated and returned.

Answer: C

Explanation:
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id. While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.

NEW QUESTION # 120
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Configure a job that executes every time new data lands in a given directory.
B. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
C. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
D. Schedule a job to execute the pipeline once hour on a new job cluster.

Answer: D

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.
Reference:
Databricks documentation on jobs and job clusters: https://docs.databricks.com/jobs.html

NEW QUESTION # 121
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
B. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
C. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
D. Databricks has autotuned to a smaller target file size based on the amount of data in each partition

Answer: A

Explanation:
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section.
https://docs.databricks.com/en/delta/tune-file-size.html#autotune-table 'Autotune file size based on workload'

NEW QUESTION # 122
......

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Examcollection Databricks-Certified-Professional-Data-Engineer Free Dumps & Databricks-Certified-Professional-Data-Engineer Reliable Dumps

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q117-Q122):

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