RELIABLE DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER EXAM SIMULATIONS | REAL DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER QUESTION

Reliable Databricks-Certified-Professional-Data-Engineer Exam Simulations | Real Databricks-Certified-Professional-Data-Engineer Question

Reliable Databricks-Certified-Professional-Data-Engineer Exam Simulations | Real Databricks-Certified-Professional-Data-Engineer Question

Blog Article

Tags: Reliable Databricks-Certified-Professional-Data-Engineer Exam Simulations, Real Databricks-Certified-Professional-Data-Engineer Question, Online Databricks-Certified-Professional-Data-Engineer Training Materials, Databricks-Certified-Professional-Data-Engineer Pdf Braindumps, Databricks-Certified-Professional-Data-Engineer Reliable Test Answers

Do you want to get the valid and latest study material for Databricks-Certified-Professional-Data-Engineer actual test? Please stop hunting with aimless, Actual4test will offer you the updated and high quality Databricks study material for you. The Databricks-Certified-Professional-Data-Engineer training dumps are specially designed for the candidates like you by our professional expert team. Databricks-Certified-Professional-Data-Engineer Questions and answers are valuable and validity, which will give you some reference for the actual test. Please prepare well for the actual test with our Databricks-Certified-Professional-Data-Engineer practice torrent, 100% pass will be an easy thing.

To take the Databricks Databricks-Certified-Professional-Data-Engineer Exam, candidates must first complete the Databricks Certified Associate Data Analyst and Databricks Certified Associate Data Engineer exams. These exams provide a foundation of knowledge and skills that are necessary to pass the professional-level exam. Candidates must also have experience working with Databricks and be familiar with its various features and capabilities.

The Databricks Databricks-Certified-Professional-Data-Engineer exam covers a wide range of topics, including data architecture, data modeling, data integration, data processing, and data analytics. Databricks-Certified-Professional-Data-Engineer exam consists of both theoretical and practical components, which test the candidate's ability to apply their knowledge to real-world scenarios. The practical component requires candidates to complete a series of hands-on exercises using Databricks notebooks, which are used to build, test, and optimize data pipelines.

Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who work with Databricks. It demonstrates that the candidate has a deep understanding of Databricks and can use it effectively to solve complex data engineering problems. Databricks Certified Professional Data Engineer Exam certification can help data engineers advance their careers, increase their earning potential, and gain recognition as experts in the field of big data and machine learning.

>> Reliable Databricks-Certified-Professional-Data-Engineer Exam Simulations <<

Real Databricks-Certified-Professional-Data-Engineer Question, Online Databricks-Certified-Professional-Data-Engineer Training Materials

Although the pass rate of our Databricks-Certified-Professional-Data-Engineer study materials can be said to be the best compared with that of other exam tests, our experts all are never satisfied with the current results because they know the truth that only through steady progress can our Databricks-Certified-Professional-Data-Engineer Preparation braindumps win a place in the field of exam question making forever.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which of the following is true of Delta Lake and the Lakehouse?

  • A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
  • B. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
  • C. Z-order can only be applied to numeric values stored in Delta Lake tables
  • D. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
  • E. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.

Answer: B

Explanation:
Explanation
This is the correct answer because it is true of Delta Lake and the Lakehouse. Delta Lake uses Parquet as the underlying storage format for data files. Parquet is a columnar format that compresses data by column rather than by row. This means that Parquet can achieve high compression ratios for columns that have low cardinality or high repetition of values, such as integers, booleans, or dates. However, for columns that have high cardinality or low repetition of values, such as strings, Parquet cannot compress data very well.
Therefore, strings will only be compressed when a character is repeated multiple times within a row. Verified References:[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delta Lake core features - Schema enforcement and evolution" section.


NEW QUESTION # 14
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. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
  • B. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
  • C. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
  • D. Databricks has autotuned to a smaller target file size based on the overall size of data in the table

Answer: C

Explanation:
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 # 15
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

  • A. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
  • B. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
  • C. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
  • D. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
  • E. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.

Answer: C

Explanation:
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; [Databricks Documentation], under "Jobs API - Create" section.


NEW QUESTION # 16
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

  • A. Credential validation errors while pulling data from an external system.
  • B. Network latency due to some cluster nodes being in different regions from the source data
  • C. Task queueing resulting from improper thread pool assignment.
  • D. Skew caused by more data being assigned to a subset of spark-partitions.
  • E. Spill resulting from attached volume storage being too small.

Answer: D

Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified Reference: [Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.


NEW QUESTION # 17
You are currently working on a production job failure with a job set up in job clusters due to a data issue, what cluster do you need to start to investigate and analyze the data?

  • A. Existing job cluster can be used to investigate the issue
  • B. Databricks SQL Endpoint can be used to investigate the issue
  • C. A Job cluster can be used to analyze the problem
  • D. All-purpose cluster/ interactive cluster is the recommended way to run commands and view the data.

Answer: D

Explanation:
Explanation
Answer is All-purpose cluster/ interactive cluster is the recommended way to run commands and view the data.
A job cluster can not provide a way for a user to interact with a notebook once the job is submitted, but an Interactive cluster allows to you display data, view visualizations write or edit quries, which makes it a perfect fit to investigate and analyze the data.


NEW QUESTION # 18
......

If you are having the same challenging problem, don't worry; Databricks is here to help. Our direct and dependable Databricks Treasury with Databricks Certified Professional Data Engineer Exam Exam Questions in three formats will surely help you pass the Databricks Treasury with Databricks-Certified-Professional-Data-Engineer certification exam. Because this is a defining moment in your career, do not undervalue the importance of our Treasury with Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer) exam dumps. Profit from the opportunity to get these top-notch exam questions for the Databricks-Certified-Professional-Data-Engineer certification test.

Real Databricks-Certified-Professional-Data-Engineer Question: https://www.actual4test.com/Databricks-Certified-Professional-Data-Engineer_examcollection.html

Report this page