John Hart John Hart's Profile Page

John Hart John Hart

0 Course Enrolled • 0 Course Completed

Biography

Databricks-Certified-Data-Engineer-Associate Latest Test Questions - New Databricks-Certified-Data-Engineer-Associate Test Topics

DOWNLOAD the newest PrepAwayTest Databricks-Certified-Data-Engineer-Associate PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=1gG5jRn9ya7HgrQk9b4bmq1CdxJl3HQyv

Our Databricks-Certified-Data-Engineer-Associate study tool can help you obtain the Databricks-Certified-Data-Engineer-Associate certification and own a powerful weapon for your interview. Our Databricks-Certified-Data-Engineer-Associate qualification test will help you gain recognition with true talents and better adapted to society. Now, I would like to give you a brief introduction in order to make you deepen your impression of our Databricks-Certified-Data-Engineer-Associate test guides. Our Databricks-Certified-Data-Engineer-Associate test guides have a higher standard of practice and are rich in content. If you are anxious about how to get Databricks-Certified-Data-Engineer-Associate certification, considering purchasing our Databricks-Certified-Data-Engineer-Associate study tool is a wise choice and you will not feel regretted. Our learning materials will successfully promote your acquisition of certification.

Databricks Certified Data Engineer Associate certification is a valuable credential for professionals who work in data engineering. Databricks Certified Data Engineer Associate Exam certification demonstrates that the candidate has a deep understanding of data engineering concepts and has the skills and experience to work with Databricks effectively. Employers often look for candidates who hold this certification when hiring for data engineering roles, as it validates the candidate's expertise and knowledge in the field.

>> Databricks-Certified-Data-Engineer-Associate Latest Test Questions <<

Unparalleled Databricks - Databricks-Certified-Data-Engineer-Associate - Databricks Certified Data Engineer Associate Exam Latest Test Questions

The meaning of qualifying examinations is, in some ways, to prove the candidate's ability to obtain qualifications that show your ability in various fields of expertise. If you choose our Databricks-Certified-Data-Engineer-Associate learning dumps, you can create more unlimited value in the limited study time, learn more knowledge, and take the exam that you can take. Through qualifying examinations, this is our Databricks-Certified-Data-Engineer-Associate Real Questions and the common goal of every user, we are trustworthy helpers, so please don't miss such a good opportunity. The acquisition of Databricks qualification certificates can better meet the needs of users' career development, so as to bring more promotion space for users. This is what we need to realize.

The Databricks Databricks-Certified-Data-Engineer-Associate Exam is a 90-minute online exam that consists of 60 multiple-choice questions. Databricks-Certified-Data-Engineer-Associate exam covers various topics such as data ingestion, data transformation, data modeling, data warehousing, and data analysis. Databricks-Certified-Data-Engineer-Associate exam is designed to validate the candidate's ability to work with Databricks in a production environment.

The Databricks Databricks-Certified-Data-Engineer-Associate exam consists of 60 multiple-choice questions that must be completed in 90 minutes. The passing score for the exam is 70%, and candidates who pass the exam will receive a certificate that validates their knowledge and expertise in Databricks. Databricks Certified Data Engineer Associate Exam certification is recognized globally and is a valuable asset for data engineers who want to advance their careers and demonstrate their proficiency in Databricks.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

A. They can clone the existing task to a new Job and then edit it to run the new notebook.
B. They can create a new job from scratch and add both tasks to run concurrently.
C. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
D. They can create a new task in the existing Job and then add it as a dependency of the original task.
E. They can clone the existing task in the existing Job and update it to run the new notebook.

Answer: D

Explanation:
Explanation
To set up the new task to run a new notebook prior to the original task in a single-task Job, the data engineer can use the following approach: In the existing Job, create a new task that corresponds to the new notebook that needs to be run. Set up the new task with the appropriate configuration, specifying the notebook to be executed and any necessary parameters or dependencies. Once the new task is created, designate it as a dependency of the original task in the Job configuration. This ensures that the new task is executed before the original task.

NEW QUESTION # 18
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(processingTime="5 seconds")
B. trigger(once="5 seconds")
C. trigger(continuous="5 seconds")
D. trigger()
E. trigger("5 seconds")

Answer: A

Explanation:
Explanation
# ProcessingTime trigger with two-seconds micro-batch interval
df.writeStream
format("console")
trigger(processingTime='2 seconds')
start()
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#triggers

NEW QUESTION # 19
In which of the following file formats is data from Delta Lake tables primarily stored?

A. CSV
B. A proprietary, optimized format specific to Databricks
C. Delta
D. Parquet
E. JSON

Answer: D

Explanation:
Delta Lake is an open source project that provides ACID transactions, time travel, and other features on top of Apache Parquet, a columnar file format that is widely used for big data analytics. Delta Lake uses versioned Parquet files to store your data in your cloud storage, along with JSON files as transaction logs and checkpoint files to track the changes and ensure data integrity. Delta Lake is compatible with any Apache Hive compatible file format, such as CSV, JSON, or AVRO, but it primarily stores data as Parquet files for better performance and compression. References: How to Create Delta Lake tables, 5 reasons to choose Delta Lake format (on Databricks), Parquet vs Delta format in Azure Data Lake Gen 2 store, What is Delta Lake? - Azure Databricks, Lakehouse and Delta tables - Microsoft Fabric

NEW QUESTION # 20
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?

A. When they are running automated reports to be refreshed as quickly as possible
B. When they are manually running reports with a large amount of data
C. When they are working interactively with a small amount of data
D. When they are concerned about the ability to automatically scale with larger data
E. When they are working with SQL within Databricks SQL

Answer: C

Explanation:
The scenario in which a data engineer will want to use a single-node cluster is when they are working interactively with a small amount of data. A single-node cluster is a cluster consisting of an Apache Spark driver and no Spark workers1. A single-node cluster supports Spark jobs and all Spark data sources, including Delta Lake1. A single-node cluster is helpful for single-node machine learning workloads that use Spark to load and save data, and for lightweight exploratory data analysis1. A single-node cluster can run Spark locally, spawn one executor thread per logical core in the cluster, and save all log output in the driver log1. A single-node cluster can be created by selecting the Single Node button when configuring a cluster1.
The other options are not suitable for using a single-node cluster. When running automated reports to be refreshed as quickly as possible, a data engineer will want to use a multi-node cluster that can scale up and down automatically based on the workload demand2. When working with SQL within Databricks SQL, a data engineer will want to use a SQL Endpoint that can execute SQL queries on a serverless pool or an existing cluster3. When concerned about the ability to automatically scale with larger data, a data engineer will want to use a multi-node cluster that can leverage the Databricks Lakehouse Platform and the Delta Engine to handle large-scale data processing efficiently and reliably4. When manually running reports with a large amount of data, a data engineer will want to use a multi-node cluster that can distribute the computation across multiple workers and leverage the Spark UI to monitor the performance and troubleshoot the issues.
Reference:
1: Single Node clusters | Databricks on AWS
2: Autoscaling | Databricks on AWS
3: SQL Endpoints | Databricks on AWS
4: Databricks Lakehouse Platform | Databricks on AWS
5: [Spark UI | Databricks on AWS]

NEW QUESTION # 21
Which of the following Git operations must be performed outside of Databricks Repos?

A. Push
B. Pull
C. Commit
D. Clone
E. Merge

Answer: E

Explanation:
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, branch management, and visual comparison of diffs when committing1. However, merge is not supported in the Git dialog2. You need to use the Repos UI or your Git provider to merge branches3. Merge is a way to combine the commit history from one branch into another branch1. During a merge, a merge conflict is encountered when Git cannot automatically combine code from one branch into another. Merge conflicts require manual resolution before a merge can be completed1. Reference: 4: Run Git operations on Databricks Repos4, 1: CI/CD techniques with Git and Databricks Repos1, 3: Collaborate in Repos3, 2: Databricks Repos - What it is and how we can use it2.
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, merge, and branch management. However, to clone a remote Git repository to a Databricks repo, you must use the Databricks UI or API. You cannot clone a Git repo using the CLI through a cluster's web terminal, as the files won't display in the Databricks UI1. Reference: 1: Run Git operations on Databricks Repos | Databricks on AWS2

NEW QUESTION # 22
......

New Databricks-Certified-Data-Engineer-Associate Test Topics: https://www.prepawaytest.com/Databricks/Databricks-Certified-Data-Engineer-Associate-practice-exam-dumps.html

P.S. Free & New Databricks-Certified-Data-Engineer-Associate dumps are available on Google Drive shared by PrepAwayTest: https://drive.google.com/open?id=1gG5jRn9ya7HgrQk9b4bmq1CdxJl3HQyv