Databricks-Certified-Professional-Data-Engineer덤프데모문제다운, Databricks-Certified-Professional-Data-Engineer합격보장가능시험

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Data Ingestion	15-20%	- Batch ingestion methods 1. DBR autoloader 2. Integration with external systems 3. Spark APIs for ingestion - Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Task dependencies and orchestration 2. Jobs and job scheduling 3. Monitoring and alerting
Delta Lake	20-25%	- Delta Lake operations 1. Merge, update, delete operations 2. Delta Live Tables 3. Schema evolution and enforcement - Delta Lake fundamentals 1. Time travel and data versioning 2. Optimize and Z-order 3. ACID transactions
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Data governance fundamentals 2. Bronze, silver, gold data layers 3. Differences between data lake, data warehouse, and lakehouse
Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Spark APIs in Python 2. Performance optimization techniques 3. Built-in and user-defined functions - Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Window functions 3. Spark SQL queries and functions

>> Databricks-Certified-Professional-Data-Engineer덤프데모문제 다운 <<

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IT업계에 종사하시는 분은 국제공인 IT인증자격증 취득이 얼마나 힘든지 알고 계실것입니다. 특히 시험이 영어로 되어있어 부담을 느끼시는 분도 계시는데 PassTIP를 알게 된 이상 이런 고민은 버리셔도 됩니다. PassTIP의Databricks Databricks-Certified-Professional-Data-Engineer덤프는 모두 영어버전으로 되어있어Databricks Databricks-Certified-Professional-Data-Engineer시험의 가장 최근 기출문제를 분석하여 정답까지 작성해두었기에 문제와 답만 외우시면 시험합격가능합니다.

최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q80-Q85):

질문 # 80
A junior data engineer is working to implement logic for a Lakehouse table namedsilver_device_recordings.
The source data contains 100 unique fields in a highly nested JSON structure.
Thesilver_device_recordingstable will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
B. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
C. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
D. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
E. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

정답：C

설명：
This is the correct answer because it accurately presents information about Delta Lake and Databricks that may impact the decision-making process of a junior data engineer who is tryingto determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Delta Lake and Databricks support schema inference and evolution, which means that they can automatically infer the schema of a table from the source data and allow adding new columns or changing column types without affecting existing queries or pipelines. However, schema inference and evolution may not always be desirable or reliable, especially when dealing with complex or nested data structures or when enforcing data quality and consistency across different systems. Therefore, setting types manually can provide greater assurance of data quality enforcement and avoid potential errors or conflicts due to incompatible or unexpected data types. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Schema inference and partition of streaming DataFrames/Datasets" section.

질문 # 81
Which of the statements are correct about lakehouse?

A. Lakehouse only supports end-to-end streaming workloads and Data warehouses support Batch workloads
B. Lakehouse only supports Machine learning workloads and Data warehouses support BI workloads
C. In Lakehouse Storage and compute are coupled
D. Lakehouse supports schema enforcement and evolution
E. Lakehouse does not support ACID

정답：D

설명：
Explanation
The answer is Lakehouse supports schema enforcement and evolution,
Lakehouse using Delta lake can not only enforce a schema on write which is contrary to traditional big data systems that can only enforce a schema on read, it also supports evolving schema over time with the ability to control the evolution.
For example below is the Dataframe writer API and it supports three modes of enforcement and evolution, Default: Only enforcement, no changes are allowed and any schema drift/evolution will result in failure.
Merge: Flexible, supports enforcement and evolution
* New columns are added
* Evolves nested columns
* Supports evolving data types, like Byte to Short to Integer to Bigint How to enable:
* DF.write.format("delta").option("mergeSchema", "true").saveAsTable("table_name")
* or
* spark.databricks.delta.schema.autoMerge = True ## Spark session
Overwrite: No enforcement
* Dropping columns
* Change string to integer
* Rename columns
How to enable:
* DF.write.format("delta").option("overwriteSchema", "True").saveAsTable("table_name") What Is a Lakehouse? - The Databricks Blog Graphical user interface, text, application Description automatically generated

질문 # 82
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A. Option E
B. Option C
C. Option B
D. Option A
E. Option D

정답：C

설명：
Option B correctly fills in the blank to meet the specified requirements. Option B uses the "cloudFiles.
schemaLocation" option, which is required for the schema detection andevolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. References:
* Configure schema inference and evolution in Auto Loader: https://docs.databricks.com/en/ingestion
/auto-loader/schema.html
* Write streaming data: https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming- data.html

질문 # 83
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Use &sh install in a notebook cell
B. Install libraries from PyPi using the cluster UI
C. Run source env/bin/activate in a notebook setup script
D. Use &Pip install in a notebook cell

정답：D

설명：
In Databricks notebooks, you can use the %pip install command in a notebook cell to install a Python package. This will install the package on all nodes in the currently active cluster at the notebook level. It is a feature provided by Databricks to facilitate the installation of Python libraries for the notebook environment specifically.

질문 # 84
Projecting a multi-dimensional dataset onto which vector has the greatest variance?

A. second eigenvector
B. first eigenvector
C. first principal component
D. not enough information given to answer
E. second principal component

정답：C

설명：
Explanation
The method based on principal component analysis (PCA) evaluates the features according to the projection of
the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear
discriminant analysis evaluates. Them according to the magnitude of the components of the discriminant
vector.
The first principal component corresponds to the greatest variance in the data, by definition. If we project the
data onto the first principal component line, the data is more spread out (higher variance) than if projected onto
any other line, including other principal components.

질문 # 85
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