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DATABRICKS LAKEHOUSE ACCREDITATION BADGE EXAM QUESTIONS WITH GUARANTEED ACCURATE ANSWERS $13.49   Add to cart

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DATABRICKS LAKEHOUSE ACCREDITATION BADGE EXAM QUESTIONS WITH GUARANTEED ACCURATE ANSWERS

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  • DATABRICKS LAKEHOUSE ACCREDITATIO

Which of the following is a common problem within a data lake architecture that can be easily solved by using the Databricks Lakehouse Platform? Select three responses. - Ineffective partitioning - Too many small files - Lack of cloud service integrations - Inability to use open-source data for...

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  • November 1, 2024
  • 15
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers
  • DATABRICKS LAKEHOUSE ACCREDITATIO
  • DATABRICKS LAKEHOUSE ACCREDITATIO
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GRADEUNITS
DATABRICKS LAKEHOUSE
ACCREDITATION BADGE EXAM
DATABRICKS LAKEHOUSE ACCREDITATION BADGE EXAM QUESTIONS WITH
GUARANTEED ACCURATE ANSWERS




Which of the following is a common problem within a data lake
architecture that can be easily solved by using the Databricks Lakehouse
Platform? Select three responses.
- Ineffective partitioning
- Too many small files
- Lack of cloud service integrations
- Inability to use open-source data formats
- Lack of ACID transaction support - ACCURATE ANSWERS✔✔
Ineffective partitioning, Too many small files, Lack of ACID transaction
support.

, Also: Lack of schema enforcement, lack of integration with a data
catalog


Maintaining and improving data quality is a major goal of modern data
engineering. Which of the following contributes directly to high levels
of data quality within the Databricks Lakehouse Platform? Select two
responses.


- Data expectations enforcement
- Business intelligence tool integrations
- Simplified machine learning model serving
- Table schema evolution
- Apache Spark's data format flexibility - ACCURATE ANSWERS✔✔
data expectations enforcement, Apache Spark's data format flexibility?


Which of the following is a benefit of the Databricks Lakehouse
Platform being designed to support all data and artificial intelligence
(AI) workloads? Select four responses.


1 Analysts can easily integrate their favorite business intelligence (BI)
tools for further analysis.
2 Data analysts, data engineers, and data scientists can easily collaborate
within a single platform.
3 Data workloads can be automatically scaled when needed.
4 There is increased need for multiple, specialist platform administrators
to maintain each component of the unified platform.

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