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[General] 100% Pass Quiz 2026 Databricks Updated Latest Databricks-Certified-Data-Engineer

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【General】 100% Pass Quiz 2026 Databricks Updated Latest Databricks-Certified-Data-Engineer

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You will obtain these updates entirely free if the Databricks Databricks-Certified-Data-Engineer-Associate certification authorities issue fresh updates. SurePassExams ensures that you will hold the prestigious Databricks Databricks-Certified-Data-Engineer-Associate certificate on the first endeavor if you work consistently, taking help from our remarkable, up-to-date, and competitive Databricks Databricks-Certified-Data-Engineer-Associate dumps.
The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification exam that tests the skills and knowledge of individuals in the field of data engineering using Databricks. Databricks is a cloud-based data platform that allows data engineers to build, manage, and deploy data pipelines at scale. Databricks-Certified-Data-Engineer-Associate Exam is designed to assess an individual's proficiency in using Databricks to perform tasks such as data transformation, data ingestion, and data analysis.
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Databricks Certified Data Engineer Associate Exam Sample Questions (Q80-Q85):NEW QUESTION # 80
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?
  • A. Databricks account representative
  • B. New lead data engineer
  • C. Original data engineer
  • D. This transfer is not possible
  • E. Workspace administrator
Answer: B

NEW QUESTION # 81
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
  • A. The ability to manipulate the same data using a variety of languages
  • B. The ability to set up alerts for query failures
  • C. The ability to distribute complex data operations
  • D. The ability to support batch and streaming workloads
  • E. The ability to collaborate in real time on a single notebook
Answer: D
Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. References: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS] Learn more

NEW QUESTION # 82
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?
  • A. CREATE TABLE AS SELECT statements cannot be used on files
  • B. Parquet files have the ability to be optimized
  • C. Parquet files have a well-defined schema
  • D. Parquet files can be partitioned
  • E. Parquet files will become Delta tables
Answer: C
Explanation:
Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.
References: CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV

NEW QUESTION # 83
Which tool is used by Auto Loader to process data incrementally?
  • A. Checkpointing
  • B. Unity Catalog
  • C. Databricks SQL
  • D. Spark Structured Streaming
Answer: D
Explanation:
Auto Loader in Databricks utilizes Spark Structured Streaming for processing data incrementally. This allows Auto Loader to efficiently ingest streaming or batch data at scale and to recognize new data as it arrives in cloud storage. Spark Structured Streaming provides the underlying engine that supports various incremental data loading capabilities like schema inference and file notification mode, which are crucial for the dynamic nature of data lakes.
Reference:
Databricks documentation on Auto Loader: Auto Loader Overview

NEW QUESTION # 84
A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?
  • A. Review the Permissions tab in the table's page in Data Explorer
  • B. Review the Owner field in the table's page in Data Explorer
  • C. All of these options can be used to identify the owner of the table
  • D. Review the Owner field in the table's page in the cloud storage solution
  • E. There is no way to identify the owner of the table
Answer: B

NEW QUESTION # 85
......
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