Databricks Databricks-Certified-Data-Engineer-Associate Accurate Prep Material - Databricks-Certified-Data-Engineer-Associate Relevant Questions

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

SectionObjectives
Topic 1: Data Governance and Quality- Unity Catalog basics
- Data quality concepts and management
- Data access control and governance
Topic 2: Data Ingestion and ELT Development- ETL patterns and transformations
- Handling structured and semi-structured data
- Data ingestion using Spark SQL and PySpark
Topic 3: Productionizing Data Pipelines- Databricks Workflows / Jobs orchestration
- Scheduling and monitoring jobs
- Pipeline deployment and operationalization
Topic 4: Databricks Lakehouse Platform Fundamentals- Clusters, notebooks, and basic Databricks environment usage
- Workspace, architecture, and core platform concepts
Topic 5: Data Processing and Transformations- Apache Spark SQL operations (joins, aggregations, filtering)
- Delta Lake fundamentals (tables, transactions, optimization)
- PySpark DataFrame transformations
- User-defined functions (UDFs)

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

Answer: C


NEW QUESTION # 24
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?

Answer: A

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.
CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV


NEW QUESTION # 25
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

Answer: D


NEW QUESTION # 26
A data engineer must apply a daily batch of customer records to a Delta table so that existing customers are updated, new customers are inserted, and no duplicates are created.
Which statement accomplishes this in a single atomic operation?

Answer: B

Explanation:
MERGE INTO performs upserts in a single ACID transaction, updating matched rows and inserting unmatched rows. INSERT INTO would create duplicates, and both INSERT OVERWRITE and CREATE OR REPLACE would discard existing records not present in the daily batch.


NEW QUESTION # 27
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which location can the data engineer review their permissions on the table?

Answer: C


NEW QUESTION # 28
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