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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: E


NEW QUESTION # 11
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?

Answer: A


NEW QUESTION # 12
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

Answer: C

Explanation:
The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.
The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user- defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.
:
1: PIVOT | Databricks on AWS
2: Reshaping Data - Long vs Wide Format | Databricks on AWS
3: CONVERT | Databricks on AWS
4: WHERE | Databricks on AWS
5: TRANSFORM | Databricks on AWS
6: [SUM | Databricks on AWS]


NEW QUESTION # 13
A data engineer has a Python variable table_name that they would like to use in a SQL query.
They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?

Answer: E


NEW QUESTION # 14
Which file format is used for storing Delta Lake Table?

Answer: D

Explanation:
Delta Lake tables use the Parquet format as their underlying storage format. Delta Lake enhances Parquet by adding a transaction log that keeps track of all the operations performed on the table. This allows features like ACID transactions, scalable metadata handling, and schema enforcement, making it an ideal choice for big data processing and management in environments like Databricks.
Reference:
Databricks documentation on Delta Lake: Delta Lake Overview


NEW QUESTION # 15
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