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The Databricks Databricks-Certified-Data-Engineer-Associate Exam consists of 60 multiple-choice questions that must be completed in 90 minutes. The passing score for the exam is 70%, and candidates who pass the exam will receive a certificate that validates their knowledge and expertise in Databricks. Databricks Certified Data Engineer Associate Exam certification is recognized globally and is a valuable asset for data engineers who want to advance their careers and demonstrate their proficiency in Databricks.

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Databricks Certified Data Engineer Associate certification is an essential certification for individuals who want to pursue a career in data engineering. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics and is designed to ensure that candidates have the necessary skills and knowledge to build and maintain large-scale data pipelines. By earning this certification, candidates can demonstrate their expertise in data engineering and increase their chances of getting hired by top companies in the data and AI industry.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q292-Q297):

NEW QUESTION # 292
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which change will need to be made to the pipeline when migrating to Delta Live Tables?

Answer: B

Explanation:
When migrating to Delta Live Tables (DLT) with a data pipeline that involves different programming languages across various data layers, the migration does not require unifying the pipeline into a single language. Delta Live Tables support multi-language pipelines, allowing data engineers and data analysts to work in their preferred languages, such as Python for data engineering tasks (raw, bronze, and silver layers) and SQL for data analytics tasks (gold layer). This capability is particularly beneficial in collaborative settings and leverages the strengths of each language for different stages of data processing.


NEW QUESTION # 293
An organization is building a data lakehouse and needs to ingest data from multiple sources into Unity Catalog-managed tables:
Salesforce: More than 50 objects, frequent schema changes, and OAuth authentication An on-premises SQL Server database: More than 100 tables, CDC enabled, and private network connectivity required Daily JSON files landing in Azure Data Lake Storage Gen2 The organization wants all ingested data governed by Unity Catalog, minimal engineering effort for schema changes, and serverless processing wherever possible.
Which ingestion strategy meets these requirements?

Answer: C


NEW QUESTION # 294
A data engineer is writing Spark code to group sales data by region and calculate total revenue for each region. Which Spark DataFrame transformation performs grouping operations?

Answer: B


NEW QUESTION # 295
Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

Answer: D

Explanation:
Explanation
The CREATE STREAMING LIVE TABLE syntax is used when you want to create Delta Live Tables (DLT) tables that are designed for processing data incrementally. This is typically used when your data pipeline involves streaming or incremental data updates, and you want the table to stay up to date as new data arrives.
It allows you to define tables that can handle data changes incrementally without the need for full table refreshes.


NEW QUESTION # 296
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

Answer: D

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 # 297
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