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Which of the following describes the type of workloads that are always compatible with Auto Loader?
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B
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Explanation
Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. References: What is Auto Loader?, Delta Live Tables |
Which of the following benefits is provided by the array functions from Spark SQL?
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D
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Explanation
The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. References: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog] |
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?
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A
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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, : [SUM | Databricks on AWS], ]
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Find answers to the most common questions about the Databricks Databricks-Certified-Data-Engineer-Associate exam, including what it is, how to prepare, and how it can boost your career.
The Databricks Databricks-Certified-Data-Engineer-Associate certification is a globally-acknowledged credential that is awarded to candidates who pass this certification exam by obtaining the required passing score. This credential attests and validates the candidates' knowledge and hands-on skills in domains covered in the Databricks Databricks-Certified-Data-Engineer-Associate certification syllabus. The Databricks Databricks-Certified-Data-Engineer-Associate certified professionals with their verified proficiency and expertise are trusted and welcomed by hiring managers all over the world to perform leading roles in organizations. The success in Databricks Databricks-Certified-Data-Engineer-Associate certification exam can be ensured only with a combination of clear knowledge on all exam domains and securing the required practical training. Like any other credential, Databricks Databricks-Certified-Data-Engineer-Associate certification may require periodic renewal to stay current with new innovations in the concerned domains.