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Databricks Certified Data Engineer Associate certification is highly regarded in the industry and is recognized by many employers as a mark of excellence in data engineering. Databricks Certified Data Engineer Associate Exam certification exam is designed to be challenging and requires candidates to have a deep understanding of Databricks and its various components. Candidates who pass the exam will be able to demonstrate their skills and knowledge to potential employers, giving them a competitive advantage in the job market.

One of the unique features of the Databricks Certified Data Engineer Associate certification is that it is focused on Databricks, a cloud-based big data processing platform. Databricks is becoming increasingly popular among data professionals, and the certification is designed to test individuals' knowledge of this platform. This makes the certification especially valuable for individuals who work with Databricks or plan to work with it in the future.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

Answer: B

Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/

NEW QUESTION # 93
Which file format is used for storing Delta Lake Table?

A. SV
B. JSON
C. Delta
D. Parquet

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 # 94
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?

A. The PARQUET file format does not support COPY INTO.
B. The COPY INTO statement requires the table to be refreshed to view the copied rows.
C. The previous day's file has already been copied into the table.
D. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
E. The names of the files to be copied were not included with the FILES keyword.

Answer: C

Explanation:
The COPY INTO statement is an idempotent operation, which means that it will skip any files that have already been loaded into the target table1. This ensures that the data is not duplicated or corrupted by multiple attempts to load the same file. Therefore, if the data engineer runs the same command every day without specifying the names of the files to be copied with the FILES keyword or a glob pattern with the PATTERN keyword, the statement will only copy the first file that matches the source location and ignore the rest. To avoid this problem, the data engineer should either use the FILES or PATTERN keywords to filter the files to be copied based on the date or some other criteria, or delete the files from the source location after they are copied into the table2. References: 1: COPY INTO | Databricks on AWS 2: Get started using COPY INTO to load data | Databricks on AWS

NEW QUESTION # 95
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

A. The table was external
B. The table's data was smaller than 10 GB
C. The table's data was larger than 10 GB
D. The table did not have a location
E. The table was managed

Answer: E

Explanation:
Explanation
managed tables files and metadata are managed by metastore and will be deleted when the table is dropped .
while external tables the metadata is stored in a external location. hence when a external table is dropped you clear off only the metadata and the files (data) remain.

NEW QUESTION # 96
Which of the following data workloads will utilize a Gold table as its source?

A. A job that enriches data by parsing its timestamps into a human-readable format
B. A job that cleans data by removing malformatted records
C. A job that aggregates uncleaned data to create standard summary statistics
D. A job that ingests raw data from a streaming source into the Lakehouse
E. A job that queries aggregated data designed to feed into a dashboard

Answer: E

Explanation:
A Gold table is a table that contains highly refined and aggregated data that powers analytics, machine learning, and production applications. It represents data that has been transformed into knowledge, rather than just information. A Gold table is typically the final output of a medallion lakehouse architecture, where data flows from Bronze to Silver to Gold tables, with each layer improving the structure and quality of data. A job that queries aggregated data designed to feed into a dashboard is an example of a data workload that will utilize a Gold table as its source, as it requires data that is ready for consumption and analysis. The other options are either data workloads that will use a Bronze or Silver table as their source, or data workloads that will produce a Gold table as their output. Reference: Databricks Documentation - What is the medallion lakehouse architecture?, Databricks Documentation - What is a Medallion Architecture?, K21Academy - Delta Lake Architecture & Azure Databricks Workspace.

NEW QUESTION # 97
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