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Becoming a Databricks Certified Data Engineer Associate provides several benefits to candidates. Firstly, it validates their skills and knowledge in data engineering, making them more competitive in the job market. Secondly, it demonstrates their commitment to staying current with the latest technologies and their dedication to professional development. Finally, it provides them with access to a community of Databricks-certified professionals, which can help them network and collaborate with other data engineers.

Databricks-Certified-Data-Engineer-Associate Exam tests the candidates' abilities to perform the necessary tasks needed to become a successful data engineer. Databricks-Certified-Data-Engineer-Associate Exam covers a wide range of topics such as data extraction, transformation, and loading (ETL), data modeling, data warehousing, and machine learning. Individuals who have passed Databricks-Certified-Data-Engineer-Associate exam possess the professional knowledge necessary to integrate, manipulate, secure, and monitor data within Databricks.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a challenging and highly respected certification for data professionals. It is designed to test individuals' knowledge and skills in data engineering, with a focus on the Databricks platform. Individuals who pass the exam will have a valuable certification that can help them advance their careers and increase their earning potential.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q227-Q232):

NEW QUESTION # 227
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

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

A. org.apache.spark.sql.sqlite
B. sqlite
C. org.apache.spark.sql.jdbc
D. autoloader
E. DELTA

Answer: B

Explanation:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver

NEW QUESTION # 228
A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id -
FROM STREAM(LIVE.customers)
WHERE loyalty_level = 'high';
Which of the following describes why the STREAM function is included in the query?

A. The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.
B. The table being created is a live table.
C. The data in the customers table has been updated since its last run.
D. The customers table is a streaming live table.
E. The STREAM function is not needed and will cause an error.

Answer: D

Explanation:
The STREAM function is used to process data from a streaming live table or view, which is a table or view that contains data that has been added only since the last pipeline update. Streaming live tables and views are stateful, meaning that they retain the state of the previous pipeline run and only process new data based on the current query. This is useful for incremental processing of streaming or batch data sources. The customers table in the query is a streaming live table, which means that it contains the latest data from the source. The STREAM function enables the query to read the data from the customers table incrementally and create another streaming live table named loyal_customers, which contains the customer IDs of the customers with high loyalty level. Reference: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.

NEW QUESTION # 229
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON
VIOLATION DROP ROW
What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
B. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
C. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
D. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
E. Records that violate the expectation cause the job to fail.

Answer: C

NEW QUESTION # 230
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?

A. The pipeline will need to be written entirely in SQL.
B. The pipeline will need to use a batch source in place of a streaming source.
C. The pipeline can have different notebook sources in SQL & Python.
D. The pipeline will need to be written entirely in Python.

Answer: C

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 # 231
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?

A. Unity Catalog
B. Delta Lake
C. Auto Loader
D. Data Explorer
E. Delta Live Tables

Answer: E

Explanation:
Explanation
https://docs.databricks.com/delta-live-tables/expectations.html
Delta Live Tables is a tool provided by Databricks that can help data engineers automate the monitoring of data quality. It is designed for managing data pipelines, monitoring data quality, and automating workflows.
With Delta Live Tables, you can set up data quality checks and alerts to detect issues and anomalies in your data as it is ingested and processed in real-time. It provides a way to ensure that the data quality meets your desired standards and can trigger actions or notifications when issues are detected. While the other tools mentioned may have their own purposes in a data engineeringenvironment, Delta Live Tables is specifically designed for data quality monitoring and automation within the Databricks ecosystem.

NEW QUESTION # 232
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