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To prepare for the certification exam, candidates can take advantage of various study resources, including online courses, study guides, and practice exams. The GAQM website also provides a detailed exam blueprint that outlines the topics and subtopics covered in the exam. Candidates can use this blueprint to create a study plan and focus their preparation efforts on the areas where they need the most improvement.

Databricks Certified Data Engineer Associate certification exam covers topics such as data engineering concepts, data ingestion, data processing, data storage, and data transformation using Apache Spark and Delta Lake. Candidates who Pass Databricks-Certified-Data-Engineer-Associate Exam will have a deep understanding of the Databricks platform and will be able to design, build, and maintain data pipelines that are scalable, reliable, and efficient. Databricks Certified Data Engineer Associate Exam certification is ideal for data engineers, data analysts, and data scientists who work with big data and want to enhance their skills and advance their careers.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
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. DELTA
E. autoloader

Answer: C

Explanation:
Explanation
CREATE TABLE new_employees_table
USING JDBC
OPTIONS (
url "<jdbc_url>",
dbtable "<table_name>",
user '<username>',
password '<password>'
) AS
SELECT * FROM employees_table_vw
https://docs.databricks.com/external-data/jdbc.html#language-sql

NEW QUESTION # 73
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: B

Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

NEW QUESTION # 74
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?

A. GRANT VIEW ON CATALOG customers TO team;
B. GRANT CREATE ON DATABASE team TO customers;
C. GRANT CREATE ON DATABASE customers TO team;
D. GRANT USAGE ON CATALOG team TO customers;
E. GRANT USAGE ON DATABASE customers TO team;

Answer: E

Explanation:
The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). References: GRANT, Privilege types, Securable objects, Principals

NEW QUESTION # 75
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(processingTime="once")
B. processingTime(1)
C. trigger(parallelBatch=True)
D. trigger(continuous="once")
E. trigger(availableNow=True)

Answer: E

Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter

NEW QUESTION # 76
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?

A. spark.delta.table
B. spark.sql
C. spark.table
D. dbutils.sql
E. spark.delta.sql

Answer: B

Explanation:
The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. Reference: Running SQL Queries Programmatically, Formatted string literals, spark.sql

NEW QUESTION # 77
......

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