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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is designed for data engineers who want to demonstrate their expertise in building and managing data pipelines using Databricks. Databricks is a cloud-based platform that allows data engineers to easily build and manage data pipelines using Apache Spark. Databricks Certified Data Engineer Associate Exam certification exam covers a wide range of topics related to data engineering, including data pipeline architecture, data modeling, data ingestion, data processing, and data storage.

The Databricks Databricks-Certified-Data-Engineer-Associate Exam is designed for data professionals who have at least two years of experience working with big data and data engineering. Databricks-Certified-Data-Engineer-Associate exam is challenging and requires a deep understanding of data engineering concepts and best practices. However, individuals who pass the exam will have a highly respected certification that can help them advance their careers and increase their earning potential.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?

A. They can set up an Alert with one-time notifications.
B. They can set up an Alert with a new email alert destination.
C. They can set up an Alert without notifications.
D. They can set up an Alert with a new webhook alert destination.
E. They can set up an Alert with a custom template.

Answer: D

Explanation:
Explanation
To achieve this, the data engineer can set up an Alert in the Databricks workspace that triggers when the query results exceed the threshold of 100 NULL values. They can create a new webhook alert destination in the Alert's configuration settings and provide the necessary messaging webhook URL to receive notifications.
When the Alert is triggered, it will send a message to the configured webhook URL, which will then notify the entire team of the issue.

NEW QUESTION # 42
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
C. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
D. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.

Answer: E

Explanation:
In Production mode, the pipeline runs continuously and updates the output tables whenever new data is available in the input sources. The compute resources are allocated on demand and released when the pipeline is stopped. This mode is suitable for production workloads that require high availability and reliability. References: Configure pipeline settings for Delta Live Tables, Tutorial: Run your first Delta Live Tables pipeline, Building Reliable Data Pipelines Using DataBricks' Delta Live Tables

NEW QUESTION # 43
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace format("delta") with format("stream")
B. Replace spark.read with spark.readStream
C. Replace "transactions" with the path to the location of the Delta table
D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
E. Replace predict with a stream-friendly prediction function

Answer: B

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 44
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 USAGE ON CATALOG team TO customers;
B. GRANT VIEW ON CATALOG customers TO team;
C. GRANT USAGE ON DATABASE customers TO team;
D. GRANT CREATE ON DATABASE customers TO team;

Answer: C

NEW QUESTION # 45
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

A. Parquet files will become Delta tables
B. Parquet files have the ability to be optimized
C. Parquet files can be partitioned
D. Parquet files have a well-defined schema
E. CREATE TABLE AS SELECT statements cannot be used on files

Answer: D

Explanation:
Explanation
https://www.databricks.com/glossary/what-is-parquet#:~:text=Columnar%20storage%20like%20Apache%20Par Columnar storage like Apache Parquet is designed to bring efficiency compared to row-based files like CSV.
When querying, columnar storage you can skip over the non-relevant data very quickly. As a result, aggregation queries are less time-consuming compared to row-oriented databases.

NEW QUESTION # 46
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