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Achieving the Databricks Certified Data Engineer Associate certification demonstrates that an individual has the skills and knowledge needed to work with big data, data engineering, and distributed systems using Databricks. Databricks Certified Data Engineer Associate Exam certification is recognized globally and can help professionals advance their careers in data engineering.

The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is designed to test an individual's knowledge and skills in using Databricks for data engineering tasks. Databricks Certified Data Engineer Associate Exam certification is highly recognized and respected in the industry and can be used as a benchmark for recruiters and employers to assess the candidate's proficiency in data engineering with Databricks.

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Databricks Certified Data Engineer Associate Exam covers a wide range of topics, including data ingestion, data transformation, data storage, data processing, and data analysis. It is designed to test the candidate's ability to work with a variety of data sources and formats, including structured, semi-structured, and unstructured data. It also tests the candidate's knowledge of data engineering best practices, such as data quality, data governance, and data security.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q100-Q105):

NEW QUESTION # 100
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:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

Answer: A


NEW QUESTION # 101
A data engineer is processing ingested streaming tables and needs to filter out NULL values in the order_datetime column from the raw streaming table orders_raw and store the results in a new table orders_valid using DLT. Which code snippet should the data engineer use?

Answer: A

Explanation:
The correct approach is to use a DLT expectation with constraint CONSTRAINT valid_date EXPECT (order_datetime IS NOT NULL) ON VIOLATION DROP ROW This ensures rows with NULL order_datetime are filtered out automatically during streaming ingestion while keeping the pipeline valid.


NEW QUESTION # 102
In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?

Answer: D

Explanation:
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. This ensures that the engine can reliably track the exact progress of the processing and handle any kind of failure by restarting and/or reprocessing. Checkpointing is the mechanism of saving the state of a streaming query to fault-tolerant storage (such as HDFS) so that it can be recovered after a failure. Write-ahead logs are files that record the offset range of the data being processed in each trigger and are written to the checkpoint location before the processing starts. These logs are used to recover the query state and resume processing from the last processed offset range in case of a failure. Reference: Structured Streaming Programming Guide, Fault Tolerance Semantics


NEW QUESTION # 103
Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?

Answer: E

Explanation:
To send the Databricks Job owner an email in the case that the Job fails, the best approach is to set up an Alert in the Job page. This way, the Job owner can configure the email address and the notification type for the Job failure event. The other options are either not feasible, not reliable, or not relevant for this task. Manually programming an alert system in each cell of the Notebook is tedious and error-prone. Setting up an Alert in the Notebook is not possible, as Alerts are only available for Jobs and Clusters. There is a way to notify the Job owner in the case of Job failure, so option D is incorrect. MLflow Model Registry Webhooks are used for model lifecycle events, not Job events, so option E is not applicable. Reference:
Add email and system notifications for job events
Alerts
MLflow Model Registry Webhooks


NEW QUESTION # 104
A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location.
Which of the following data entities should the data engineer create?

Answer: D


NEW QUESTION # 105
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