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The Databricks Databricks-Certified-Data-Engineer-Associate exam is a 90-minute online exam that consists of 60 multiple-choice questions. Databricks-Certified-Data-Engineer-Associate exam covers various topics such as data ingestion, data transformation, data modeling, data warehousing, and data analysis. Databricks-Certified-Data-Engineer-Associate Exam is designed to validate the candidate's ability to work with Databricks in a production environment.

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Databricks Certified Data Engineer Associate certification is an essential certification for individuals who want to pursue a career in data engineering. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics and is designed to ensure that candidates have the necessary skills and knowledge to build and maintain large-scale data pipelines. By earning this certification, candidates can demonstrate their expertise in data engineering and increase their chances of getting hired by top companies in the data and AI industry.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q296-Q301):

NEW QUESTION # 296
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. Data Explorer
B. Delta Lake
C. Unity Catalog
D. Auto Loader
E. Delta Live Tables

Answer: E

NEW QUESTION # 297
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

A. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
B. They can clone the existing task in the existing Job and update it to run the new notebook.
C. They can create a new task in the existing Job and then add it as a dependency of the original task.
D. They can clone the existing task to a new Job and then edit it to run the new notebook.
E. They can create a new job from scratch and add both tasks to run concurrently.

Answer: C

NEW QUESTION # 298
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE 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 dropped from the target dataset and recorded as invalid in the event log.
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 added to the target dataset and flagged as invalid in a field added to the target dataset.
D. Records that violate the expectation cause the job to fail.

Answer: A

Explanation:
The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset.
Option A is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION DROP RECORD option should be used.
Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the ON VIOLATION QUARANTINE RECORD option should be used.
Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION LOG RECORD option should be used.
Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the target dataset, the ON VIOLATION FLAG RECORD option should be used.
Delta Live Tables Expectations
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 299
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?

A. Checkpointing and Write-ahead Logs
B. Checkpointing and Idempotent Sinks
C. Structured Streaming cannot record the offset range of the data being processed in each trigger.
D. Replayable Sources and Idempotent Sinks
E. Write-ahead Logs and Idempotent Sinks

Answer: A

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 # 300
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

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

Answer: C

Explanation:
A webhook alert destination is a notification destination that allows Databricks to send HTTP POST requests to a third-party endpoint when an alert is triggered. This enables the data engineer to integrate Databricks alerts with their preferred messaging or collaboration platform, such as Slack, Microsoft Teams, or PagerDuty. To set up a webhook alert destination, the data engineer needs to create and configure a webhook connector in their messaging platform, and then add the webhook URL to the Databricks notification destination. After that, the data engineer can create an alert for their Databricks SQL query, and select the webhook alert destination as the notification destination. The alert can be configured with a custom condition, such as when the number of stores with $0 in sales is greater than zero, and a custom message template, such as "Alert: {number_of_stores} stores have $0 in sales". The alert can also be configured with a recurrence interval, such as every hour, to check the query result periodically. When the alert condition is met, the data engineer and their team will receive a notification via the messaging webhook, with the custom message and a link to the Databricks SQL query. The other options are either not suitable for sending notifications via a messaging webhook (A, B, E), or not suitable for sending recurring notifications . References: Databricks Documentation - Manage notification destinations, Databricks Documentation - Create alerts for Databricks SQL queries, Databricks Documentation - Configure alert conditions and messages.

NEW QUESTION # 301
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