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Databricks Certified Data Engineer Associate Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?

A. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
B. The COPY INTO statement requires the table to be refreshed to view the copied rows.
C. The previous day's file has already been copied into the table.
D. The PARQUET file format does not support COPY INTO.
E. The names of the files to be copied were not included with the FILES keyword.

Answer: C

Explanation:
The COPY INTO statement is an idempotent operation, which means that it will skip any files that have already been loaded into the target table1. This ensures that the data is not duplicated or corrupted by multiple attempts to load the same file. Therefore, if the data engineer runs the same command every day without specifying the names of the files to be copied with the FILES keyword or a glob pattern with the PATTERN keyword, the statement will only copy the first file that matches the source location and ignore the rest. To avoid this problem, the data engineer should either use the FILES or PATTERN keywords to filter the files to be copied based on the date or some other criteria, or delete the files from the source location after they are copied into the table2. References: 1: COPY INTO | Databricks on AWS 2: Get started using COPY INTO to load data | Databricks on AWS

NEW QUESTION # 46
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 without notifications.
B. They can set up an Alert with one-time notifications.
C. They can set up an Alert with a new webhook alert destination.
D. They can set up an Alert with a new email alert destination.
E. They can set up an Alert with a custom template.

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 # 47
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?

A. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
B. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
C. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
D. They can schedule the query to run every 12 hours from the Jobs UI.
E. They can schedule the query to run every 1 day from the Jobs UI.

Answer: B

Explanation:
Databricks SQL allows users to schedule queries to run automatically at a specified frequency and time zone.
This can help users to keep their dashboards or alerts updated with the latest data. To schedule a query, users need to do the following steps:
* In the Query Editor, click Schedule > Add schedule to open a menu with schedule settings.
* Choose when to run the query. Use the dropdown pickers to specify the frequency, period, starting time, and time zone. Optionally, select the Show cron syntax checkbox to edit the schedule in Quartz Cron Syntax.
* Choose More options to show optional settings. Users can also choose a name for the schedule, and a SQL warehouse to power the query.
* Click Create. The query will run automatically according to the schedule.
The other options are incorrect because they do not refer to the correct location or frequency to schedule the query. The query's page in Databricks SQL is the place where users can edit, run, or schedule the query. The SQL endpoint's page in Databricks SQL is the place where users can manage the SQL warehouses and SQL endpoints. The Jobs UI is the place where users can create, run, or schedule jobs that execute notebooks, JARs, or Python scripts. References: Schedule a query, What are Databricks SQL alerts?, Jobs.

NEW QUESTION # 48
Which of the following describes the relationship between Bronze tables and raw data?

A. Bronze tables contain aggregates while raw data is unaggregated.
B. Bronze tables contain raw data with a schema applied.
C. Bronze tables contain a less refined view of data than raw data.
D. Bronze tables contain less data than raw data files.
E. Bronze tables contain more truthful data than raw data.

Answer: B

Explanation:
Explanation
The Bronze layer is where we land all the data from external source systems. The table structures in this layer correspond to the source system table structures "as-is," along with any additional metadata columns that capture the load date/time, process ID, etc. The focus in this layer is quick Change Data Capture and the ability to provide an historical archive of source (cold storage), data lineage, auditability, reprocessing if needed without rereading the data from the source system.https://www.databricks.com/glossary/medallion-architecture#:~:text=Bronze%20layer%20%28raw%20d

NEW QUESTION # 49
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 recorded as invalid in the event log.
B. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
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 cause the job to fail.
E. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.

Answer: A

NEW QUESTION # 50
......

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