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| Section | Objectives |
|---|---|
| Databricks Lakehouse Platform Fundamentals | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage |
| Productionizing Data Pipelines | - Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs - Pipeline deployment and operationalization |
| Data Processing and Transformations | - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) - PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering) |
| Data Governance and Quality | - Data access control and governance - Unity Catalog basics - Data quality concepts and management |
| Data Ingestion and ELT Development | - ETL patterns and transformations - Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data |
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NEW QUESTION # 142
A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job's current run. The data engineer asks a tech lead for help in identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?
Answer: A
Explanation:
The Tasks tab in the Jobs UI shows the list of tasks that are part of a job, and allows the user to view the details of each task, such as the notebook path, the cluster configuration, the run status, and the duration. By clicking on the active run of a task, the user can access the Spark UI, the notebook output, and the logs of the task. These can help the user to identify the performance bottlenecks and errors in the task. The Runs tab in the Jobs UI only shows the summary of the job runs, such as the start time, the end time, the trigger, and the status. It does not provide the details of the individual tasks within a job run. Reference: Jobs UI, Monitor running jobs with a Job Run dashboard, How to optimize jobs performance
NEW QUESTION # 143
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?
Answer: A
Explanation:
A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches
100. To set up a webhook alert destination, the data engineer needs to do the following steps:
* In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.
* Click Alert Destinations and click Add New Alert Destination.
* Select Webhook and enter the webhook URL and the optional custom template for the notification message.
* Click Create to save the webhook alert destination.
* In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.
* Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.
* In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. References: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with
* Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.
NEW QUESTION # 144
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?
Answer: A
NEW QUESTION # 145
Which of the following commands will return the location of database customer360?
Answer: D
NEW QUESTION # 146
A data engineer needs to combine sales data from an on-premises PostgreSQL database with customer data in Azure Synapse for a comprehensive report. The goal is to avoid data duplication and ensure up-to-date information How should the data engineer achieve this using Databricks?
Answer: B
NEW QUESTION # 147
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