Databricks Databricks-Certified-Data-Engineer-Associate Exam Dumps - Valid Questions Answers

 Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.

References: CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV

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.

The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network. References: Databricks architecture overview, Security and Trust CenterQUESTION NO: 4

Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

A. The ability to manipulate the same data using a variety of languages

B. The ability to collaborate in real time on a single notebook

C. The ability to set up alerts for query failures

D. The ability to support batch and streaming workloads

E. The ability to distribute complex data operations

Answer: D

Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. References: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS]

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