Databricks-Certified-Professional-Data-Engineer Exam Dumps

This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Structured Streaming'' section;Databricks Documentation, under ''Checkpointing'' section.

DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems1.DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources1.By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container2.Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting3.Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled4.The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace1.Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted3.Reference:DBFS,Work with files on Azure Databricks,Mounting cloud object storage on Azure Databricks,Access DBFS with FUSE

This is the correct answer because it is the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data. Notebook permissions are used to control access to notebooks in Databricks workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can Run, and Can Read. Can Manage allows full control over the notebook, including editing, running, deleting, exporting, and changing permissions. Can Edit allows modifying and running the notebook, but not changing permissions or deleting it. Can Run allows executing commands in an existing cluster attached to the notebook, but not modifying or exporting it. Can Read allows viewing the notebook content, but not running or modifying it. In this case, granting Can Read permission to the user will allow them to review the production logic in the notebook without allowing them to make any changes to it or run any commands that may affect production data. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Databricks Workspace'' section; Databricks Documentation, under ''Notebook permissions'' section.

When joining a stream of advertisement impressions with a stream of user clicks, you want to minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition that clickTime == impressionTime, which is suitable for correlating events that occur at the exact same time. However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It's important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that's unlikely to match with new data.

https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html

The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use theDatabricks Connectfeature to run your Python code on a remote Databricks cluster, or you can use theSpark Git Connectorto read data from Git repositories as Spark DataFrames.