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The Databricks Databricks-Certified-Data-Engineer-Associate exam has 50 multiple-choice questions, and the candidate has 90 minutes to complete it. It is an online exam that can be taken at any time and from anywhere in the world. The questions in the exam are designed to test the candidate's ability to handle real-world data engineering challenges. Databricks-Certified-Data-Engineer-Associate Exam also tests the candidate's understanding of the Databricks platform and how it can be used to solve data engineering problems.
NEW QUESTION # 74
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
Answer: C
Explanation:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver
NEW QUESTION # 75
A data engineer is processing ingested streaming tables and needs to filter out NULL values in the order_datetime column from the raw streaming table orders_raw and store the results in a new table orders_valid using DLT.
Which code snippet should the data engineer use?
Answer: C
NEW QUESTION # 76
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?
Answer: B
NEW QUESTION # 77
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: E
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. References: Jobs UI, Monitor running jobs with a Job Run dashboard, How to optimize jobs performance
NEW QUESTION # 78
A data engineer is migrating pipeline tasks to reduce operational toil. The workspace uses Unity Catalog and is in a region that supports serverless. The engineer wants Databricks to auto-select instance types, manage scaling, apply Photon, and handle runtime upgrades automatically for job runs.
How should the data engineer meet this requirement while adhering to Databricks constraints?
Answer: A
Explanation:
Serverless compute for workflows is designed to minimize operational overhead for Databricks Jobs while enforcing Unity Catalog governance. When a job is configured to run on serverless compute (in supported regions), Databricks automatically selects instance types, manages autoscaling, enables Photon where applicable, and handles runtime upgrades without user intervention. This directly satisfies the requirement to reduce operational toil. All-purpose clusters (option B) and traditional job clusters (option C) still require users to manage cluster sizing, instance families, and runtime versions, which contradicts the goal of automation. SQL warehouses (option A) are intended for SQL workloads and do not support running general Python notebook job tasks as workflow steps. Databricks documentation specifies that serverless workflows are compatible with Unity Catalog and are the recommended approach when users want fully managed compute for scheduled pipelines and tasks, provided the workspace is in a supported region. This makes serverless compute the correct and fully compliant solution.
NEW QUESTION # 79
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