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Databricks Certified Data Engineer Associate Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

A. Both teams would autoscale their work as data size evolves
B. Both teams would respond more quickly to ad-hoc requests
C. Both teams would use the same source of truth for their work
D. Both teams would reorganize to report to the same department
E. Both teams would be able to collaborate on projects in real-time

Answer: C

Explanation:
Explanation
A data lakehouse is designed to unify the data engineering and data analysis architectures by integrating features of both data lakes and data warehouses. One of the key benefits of a data lakehouse is that it provides a common, centralized data repository (the "lake") that serves as a single source of truth for data storage and analysis. This allows both data engineering and data analysis teams to work with the same consistent data sets, reducing discrepancies and ensuring that the reports generated by both teams are based on the same underlying data.

NEW QUESTION # 110
Which tool is used by Auto Loader to process data incrementally?

A. Unity Catalog
B. Checkpointing
C. Databricks SQL
D. Spark Structured Streaming

Answer: D

Explanation:
Auto Loader in Databricks utilizes Spark Structured Streaming for processing data incrementally. This allows Auto Loader to efficiently ingest streaming or batch data at scale and to recognize new data as it arrives in cloud storage. Spark Structured Streaming provides the underlying engine that supports various incremental data loading capabilities like schema inference and file notification mode, which are crucial for the dynamic nature of data lakes.
Reference:
Databricks documentation on Auto Loader: Auto Loader Overview

NEW QUESTION # 111
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?

A. Workspace administrator
B. Databricks account representative
C. New lead data engineer
D. Original data engineer
E. This transfer is not possible

Answer: A

Explanation:
Explanation
https://docs.databricks.com/sql/admin/transfer-ownership.html

NEW QUESTION # 112
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
B. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.

Answer: E

Explanation:
The Continuous Pipeline Mode for Delta Live Tables allows the pipeline to run continuously and process data as it arrives. This mode is suitable for streaming ingest and CDC workloads that require low-latency updates. The Development mode for Delta Live Tables allows the pipeline to run on a dedicated cluster that is not shared with other pipelines. This mode is useful for testing and debugging the pipeline logic before deploying it to production. Therefore, the correct answer is B, because the pipeline will run continuously on a dedicated cluster until it is manually stopped, and the compute resources will be released only after the pipeline is shut down. Reference: Databricks Documentation - Configure pipeline settings for Delta Live Tables, Databricks Documentation - Continuous vs. triggered pipeline execution, Databricks Documentation - Development vs. production mode.

NEW QUESTION # 113
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?

A. When they are working with SQL within Databricks SQL
B. When they are running automated reports to be refreshed as quickly as possible
C. When they are working interactively with a small amount of data
D. When they are manually running reports with a large amount of data
E. When they are concerned about the ability to automatically scale with larger data

Answer: C

Explanation:
The scenario in which a data engineer will want to use a single-node cluster is when they are working interactively with a small amount of data. A single-node cluster is a cluster consisting of an Apache Spark driver and no Spark workers1. A single-node cluster supports Spark jobs and all Spark data sources, including Delta Lake1. A single-node cluster is helpful for single-node machine learning workloads that use Spark to load and save data, and for lightweight exploratory data analysis1. A single-node cluster can run Spark locally, spawn one executor thread per logical core in the cluster, and save all log output in the driver log1. A single-node cluster can be created by selecting the Single Node button when configuring a cluster1.
The other options are not suitable for using a single-node cluster. When running automated reports to be refreshed as quickly as possible, a data engineer will want to use a multi-node cluster that can scale up and down automatically based on the workload demand2. When working with SQL within Databricks SQL, a data engineer will want to use a SQL Endpoint that can execute SQL queries on a serverless pool or an existing cluster3. When concerned about the ability to automatically scale with larger data, a data engineer will want to use a multi-node cluster that can leverage the Databricks Lakehouse Platform and the Delta Engine to handle large-scale data processing efficiently and reliably4. When manually running reports with a large amount of data, a data engineer will want to use a multi-node cluster that can distribute the computation across multiple workers and leverage the Spark UI to monitor the performance and troubleshoot the issues.
References:
* 1: Single Node clusters | Databricks on AWS
* 2: Autoscaling | Databricks on AWS
* 3: SQL Endpoints | Databricks on AWS
* 4: Databricks Lakehouse Platform | Databricks on AWS
* : [Spark UI | Databricks on AWS]

NEW QUESTION # 114
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