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Databricks Certified Data Engineer Associate exam consists of 60 multiple-choice questions that must be completed within 90 minutes. Candidates are required to score at least 70% to pass the exam. Databricks-Certified-Data-Engineer-Associate Exam is administered online through the GAQM testing platform, which allows candidates to take the exam from anywhere in the world.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q252-Q257):

NEW QUESTION # 252
A data engineer streams customer orders into a Kafka topic (orders_topic) and is currently writing the ingestion script of a DLT pipeline. The data engineer needs to ingest the data from Kafka brokers to DLT using Databricks What is the correct code for ingesting the data?

Answer: A


NEW QUESTION # 253
A new data engineering team team. has been assigned to an ELT project. The new data engineering team will need full privileges on the database customers to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

Answer: E

Explanation:
To grant full permissions on a database to a user, group, or service principal, the GRANT ALL PRIVILEGES ON DATABASE command can be used. This command grants all the applicable privileges on the database, such as CREATE, SELECT, MODIFY, and USAGE. The other options are either incorrect or incomplete, as they do not grant all the privileges or specify the wrong database or principal. Reference:
GRANT
Privileges


NEW QUESTION # 254
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?

Answer: E

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 # 255
A data engineering team must version-control notebooks, review changes through pull requests, and deploy the same code to dev, staging, and production workspaces.
Which Databricks capability supports this workflow?

Answer: D

Explanation:
Databricks Git folders provide native integration with providers such as GitHub, GitLab, Azure DevOps, and Bitbucket, supporting branching, commits, pulls, and pull-request workflows.
Combined with Databricks Asset Bundles or job Git sources, the same repository can be deployed across environments.


NEW QUESTION # 256
A data engineer uploads a CSV file using the Create or modify a table using file upload option in Databricks. To avoid incorrect schema inference, the engineer disables Automatically detect column types before creating a Unity Catalog-managed table.
What is the outcome?

Answer: A

Explanation:
When Automatically detect column types is disabled during file upload, Databricks does not infer specialized data types from the CSV values. Instead, every uploaded column is created as STRING. This behavior prevents values that resemble numbers, dates, or Boolean values from being automatically assigned potentially incorrect types. The engineer can subsequently transform or cast individual columns into the required target types as part of a controlled Silver-layer process. Option B is incorrect because partial numeric inference does not occur when automatic detection is disabled. Option C is incorrect because Databricks can create the table without manually supplied types by using STRING for every column. Option D is also incorrect because disabling detection determines the schema behavior for the table creation operation. Therefore, option A accurately describes the result.


NEW QUESTION # 257
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