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Latest Databricks Databricks-Certified-Data-Engineer-Associate Practice Questions - Databricks-Certified-Data-Engineer-Associate Dump Torrent

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The Databricks Databricks-Certified-Data-Engineer-Associate Exam consists of 60 multiple-choice questions and has a duration of 120 minutes. The topics covered in the exam include Databricks architecture, data ingestion, data processing, data transformation, data modeling, and data warehousing. Databricks-Certified-Data-Engineer-Associate exam is intended for data engineers, data architects, data analysts, and data scientists who have experience in working with Databricks for data engineering tasks. By obtaining this certification, candidates can demonstrate their expertise in Databricks and enhance their career prospects in the field of data engineering.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
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 at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
C. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
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 # 14
Which of the following benefits is provided by the array functions from Spark SQL?

A. An ability to work with time-related data in specified intervals
B. An ability to work with an array of tables for procedural automation
C. An ability to work with data in a variety of types at once
D. An ability to work with complex, nested data ingested from JSON files
E. An ability to work with data within certain partitions and windows

Answer: D

Explanation:
The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. References: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog]

NEW QUESTION # 15
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can turn on the Auto Stop feature for the SQL endpoint.
B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
C. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
D. They can set up the dashboard's SQL endpoint to be serverless.
E. They can reduce the cluster size of the SQL endpoint.

Answer: D

Explanation:
A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API. References:
* Create a serverless SQL endpoint
* Assign a SQL endpoint to a dashboard
* SQL Analytics API

NEW QUESTION # 16
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?

A. dbfs:/user/hive/database/customer360
B. dbfs:/user/hive/customer360
C. dbfs:/user/hive/warehouse
D. More information is needed to determine the correct response

Answer: C

Explanation:
Explanation
dbfs:/user/hive/warehouse - which is the default location

NEW QUESTION # 17
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace "transactions" with the path to the location of the Delta table
B. Replace predict with a stream-friendly prediction function
C. Replace spark.read with spark.readStream
D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
E. Replace format("delta") with format("stream")

Answer: C

Explanation:
1: To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. Reference: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 18
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