Databricks passguarantee databricks Certification Databricks-certified-data-engineer-associate Updated Exam by Katie q27 vce pdf

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Exam Name:	Databricks Certified Data Engineer Associate Exam
Exam Code:	Databricks-Certified-Data-Engineer-Associate Dumps
Vendor:	Databricks	Certification:	Databricks Certification
Questions:	159 Q&A's	Shared By:	katie

Question 12

A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.

The cade block used by the data engineer is below:

Questions 12

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

Options:

trigger("5 seconds")

trigger()

trigger(once="5 seconds")

trigger(processingTime="5 seconds")

trigger(continuous="5 seconds")

Discussion

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Question 13

Which SQL code snippet will correctly demonstrate a Data Definition Language (DDL) operation used to create a table?

Options:

DROP TABLE employees;

INSERT INTO employees (id, name) VALUES (1, 'Alice');

CRFATF tabif employees ( id INT, name suing

ALTFR TABIF employees add column salary DECTMA(10,2);

Discussion

Question 14

A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.

Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

Options:

None of these changes will need to be made

The pipeline will need to stop using the medallion-based multi-hop architecture

The pipeline will need to be written entirely in SQL

The pipeline will need to use a batch source in place of a streaming source

The pipeline will need to be written entirely in Python

Discussion

Question 15

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

Options:

When they are working interactively with a small amount of data

When they are running automated reports to be refreshed as quickly as possible

When they are working with SQL within Databricks SQL

When they are concerned about the ability to automatically scale with larger data

When they are manually running reports with a large amount of data

Discussion

Answer:

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.

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]

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