Databricks selftestengine new Release Databricks-certified-data-engineer-associate Databricks Certification Questions by Nala q36 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:	230 Q&A's	Shared By:	nala

Question 60

Identify the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE for a constraint violation.

A data engineer has created an ETL pipeline using Delta Live table to manage their company travel reimbursement detail, they want to ensure that the if the location details has not been provided by the employee, the pipeline needs to be terminated.

How can the scenario be implemented?

Options:

CONSTRAINT valid_location EXPECT (location = NULL)

CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL UPDATE

CONSTRAINT valid_location EXPECT (location != NULL) ON DROP ROW

CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL

Discussion

Question 61

Identify how the count_if function and the count where x is null can be used

Consider a table random_values with below data.

What would be the output of below query?

select count_if(col > 1) as count_a. count(*) as count_b.count(col1) as count_c from random_values col1

NULL -

Options:

3 6 5

4 6 5

3 6 6

4 6 6

Discussion

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Question 62

Identify a scenario to use an external table.

A Data Engineer needs to create a parquet bronze table and wants to ensure that it gets stored in a specific path in an external location.

Which table can be created in this scenario?

Options:

An external table where the location is pointing to specific path in external location.

An external table where the schema has managed location pointing to specific path in external location.

A managed table where the catalog has managed location pointing to specific path in external location.

A managed table where the location is pointing to specific path in external location.

Discussion

Question 63

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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