Home > Databricks-Certified-Data-Engineer-Associate / Databricks

[Oct 12, 2024] Databricks-Certified-Data-Engineer-Associate PDF Recently Updated Questions Dumps to Improve Exam Score [Q30-Q45]

Published date October 12, 2024

Rate this post

[Oct 12, 2024] Databricks-Certified-Data-Engineer-Associate PDF Recently Updated Questions Dumps to Improve Exam Score

Databricks-Certified-Data-Engineer-Associate Dumps Full Questions with Free PDF Questions to Pass

The GAQM Databricks-Certified-Data-Engineer-Associate exam is a comprehensive test of an individual’s knowledge of Databricks and its related technologies. It evaluates the ability of candidates to design, build, and maintain data pipelines using Databricks. Databricks Certified Data Engineer Associate Exam certification is ideal for data engineers, data architects, and data scientists who want to showcase their skills and knowledge in working with Databricks. Earning the certification can help individuals advance their careers and lead to better job opportunities and higher salaries.

NO.30 Which of the following data lakehouse features results in improved data quality over a traditional data lake?

A data lakehouse provides storage solutions for structured and unstructured data.

A data lakehouse supports ACID-compliant transactions.

A data lakehouse allows the use of SQL queries to examine data.

A data lakehouse stores data in open formats.

A data lakehouse enables machine learning and artificial Intelligence workloads.

Explanation
One of the key features of a data lakehouse that results in improved data quality over a traditional data lake is its support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. ACID transactions provide data integrity and consistency guarantees, ensuring that operations on the data are reliable and that data is not left in an inconsistent state due to failures or concurrent access. In a traditional data lake, such transactional guarantees are often lacking, making it challenging to maintain data quality, especially in scenarios involving multiple data writes, updates, or complex transformations. A data lakehouse, by offering ACID compliance, helps maintain data quality by providing strong consistency and reliability, which is crucial for data pipelines and analytics.

NO.31 A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

They can set up separate expectations for each table when developing their DLT pipeline.

They cannot determine which table is dropping the records.

They can set up DLT to notify them via email when records are dropped.

They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.

They can navigate to the DLT pipeline page, click on the “Error” button, and review the present errors.

One of the features of DLT is that it provides data quality metrics for each dataset in the pipeline, such as the number of records that pass or fail expectations, the number of records that are dropped, and the number of records that are written to the target. These metrics can be accessed from the DLT pipeline page, where the data engineer can click on each table and view the data quality statistics for the latest update or any previous update. This way, they can identify which table is dropping the records and why. Reference:
Monitor Delta Live Tables pipelines
Manage data quality with Delta Live Tables

NO.32 Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

DROP

IGNORE

MERGE

APPEND

INSERT

The MERGE command can be used to upsert data from a source table, view, or DataFrame into a target Delta table. It allows you to specify conditions for matching and updating existing records, and inserting new records when no match is found. This way, you can avoid writing duplicate records into a Delta table1. The other commands (DROP, IGNORE, APPEND, INSERT) do not have this functionality and may result in duplicate records or data loss234. References: 1: Upsert into a Delta Lake table using merge | Databricks on AWS 2: SQL DELETE | Databricks on AWS 3: SQL INSERT INTO | Databricks on AWS 4: SQL UPDATE | Databricks on AWS

NO.33 A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

None of these lines of code are needed to successfully complete the task

USING CSV

FROM CSV

USING DELTA

FROM “path/to/csv”

A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. References: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

NO.34 A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?

They can set up an Alert with a custom template.

They can set up an Alert with a new email alert destination.

They can set up an Alert with a new webhook alert destination.

They can set up an Alert with one-time notifications.

They can set up an Alert without notifications.

A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches
100. To set up a webhook alert destination, the data engineer needs to do the following steps:
* In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.
* Click Alert Destinations and click Add New Alert Destination.
* Select Webhook and enter the webhook URL and the optional custom template for the notification message.
* Click Create to save the webhook alert destination.
* In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.
* Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.
* In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. References: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.

NO.35 A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

Explanation
https://www.w3schools.com/python/python_functions.asp

NO.36 Which of the following is hosted completely in the control plane of the classic Databricks architecture?

Worker node

JDBC data source

Databricks web application

Databricks Filesystem

Driver node

1: The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network. Reference: Databricks architecture overview, Security and Trust Center

NO.37 An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query.
For the first week following the project’s release, the managerwants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project’s release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project’s release?

They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.

They can set the query’s refresh schedule to end after a certain number of refreshes.

They cannot ensure the query does not cost the organization money beyond the first week of the project’s release.

They can set a limit to the number of individuals that are able to manage the query’s refresh schedule.

They can set the query’s refresh schedule to end on a certain date in the query scheduler.

NO.38 Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?

Silver tables contain a less refined, less clean view of data than Bronze data.

Silver tables contain aggregates while Bronze data is unaggregated.

Silver tables contain more data than Bronze tables.

Silver tables contain a more refined and cleaner view of data than Bronze tables.

Silver tables contain less data than Bronze tables.

In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. References: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?

NO.39 A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?

They can set up an Alert with a custom template.

They can set up an Alert with a new email alert destination.

They can set up an Alert with a new webhook alert destination.

They can set up an Alert with one-time notifications.

They can set up an Alert without notifications.

A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches 100. To set up a webhook alert destination, the data engineer needs to do the following steps:
In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.
Click Alert Destinations and click Add New Alert Destination.
Select Webhook and enter the webhook URL and the optional custom template for the notification message.
Click Create to save the webhook alert destination.
In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.
Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.
In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. Reference: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.

NO.40 A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

Databricks Repos automatically saves development progress

Databricks Repos supports the use of multiple branches

Databricks Repos allows users to revert to previous versions of a notebook

Databricks Repos provides the ability to comment on specific changes

Databricks Repos is wholly housed within the Databricks Lakehouse Platform

Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. References: Git integration with Databricks Repos – Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available – New ‘Files’ Feature in …, Databricks Repos – What it is and how we can use it | Adatis.

NO.41 Which of the following must be specified when creating a new Delta Live Tables pipeline?

A key-value pair configuration

The preferred DBU/hour cost

A path to cloud storage location for the written data

A location of a target database for the written data

At least one notebook library to be executed

Option E is the correct answer because it is the only mandatory requirement when creating a new Delta Live Tables pipeline. A pipeline is a data processing workflow that contains materialized views and streaming tables declared in Python or SQL source files. Delta Live Tables infers the dependencies between these tables and ensures updates occur in the correct order. To create a pipeline, you need to specify at least one notebook library to be executed, which contains the Delta Live Tables syntax. You can also specify multiple libraries of different languages within your pipeline. The other options are optional or not applicable for creating a pipeline. Option A is not required, but you can optionally provide a key-value pair configuration to customize the pipeline settings, such as the storage location, the target schema, the notifications, and the pipeline mode.
Option B is not applicable, as the DBU/hour cost is determined by the cluster configuration, not the pipeline creation. Option C is not required, but you can optionally specify a storage location for the output data from the pipeline. If you leave it empty, the system uses a default location. Option D is not required, but you can optionally specify a location of a target database for the written data, either in the Hive metastore or the Unity Catalog.
References: Tutorial: Run your first Delta Live Tables pipeline, What is Delta Live Tables?, Create a pipeline, Pipeline configuration.

NO.42 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?

Both teams would autoscale their work as data size evolves

Both teams would use the same source of truth for their work

Both teams would reorganize to report to the same department

Both teams would be able to collaborate on projects in real-time

Both teams would respond more quickly to ad-hoc requests

NO.43 A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

They can set up an Alert with a custom template.

They can set up an Alert with a new email alert destination.

They can set up an Alert with one-time notifications.

They can set up an Alert with a new webhook alert destination.

They can set up an Alert without notifications.

NO.44 In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?

Checkpointing and Write-ahead Logs

Structured Streaming cannot record the offset range of the data being processed in each trigger.

Replayable Sources and Idempotent Sinks

Write-ahead Logs and Idempotent Sinks

Checkpointing and Idempotent Sinks

Structured Streaming uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. This ensures that the engine can reliably track the exact progress of the processing and handle any kind of failure by restarting and/or reprocessing. Checkpointing is the mechanism of saving the state of a streaming query to fault-tolerant storage (such as HDFS) so that it can be recovered after a failure.
Write-ahead logs are files that record the offset range of the data being processed in each trigger and are written to the checkpoint location before the processing starts. These logs are used to recover the query state and resume processing from the last processed offset range in case of a failure. References: Structured Streaming Programming Guide, Fault Tolerance Semantics

NO.45 A data engineer needs to create a table in Databricks using data from their organization’s existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

org.apache.spark.sql.jdbc

autoloader

DELTA

sqlite

org.apache.spark.sql.sqlite

In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is “sqlite” because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, “sqlite” would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver

Loading …

100% Updated Databricks Databricks-Certified-Data-Engineer-Associate Enterprise PDF Dumps: https://www.passtestking.com/Databricks/Databricks-Certified-Data-Engineer-Associate-practice-exam-dumps.html

Categories:Databricks-Certified-Data-Engineer-AssociateDatabricks

Tags:Databricks-Certified-Data-Engineer-Associate real dumps free Databricks-Certified-Data-Engineer-Associate reliable exam dumps.zip Databricks-Certified-Data-Engineer-Associate reliable exam sample online Databricks-Certified-Data-Engineer-Associate reliable test braindumps Databricks-Certified-Data-Engineer-Associate valid mock test

admin

Databricks-Certified-Professional-Data-Engineer / Databricks

Free Databricks (Databricks-Certified-Professional-Data-Engineer) Certification Sample Questions with Online Practice Test [Q94-Q114]

Databricks-Certified-Data-Engineer-Associate Practice Tests