[Dec 07, 2024] Databricks Databricks-Certified-Data-Engineer-Associate Real Exam Questions and Answers FREE

Pass Databricks Databricks-Certified-Data-Engineer-Associate Exam Info and Free Practice Test

The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is designed for professionals who want to demonstrate their expertise in building and managing data pipelines with Databricks. Databricks Certified Data Engineer Associate Exam certification is a vendor-neutral validation of your abilities to work with Databricks Unified Analytics Platform and Apache Spark.

 

NEW QUESTION # 61
Which tool is used by Auto Loader to process data incrementally?

• A. Checkpointing
• B. Unity Catalog
• C. Spark Structured Streaming
• D. Databricks SQL

Answer: C

Explanation:
Auto Loader in Databricks utilizes Spark Structured Streaming for processing data incrementally. This allows Auto Loader to efficiently ingest streaming or batch data at scale and to recognize new data as it arrives in cloud storage. Spark Structured Streaming provides the underlying engine that supports various incremental data loading capabilities like schema inference and file notification mode, which are crucial for the dynamic nature of data lakes.
Reference:
Databricks documentation on Auto Loader: Auto Loader Overview

NEW QUESTION # 62
Which of the following describes the relationship between Bronze tables and raw data?

• A. Bronze tables contain less data than raw data files.
• B. Bronze tables contain raw data with a schema applied.
• C. Bronze tables contain more truthful data than raw data.
• D. Bronze tables contain a less refined view of data than raw data.
• E. Bronze tables contain aggregates while raw data is unaggregated.

Answer: E

NEW QUESTION # 63
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?

• A. FROM "path/to/csv"
• B. None of these lines of code are needed to successfully complete the task
• C. USING CSV
• D. USING DELTA
• E. FROM CSV

Answer: A

Explanation:
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

NEW QUESTION # 64
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 until the pipeline is shut down.
• B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
• 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 at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
• E. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

Answer: A

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 # 65
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

• A.
• B.
• C.
• D.
• E.

Answer: E

NEW QUESTION # 66
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?

• A. They can set up an Alert with a custom template.
• B. They can set up an Alert with a new email alert destination.
• C. They can set up an Alert with a new webhook alert destination.
• D. They can set up an Alert without notifications.
• E. They can set up an Alert with one-time notifications.

Answer: C

Explanation:
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.

NEW QUESTION # 67
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?

• A. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
• B. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
• C. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.
• D. They can set the query's refresh schedule to end after a certain number of refreshes.
• E. They can set the query's refresh schedule to end on a certain date in the query scheduler.

Answer: E

NEW QUESTION # 68
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?

• A. spark.sql("sales")
• B. spark.table("sales")
• C. There is no way to share data between PySpark and SQL.
• D. spark.delta.table("sales")
• E. SELECT * FROM sales

Answer: B

Explanation:
The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. References: 1: SparkSession | PySpark
3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page - delta-spark 2.4.0 documentation

NEW QUESTION # 69
A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location.
Which of the following data entities should the data engineer create?

• A. Database
• B. View
• C. Function
• D. Table
• E. Temporary view

Answer: D

Explanation:
Explanation
In the context described, creating a "Table" is the most suitable choice. Tables in SQL are data entities that exist independently of any session and are saved in a physical location. They can be accessed and manipulated by other data engineers in different sessions, which aligns with the requirements stated. A "Database" is a collection of tables, views, and other database objects. A "Function" is a stored procedure that performs an operation. A "View" is a virtual table based on the result-set of an SQL statement, but it is not stored physically. A "Temporary view" is a feature that allows you to store the result of a query as a view that disappears once your session with the database is closed.

NEW QUESTION # 70
Which of the following can be used to simplify and unify siloed data architectures that are specialized for specific use cases?

• A. None of these
• B. Data lake
• C. All of these
• D. Data lakehouse
• E. Data warehouse

Answer: D

Explanation:
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

NEW QUESTION # 71
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?

• A. They can increase the cluster size of the SQL endpoint.
• B. They can turn on the Serverless feature for the SQL endpoint.
• C. They can increase the maximum bound of the SQL endpoint's scaling range.
• D. They can turn on the Auto Stop feature for the SQL endpoint.
• E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."

Answer: C

Explanation:
https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696

NEW QUESTION # 72
Which of the following describes the relationship between Gold tables and Silver tables?

• A. Gold tables are more likely to contain more data than Silver tables.
• B. Gold tables are more likely to contain a less refined view of data than Silver tables.
• C. Gold tables are more likely to contain truthful data than Silver tables.
• D. Gold tables are more likely to contain valuable data than Silver tables.
• E. Gold tables are more likely to contain aggregations than Silver tables.

Answer: E

Explanation:
According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer.
They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. References:
* What is the medallion lakehouse architecture?
* What is a Medallion Architecture?

NEW QUESTION # 73
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

• A. There is no way to share data between PySpark and SQL.
• B. spark.delta.table
• C. spark.sql
• D. spark.table
• E. SELECT * FROM sales

Answer: C

Explanation:
Explanation
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
df = spark.sql("SELECT * FROM sales")
print(df.count())

NEW QUESTION # 74
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?

• A. Databricks Repos supports the use of multiple branches
• B. Databricks Repos allows users to revert to previous versions of a notebook
• C. Databricks Repos automatically saves development progress
• D. Databricks Repos provides the ability to comment on specific changes
• E. Databricks Repos is wholly housed within the Databricks Lakehouse Platform

Answer: A

Explanation:
Explanation
An advantage of using Databricks Repos over the built-in Databricks Notebooks versioning is the ability to work with multiple branches. Branching is a fundamental feature ofversion control systems like Git, which Databricks Repos is built upon. It allows you to create separate branches for different tasks, features, or experiments within your project. This separation helps in parallel development and experimentation without affecting the main branch or the work of other team members. Branching provides a more organized and collaborative development environment, making it easier to merge changes and manage different development efforts. While Databricks Notebooks versioning also allows you to track versions of notebooks, it may not provide the same level of flexibility and collaboration as branching in Databricks Repos.

NEW QUESTION # 75
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

• A. PIVOT
• B. WHERE
• C. SUM
• D. TRANSFORM
• E. CONVERT

Answer: A

Explanation:
Explanation
The SQL keyword PIVOT can be used to convert a table from a long format to a wide format. A long format table has one column for each variable and one row for each observation. A wide format table has one column for each variable and value combination and one row for each observation. PIVOT allows you to specify the column that contains the values to be pivoted, the column that contains the categories to be pivoted, and the aggregation function to be applied to the values. For example, the following query converts a long format table of sales data into a wide format table with columns for each product and sum of sales:
SELECT *
FROM sales
PIVOT (
SUM(sales_amount) FOR product IN ('A', 'B', 'C')
)
References: The information can be referenced from Databricks documentation on SQL: PIVOT.
https://files.training.databricks.com/assessments/practice-exams/PracticeExam-DataEngineerAssociate.pdf
https://community.databricks.com/t5/data-engineering/practice-exams-for-databricks-certified-data-engineer/td-p

NEW QUESTION # 76
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