Most Recent Databricks-Certified-Data-Engineer-Associate Exam Questions & Answers

Prepare for the Databricks Certified Data Engineer Associate Exam exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Databricks-Certified-Data-Engineer-Associate exam and achieve success.

The questions for Databricks-Certified-Data-Engineer-Associate were last updated on Dec 20, 2024.

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Question No. 1

Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

AThe ability to manipulate the same data using a variety of languages

BThe ability to collaborate in real time on a single notebook

CThe ability to set up alerts for query failures

DThe ability to support batch and streaming workloads

EThe ability to distribute complex data operations

Show Answer

Correct Answer: D

Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse.Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1.Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2.Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3.Reference:1:What is Delta Lake? | Databricks on AWS2:Upsert into a table using merge | Databricks on AWS3: [Query an older snapshot of a table (time travel) | Databricks on AWS]

Learn more

1learn.microsoft.com2medium.com3slideshare.net4docs.databricks.com5github.com6key2consulting.com

Question No. 2

Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?

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

BSilver tables contain aggregates while Bronze data is unaggregated.

CSilver tables contain more data than Bronze tables.

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

ESilver tables contain less data than Bronze tables.

Show Answer

Correct Answer: D

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.Reference:What is a Medallion Architecture?,Transforming Bronze Tables in Silver Tables,What is the medallion lakehouse architecture?

Question No. 3

Which of the following benefits is provided by the array functions from Spark SQL?

AAn ability to work with data in a variety of types at once

BAn ability to work with data within certain partitions and windows

CAn ability to work with time-related data in specified intervals

DAn ability to work with complex, nested data ingested from JSON files

EAn ability to work with an array of tables for procedural automation

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Correct Answer: D

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, theexplodefunction can be used to transform an array column into multiple rows, one for each element in the array3.Thearray_containsfunction can be used to check if a value is present in an array column4. Thearray_joinfunction 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.Reference:1:Spark SQL, Built-in Functions - Apache Spark2:Spark SQL Array Functions Complete List - Spark By Examples3:Spark SQL Array Functions - Syntax and Examples - DWgeek.com4: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]

Question No. 4

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?

AThey can set up an Alert with a custom template.

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

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

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

EThey can set up an Alert without notifications.

Show Answer

Correct Answer: C

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.

Question No. 5

A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.

Which of the following tools can the data engineer use to solve this problem?

AUnity Catalog

BDelta Lake

CDatabricks SQL

DData Explorer

EAuto Loader

Show Answer

Correct Answer: E

Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage. Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks.Reference:What is Auto Loader?,Get started with Databricks Auto Loader,Auto Loader in Delta Live Tables

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