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Databricks Certified Data Engineer Associate certification is a valuable credential for professionals who work in data engineering. Databricks Certified Data Engineer Associate Exam certification demonstrates that the candidate has a deep understanding of data engineering concepts and has the skills and experience to work with Databricks effectively. Employers often look for candidates who hold this certification when hiring for data engineering roles, as it validates the candidate's expertise and knowledge in the field.

Databricks Certified Data Engineer Associate certification is suitable for data engineers, data scientists, and other professionals who work with big data and want to demonstrate their expertise in Databricks. It is also relevant for organizations that use Databricks to process and analyze large amounts of data, as it ensures that their employees have the necessary skills and knowledge to work with the platform effectively.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
In which of the following file formats is data from Delta Lake tables primarily stored?

A. Delta
B. A proprietary, optimized format specific to Databricks
C. JSON
D. CSV
E. Parquet

Answer: E

Explanation:
Delta Lake is an open source project that provides ACID transactions, time travel, and other features on top of Apache Parquet, a columnar file format that is widely used for big data analytics. Delta Lake uses versioned Parquet files to store your data in your cloud storage, along with JSON files as transaction logs and checkpoint files to track the changes and ensure data integrity. Delta Lake is compatible with any Apache Hive compatible file format, such as CSV, JSON, or AVRO, but it primarily stores data as Parquet files for better performance and compression. Reference: How to Create Delta Lake tables, 5 reasons to choose Delta Lake format (on Databricks), Parquet vs Delta format in Azure Data Lake Gen 2 store, What is Delta Lake? - Azure Databricks, Lakehouse and Delta tables - Microsoft Fabric

NEW QUESTION # 85
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

A. A data lakehouse stores data in open formats.
B. A data lakehouse enables machine learning and artificial Intelligence workloads.
C. A data lakehouse supports ACID-compliant transactions.
D. A data lakehouse allows the use of SQL queries to examine data.
E. A data lakehouse provides storage solutions for structured and unstructured data.

Answer: C

Explanation:
A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. One of the key features of a data lakehouse is that it supports ACID-compliant transactions, which means that it ensures data integrity, consistency, and isolation across concurrent read and write operations3. This feature results in improved data quality over a traditional data lake, which does not support transactions and may suffer from data corruption, duplication, or inconsistency due to concurrent or streaming data ingestion and processing . Reference: 1: What is a Data Lakehouse? - Databricks 2: What is a Data Lakehouse? Definition, features & benefits. - Qlik 3: ACID Transactions - Databricks : [Data Lake vs Data Warehouse: Key Differences] : [Data Lakehouse: The Future of Data Engineering]

NEW QUESTION # 86
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace predict with a stream-friendly prediction function
B. Replace format("delta") with format("stream")
C. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
D. Replace spark.read with spark.readStream
E. Replace "transactions" with the path to the location of the Delta table

Answer: D

Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html
In the context of Databricks, when transitioning from batch processing to stream processing, one common change that needs to be made is replacing spark.read with spark.readStream. This modification is essential because spark.read is used for batch processing, while spark.readStream is used for stream processing. The rest of the code can often remain the same or require minimal changes. References: The information can be referenced from Databricks documentation on structured streaming: Structured Streaming Programming Guide.

NEW QUESTION # 87
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?

A. Unity Catalog
B. Auto Loader
C. Data Explorer
D. Delta Live Tables
E. Delta Lake

Answer: D

Explanation:
Explanation
https://docs.databricks.com/delta-live-tables/expectations.html
Delta Live Tables is a tool provided by Databricks that can help data engineers automate the monitoring of data quality. It is designed for managing data pipelines, monitoring data quality, and automating workflows.
With Delta Live Tables, you can set up data quality checks and alerts to detect issues and anomalies in your data as it is ingested and processed in real-time. It provides a way to ensure that the data quality meets your desired standards and can trigger actions or notifications when issues are detected. While the other tools mentioned may have their own purposes in a data engineeringenvironment, Delta Live Tables is specifically designed for data quality monitoring and automation within the Databricks ecosystem.

NEW QUESTION # 88
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?

Answer: A

Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/

NEW QUESTION # 89
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