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Databricks-Certified-Data-Engineer-Associate exam is a comprehensive test of an individual's knowledge of Databricks and its related technologies, including Apache Spark, SQL, and Python. 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.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q132-Q137):

NEW QUESTION # 132
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 turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."
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 increase the cluster size of the SQL endpoint.

Answer: D

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

NEW QUESTION # 133
A data engineer has written a function in a Databricks Notebook to calculate the population of bacteria in a given medium.

Analysts use this function in the notebook and sometimes provide input arguments of the wrong data type, which can cause errors during execution.
Which Databricks feature will help the data engineer quickly identify if an incorrect data type has been provided as input?

A. The Spark User interface has a debug tab that contains the variables that are used in this session.
B. The Databricks debugger enables the use of a variable explorer to see at a glance the value of the variables.
C. The Databricks debugger enables breakpoints that will raise an error if the wrong data type is submitted
D. The Data Engineer should add print statements to find out what the variable is.

Answer: B

NEW QUESTION # 134
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

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

Answer: E

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 . References: 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 # 135
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

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

Answer: D

Explanation:
The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.
The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user- defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.
:
1: PIVOT | Databricks on AWS
2: Reshaping Data - Long vs Wide Format | Databricks on AWS
3: CONVERT | Databricks on AWS
4: WHERE | Databricks on AWS
5: TRANSFORM | Databricks on AWS
[SUM | Databricks on AWS]

NEW QUESTION # 136
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(continuous="once")
B. trigger(parallelBatch=True)
C. trigger(processingTime="once")
D. processingTime(1)
E. trigger(availableNow=True)

Answer: E

Explanation:
Explanation
https://stackoverflow.com/questions/71061809/trigger-availablenow-for-delta-source-streaming-queries-in-pyspa

NEW QUESTION # 137
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