Microsoft DP-600 Practice Exams
Last updated on Oct 03,2026- Exam Code: DP-600
- Exam Name: Implementing Analytics Solutions Using Microsoft Fabric
- Certification Provider: Microsoft
- Latest update: Oct 03,2026
Question #231
You have a Fabric tenant that contains JSON files in OneLake. The files have one billion items.
You plan to perform time series analysis of the items.
You need to transform the data, visualize the data to find insights, perform anomaly detection, and share the insights with other business users.
The solution must meet the following requirements:
– Use parallel processing.
– Minimize the duplication of data.
– Minimize how long it takes to load the data.
What should you use to transform and visualize the data?
- A . the PySpark library in a Fabric notebook
- B . the pandas library in a Fabric notebook
- C . a Microsoft Power BI report that uses core visuals
Correct Answer: A
A
Explanation:
PySpark vs Pandas Performance
Pyspark has been created to help us work with big data on distributed systems. On the other hand, the pandas module is used to manipulate and analyze datasets up to a few GigaBytes (Less than 10 GB to be specific). So, PySpark, when used with a distributed computing system, gives better performance than pandas. Pyspark also uses resilient distributed datasets (RDDs) to work parallel on the data. Hence, it performs better than pandas.
Note: PySpark is a Python library that provides an interface for Apache Spark. Spark is an open-source framework for big data processing. Spark is built to process large amounts of data quickly by distributing computing tasks across a cluster of machines.
PySpark allows us to use Apache Spark and its ecosystem of libraries, such as Spark SQL for working with structured data.
We can also use Spark MLlib for machine learning and GraphX for graph processing using Pyspark in Python.
PySpark supports many data sources, including Hadoop Distributed File System (HDFS), Apache Cassandra, and Amazon S3.
Along with the data processing capabilities, we can also use pyspark with popular Python libraries such as NumPy and Pandas.
Reference: https://www.codeconquest.com/blog/pyspark-vs-pandas-performance-memory-consumption-and-use-cases
A
Explanation:
PySpark vs Pandas Performance
Pyspark has been created to help us work with big data on distributed systems. On the other hand, the pandas module is used to manipulate and analyze datasets up to a few GigaBytes (Less than 10 GB to be specific). So, PySpark, when used with a distributed computing system, gives better performance than pandas. Pyspark also uses resilient distributed datasets (RDDs) to work parallel on the data. Hence, it performs better than pandas.
Note: PySpark is a Python library that provides an interface for Apache Spark. Spark is an open-source framework for big data processing. Spark is built to process large amounts of data quickly by distributing computing tasks across a cluster of machines.
PySpark allows us to use Apache Spark and its ecosystem of libraries, such as Spark SQL for working with structured data.
We can also use Spark MLlib for machine learning and GraphX for graph processing using Pyspark in Python.
PySpark supports many data sources, including Hadoop Distributed File System (HDFS), Apache Cassandra, and Amazon S3.
Along with the data processing capabilities, we can also use pyspark with popular Python libraries such as NumPy and Pandas.
Reference: https://www.codeconquest.com/blog/pyspark-vs-pandas-performance-memory-consumption-and-use-cases