DP-200 Interview Questions
If you are looking forward to ace the DP-200 interview then you must know that in order to ace an interview, you not only need to have technical knowledge but also the confidence and ability to portray the answers in the best possible manner. However, for your ease, we have brought for you the collection of the best possible and frequently asked DP-200 interview questions.
Firstly, you should know the Exam DP-200 Implementing an Azure Data Solution tests your capability of implementing data storage solutions; managing and developing data processing, and monitoring data solutions. The Azure data engineers are responsible for the following tasks –
- Provisioning data storage services
- Implementing data retention policies
- Identifying performance bottlenecks
- Ingesting streaming and batch data
- Transforming data
- Implementing security requirements
- Accessing external data sources.
What is Data engineering?
Well, data engineering as the name suggests, is a term that centers around the use of data research. Data engineers deal with large data collections and change the inappropriate data into valuable data. They design and build pipelines that are responsible for the transformation and transportation of data into a format that is in high usability state.
What are the roles of an Azure Data engineer?
The role of an Azure data engineer is to collaborate with business stakeholders to find and hence meet the data requirements. Their work is to design and implement solutions. Alongside, they manage and monitor the security and privacy of data to satisfy the needs of a business. Some other general roles are as follows:
- Aligning architecture with the requirements of the business.
- Acquisition of data.
- Developing data set processes.
- Using programming tools and languages.
What is Data Modelling?
Data modeling is defined as the process of creating a visual representation of an information system or its parts so as to communicate connections between data points and structures. The overall goal is to present the types of data that are used and stored within the system.
The data models are built around the needs of a business. The feedback from business stakeholders forms the rules and regulations that can be incorporated into the design of a new system or modified in the succession of an existing one.
How would you describe Azure Data Factory?
The Azure Data Factory is a service that is designed to allow developers to compile various data sources. It is somewhat like SSIS in the cloud that is used to manage the data you have in the cloud. It also provides entry to on-premises data in the SQL Server, cloud data in the Azure Storage, and Azure SQL Database. However, access to on-premises data is given through a data management gateway that helps connecting to the on-premises SQL Server databases.
Why is Azure Data Factory in demand?
There are several reasons for high demand of Azure Data Factory. Some of it are:
- Storing an umpteen amount of data, orchestrating it, and automating the movement of data in the cloud seamlessly.
- Enabling data engineers to drive business and the IT-led Analytics/BI.
- Preparing data, constructing ETL and ELT processes.
- Monitoring pipelines code-free.
What do you know about Blob storage in Azure?
Azure Blob storage is basically a feature of Microsoft Azure that allows its users to store large amounts of unstructured data on a data storage platform. Here, Blob stands for Binary Large Object, which is inclusive of objects such as images and multimedia files.
What is meant by *args and **kwargs?
Well, *args basically define an ordered function on the other hand **kwargs represent unordered arguments that are used in a function.
What are the types of integration runtimes?
There are three types of integration runtimes:
- Azure Integration Run Time
- Self Hosted Integration Run Time
- Azure SSIS Integration Run Time
What is Azure Data Warehouse and Data Lake?
Data Warehouse is a way of storing data that is used largely even today. Data Lake on the other side is complementary to Data Warehouse which means that if you have your data at a data lake then that can be stored in the data warehouse after following certain rules of SQL.
What are the essential frameworks for data engineers?
They are as follows:
- Spark
- Flink
- Kafka
- ElasticSearch
- PostgreSQL/Redshift
- Airflow
What is the use of HDinsight service?
HDInsight is a platform as a service. If you want to process a data set then you have to configure the cluster with predefined nodes and further use a language like pig or hive for processing data. Using HD insight, you can create a cluster as you want and can control it as well.
What are the different components of Hadoop?
Hadoop comprises of the following components:
- HDFS
- MapReduce
- Hadoop Common
- YARN
Explain the concept of pipeline in Azure Data factory?
A pipeline in Azure Data Factory is a logical grouping of activities that perform a task. For instance, a pipeline can contain a set of activities that ingest log data and hence kick off a mapping data flow so as to analyze the log data.
What are the ways to schedule a pipeline?
Pipeline can be scheduled as follows:
- Using scheduler trigger
- Using time window trigger
- The trigger basically uses a wall-clock calendar schedule that can schedule pipelines periodically or in calendar-based recurrent patterns
What do we use to create data flows in Data Factory?
We use the Data Factory V2 version in order to create data flows.
How do one create ETL process in Azure Data Factory?
The steps for Creating ETL are:
- Create a linked service for source data store which is SQL Server Database
- Suppose that we have a cars dataset
- Prepare a linked service for the destination datastore which is Azure Data Lake Store
- Create a dataset for saving data
- Make the pipeline and add copy activity
- Schedule the pipeline by adding a trigger
What important languages do data engineers use?
- Probability
- Linear algebra
- Machine learning
- Trend analysis and regression
- Hive QL and SQL database
What is a Hadoop application?
The HDFS Hadoop application identifies the document framework where one keeps the Hadoop data. It has a dynamic document framework that has a high capacity to transfer data.
What is the full form of HDFS?
Hadoop Distributed File System
What do you understand by the term NameNode?
NameNode is the center point of HDFS. It not only stores information on HDFS but also tracks different documents over the groups. However, the real information is not stored here but is kept in DataNodes.
What messages does DataNode give to NameNode?
The two signs that DataNode gives to NameNode are:
- Block report signals i.e the list of data blocks stored on DataNode and its functioning.
- Heartbeat signals that DataNode is functional. This is a periodic report that establishes whether to use NameNode or not.
Which scripting languages are you experienced in?
Scripting languages have high significance for a data engineer. So, you should have a good knowledge of scripting languages, as it helps you perform the analytical tasks efficiently and also in automation of data flow.
How is data analytics beneficial for a business?
Data analysis helps in increasing revenue of a business as follows:
- Using data efficiently to help businesses grow.
- Increasing customer value.
- Turning analytical in order to improve staffing levels forecasts.
- Cutting down the production cost of the organizations.
What skills of a good data engineer do you possess?
- Appreciable knowledge about Data Modelling.
- Understanding the design and architecture of databases.
- In-Depth Database Knowledge – SQL and NoSQL.
- Working experience in data stores and systems like Hadoop (HDFS).
- Data Visualization Skills.
- Good communication, leadership, critical thinking, and problem-solving ability.
What is the difference between a data engineer and a data scientist?
Though the responsibilities of both of these positions somewhat overlap, yet they are different from each other. A data engineer develops, tests, and maintains the complete architecture for data generation, on the other side a data scientist analyzes and interprets complex data. Hence, data scientists require data engineers to create the required infrastructure for them to work.
