AWS Data Analytics Specialty Interview Questions

Getting an industry-approved credential from AWS proves your expertise in AWS data lakes and analytics assistance. It establishes credibility and trust by highlighting your capability to design, build, guard, and manage analytics solutions on AWS that are effective, cost-effective, and reliable. Further, it shows that you have breadth and depth in remitting insight from data. All this knowledge and expertise will definitely help one to get a perfect job. But, the main challenge is to pass the interview session. But worry not! For polishing your knowledge and giving you a glance at interview session, we at Testprep training has curated some reliable and most possibly appearing questions for AWS Data Analytics Specialty Interview.

So, let’s get started:

What is Amazon EMR and how is it used in data analytics?

Amazon Elastic MapReduce (EMR) is a web service that makes it easy to process large amounts of data using the Hadoop ecosystem on AWS. It enables you to run various big data frameworks such as Apache Hadoop, Apache Spark, and Apache Hive, among others, on a fully managed cluster of Amazon Elastic Compute Cloud (EC2) instances.

EMR is used in data analytics to process and analyze large data sets stored in Amazon S3. It allows you to quickly and easily spin up a cluster of instances to process the data, without the need to manage the underlying infrastructure. This can be especially useful for data warehousing, data lake, and big data analytics use cases.

EMR also provides a way to use Apache Hive, Pig, and Presto on top of the Hadoop Distributed File System (HDFS) to process and analyze data stored in S3. Additionally, it can also be integrated with other AWS services such as Amazon Redshift, Amazon Kinesis, Amazon DynamoDB, and Amazon SNS, among others, to build a complete data analytics pipeline.

In summary, Amazon EMR is a fully managed service that makes it easy to process big data using the Hadoop ecosystem on AWS. It allows you to quickly spin up a cluster of instances to process large data sets stored in Amazon S3, and provides a way to use different big data frameworks and other AWS services for data warehousing, data lake and big data analytics use cases.

How does Amazon Redshift differ from other data warehousing solutions?

Amazon Redshift is a data warehousing service provided by AWS that is designed for OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) workloads. It differs from other data warehousing solutions in several ways:

1. Scalability: Redshift is designed to be highly scalable, making it easy to scale up or down based on the needs of your workload. It allows you to start with a small cluster and then add more nodes as your data grows.
2. Cost-effective: Redshift is a cost-effective data warehousing solution, with a pay-as-you-go pricing model. This eliminates the need for expensive upfront investments and allows you to pay only for the resources you use.
3. Performance: Redshift is optimized for high performance and can handle complex queries and large data sets with ease. It uses columnar storage and advanced compression algorithms to reduce the amount of storage required and improve query performance.
4. Security: Redshift provides a number of security features to protect your data, including encryption of data at rest, network isolation, and integration with AWS Identity and Access Management (IAM) for fine-grained access control.

In summary, Amazon Redshift is a highly scalable, cost-effective, high-performance data warehousing solution that provides robust security and easy integration with other AWS services, and has a user-friendly management console.

Can you explain the process of setting up a data lake on AWS?

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. The process of setting up a data lake on AWS typically involves the following steps:

1. Decide on a data lake architecture: Decide on the architecture of your data lake, taking into consideration factors such as scalability, security, and data governance. AWS provides several services that can be used to build a data lake, such as Amazon S3, Amazon Glue, Amazon Lake Formation, and Amazon Athena.
2. Create an Amazon S3 bucket: Create an Amazon S3 bucket to store your raw data. This is the foundation of your data lake, and all data in the lake will be stored in this bucket.
3. Ingest data: Ingest your data into the S3 bucket by using services such as Amazon Kinesis, AWS Glue, or AWS Data Pipeline. These services can help you collect, clean, and move data into the data lake.
4. Catalog and organize data: Catalog and organize your data using services such as AWS Glue Data Catalog or Amazon Lake Formation. These services allow you to define and manage the schema of your data, making it easier to search and query.

In summary, setting up a data lake on AWS involves creating an S3 bucket, ingesting data, cataloging and organizing data, analyzing data, governing and securing data, and monitoring performance. There are several AWS services that can be used to accomplish these tasks, allowing you to create a data lake that is scalable, secure, and optimized for performance.

How can you use AWS Glue and Amazon Athena for data discovery and querying?

AWS Glue and Amazon Athena are both services provided by AWS that can be used for data discovery and querying.

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. It can be used to discover, catalog and classify data stored in various data stores such as S3, RDS, and Redshift. Glue Crawlers scan the data store, extracts metadata and creates table definitions in the Glue Data Catalog.

Amazon Athena is an interactive query service that makes it easy to analyze data stored in Amazon S3 using standard SQL. Athena uses the Glue Data Catalog as the metastore for the data stored in S3. This means that the table definitions created by Glue Crawlers are automatically available for querying in Athena.

By using Glue and Athena together, you can easily discover and catalog your data stored in S3, and then query it using SQL. This makes it easy to analyze your data without having to set up and maintain a separate data warehouse.

Here is an example use case:

• Use Glue Crawlers to discover and catalog your data stored in S3
• Use Glue ETL jobs to clean, transform and prepare the data
• Use Athena to run SQL queries against the data catalogued by Glue and stored in S3
• Use Amazon QuickSight to create visualizations and dashboards based on the queried data

Can you explain the use of Amazon Kinesis for real-time streaming data analytics?

Amazon Kinesis is a real-time streaming data analytics service provided by AWS that allows you to collect, process, and analyze large streams of data records in real-time. With Kinesis, you can easily build applications that process and analyze data streams, such as IoT telemetry data, log files, social media, and financial transactions.

There are three main components of Amazon Kinesis: Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics.

• Kinesis Data Streams: This is a fully managed service that allows you to collect, store and process large streams of data records in real-time. You can use Kinesis Data Streams to ingest data from various sources, such as IoT devices, mobile apps, or social media platforms.
• Kinesis Data Firehose: This service allows you to easily load data streams into data stores such as Amazon S3, Amazon Redshift, or Amazon Elasticsearch Service. Data Firehose automatically scales with the incoming data rate and can also perform real-time data transformation and filtering.
• Kinesis Data Analytics: This service allows you to run SQL queries on the data streams in real-time. With Kinesis Data Analytics, you can perform real-time analytics on streaming data, such as anomaly detection, counting, aggregations, and filtering.

Here is an example use case:

• Use Kinesis Data Streams to ingest data from IoT devices in real-time
• Use Kinesis Data Firehose to automatically load data streams into S3 for long-term storage
• Use Kinesis Data Analytics to perform real-time analytics on the data streams, such as anomaly detection and aggregations
• Use Amazon QuickSight to create visualizations and dashboards based on the queried data

How can you use AWS QuickSight for data visualization and business intelligence?

AWS QuickSight is a cloud-based business intelligence (BI) and data visualization service provided by AWS. It allows you to easily create and share interactive dashboards and visualizations of your data.

QuickSight can connect to various data sources such as Amazon RDS, Amazon Redshift, Amazon S3, and others. It also supports data connectors for popular data warehousing and BI tools like SAP, Google Analytics, and Salesforce. Once connected, you can use QuickSight’s intuitive web-based interface to create and customize visualizations and dashboards.

QuickSight provides several features to help with data visualization and business intelligence:

• Automatic data preparation: QuickSight can automatically detect and combine data from different sources, and perform data cleaning and transformation tasks.
• Drag-and-drop interface: QuickSight’s web-based interface allows you to easily create visualizations and dashboards by dragging and dropping fields and metrics.
• Customizable visualizations: QuickSight provides a variety of chart types and visualization options, such as bar charts, line charts, scatter plots, and heat maps, and you can customize the appearance of the visualizations using built-in themes and color palettes.
• Collaboration: QuickSight makes it easy to share visualizations and dashboards with others by creating dedicated URLs or embedding them in other applications.
• Data security: QuickSight supports fine-grained access controls and data encryption to ensure that your data is secure.

Here is an example use case:

• Use Amazon S3 and AWS Glue to store and prepare your data for analytics
• Use QuickSight to connect to your S3 data, create and customize visualizations and dashboards
• Share the visualizations and dashboards with others using dedicated URLs or by embedding them in other applications

Can you explain the use of AWS Machine Learning and Amazon SageMaker for building and deploying machine learning models?

WS Machine Learning and Amazon SageMaker are two services provided by AWS that can be used to build and deploy machine learning models.

AWS Machine Learning is a collection of pre-built, pre-trained machine learning models that can be used for a variety of common use cases such as image and text analysis, forecasting, and anomaly detection. It provides an easy-to-use web interface that allows you to select the appropriate model for your use case, and to input your data to generate predictions.

Amazon SageMaker, on the other hand, is a fully managed machine learning service that enables developers and data scientists to build, train, and deploy machine learning models. It provides a suite of tools and capabilities to help with the entire machine learning workflow, including data preparation, model building and training, model deployment, and model management.

Here is an example use case:

• Use Amazon S3 to store your data
• Use Amazon SageMaker to prepare your data, build and train a machine learning model
• Use SageMaker to deploy the model to an endpoint, making it accessible through a REST API
• Use AWS Machine Learning to generate predictions using the deployed model

How can you use AWS Data Pipeline and AWS Glue for data ETL?

AWS Data Pipeline and AWS Glue can be used together for data ETL (extract, transform, and load) processes.

AWS Data Pipeline is a managed service that helps you move and process data between different AWS services and on-premises data sources. It can be used to create and schedule data-driven workflows that move and transform data.

AWS Glue is a managed extract, transform, and load (ETL) service that moves data among data stores. With AWS Glue, you can create ETL jobs that extract data from various sources, transform the data to match the target schema, and load the data into the target data store.

You can use AWS Data Pipeline to schedule and run AWS Glue ETL jobs, so that the data is extracted, transformed, and loaded at regular intervals. Additionally, you can use Data Pipeline to create and manage dependencies between different ETL jobs, so that the data is processed in the correct order.

In summary, you can use AWS Data Pipeline to schedule and run AWS Glue ETL jobs and manage dependencies between the ETL jobs.

Can you explain the use of Amazon Elasticsearch for real-time search and analytics?

Amazon Elasticsearch is a fully managed service that makes it easy to deploy, operate, and scale Elasticsearch clusters in the AWS cloud. Elasticsearch is a powerful, open-source search and analytics engine that is designed for handling large volumes of data in real-time.

One of the main use cases for Amazon Elasticsearch is real-time search and analytics. With Elasticsearch, you can index and search large volumes of data quickly and easily, making it possible to perform complex search queries and analyze data in near real-time.

For example, you can use Elasticsearch to index and search large log files, allowing you to quickly troubleshoot issues in your systems. You can also use Elasticsearch to index and search large amounts of data from web applications, allowing you to provide fast, relevant search results to your users.

Additionally, Elasticsearch has built-in support for analytics and aggregation, you can use it to analyze and gain insights from large datasets, like website analytics data, IoT data, application log data, and more. Elasticsearch’s aggregation capabilities allow you to perform complex data analysis and create interactive visualizations, such as charts and graphs, that can be used to explore and understand your data.

In summary, Amazon Elasticsearch is a fully managed service that makes it easy to deploy, operate, and scale Elasticsearch clusters in the AWS cloud. It is mainly used for real-time search and analytics, indexing and searching large volumes of data, providing fast and relevant search results, and performing complex data analysis and creating interactive visualizations.

How can you use Amazon CloudWatch for monitoring and troubleshooting data analytics pipelines?

Amazon CloudWatch is a monitoring service that can be used to monitor and troubleshoot data analytics pipelines. CloudWatch can be used to collect and track metrics, collect and monitor log files, and set alarms.

Here are a few ways in which you can use Amazon CloudWatch for monitoring and troubleshooting data analytics pipelines:

1. Metrics: CloudWatch can be used to collect and track metrics from various AWS services, including those used in data analytics pipelines such as Amazon S3, Amazon EMR, and Amazon Redshift. You can use these metrics to monitor the performance and resource usage of your data analytics pipeline, and set alarms to notify you when certain thresholds are breached.
2. Logs: CloudWatch can be used to collect and monitor log files from various sources, including those used in data analytics pipelines. This can be useful for troubleshooting issues with your pipeline, as it allows you to view detailed log data in real-time.
3. Alarms: CloudWatch can be used to set alarms that notify you when certain thresholds are breached. This can be useful for monitoring the health of your data analytics pipeline, and for being notified when something goes wrong.
4. Dashboards: CloudWatch also allows you to create custom dashboards to monitor specific aspects of your data analytics pipelines, such as the number of data processed, memory usage, and more.

In summary, Amazon CloudWatch is a monitoring service that can be used to monitor and troubleshoot data analytics pipelines. It can be used to collect and track metrics, collect and monitor log files, set alarms, and create custom dashboards to monitor specific aspects of your data analytics pipelines.

What do you understand by AWS?

Answer: Amazon Web Services or AWS is a collection of cloud computing services and devices from Amazon. It contributes over 200 complete data center services worldwide. AWS is a cross-functional program that administers a broad variety of services varying from data warehousing to content delivery.

Explain Data Quality.

Answer: Data quality assesses whether information can assist its mission in an appropriate context (such as data analysis, for example).

What are the characteristics of the Data quality?

Answer: There are five characteristics that one finds within data quality which are:

• Accuracy
• Completeness
• Reliability
• Relevance
• Timeliness’

What do you reckon about the “completeness” of data?

Answer: “Completeness” indicates how extensive the information is. When resembling data completeness, consider whether all of the information you want is available; you might require a client’s first and last name, but the middle initial may be arbitrary.

Describe Stream Processing.

Answer: Stream Processing is considered a Big data technology. It is utilized to question continuous data stream and identify conditions, promptly, within a short time period from the time of obtaining the data. The detection time period ranges from few milliseconds to minutes. For instance, with stream processing, one can obtain an alarm when the temperature has approached the freezing point, questioning data streams emanating from a temperature sensor.

What is Data Governance?

Answer: Data governance indicates making certain you have methods in place to regulate your data and ensure that all regulations are satisfied in all your organization’s data practices. Adequate compliance can only grow with holistic and impeccable access to your data governance policy. 

What are the interpretive aspects of creating an effective data governance strategy?

Answer: The three important aspects of creating an effective data governance strategy are the people, processes, and technology. With an efficient strategy, not only can you guarantee that your organization continues compliant, but you can also supplement value to your overall business plan.

Difference between Data governance and data management?

Answer: Data governance is only one component of the whole discipline of data administration, but an important one. Whereas data governance is regarding the tasks, responsibilities, and methods for assuring accountability for and ownership of data assets. However, data management is “an overarching phrase that illustrates the methods used to design, specify, enable, build, acquire, control, maintain, archive, use, retrieve, and purge data.”

While data management is a common term for the discipline, it is sometimes applied to as data resource management or enterprise information management (EIM).

What should be the purpose of Data Governance?

Answer: An organization’s key purposes should be to:

• Reduce risks
• Install domestic rules for data use
• Complete compliance necessities
• Promote internal and external communication
• Enhance the value of data
• Promote the administration of the above
• Decrease costs
• Assist to secure the continued existence of the company by risk management and optimization

What are the advantages of Data Governance?

Answer: Some of the advantages include:

• More reliable, more general decision support arising from consistent, uniform data crosswise the organization
• Fair rules for changing methods and data that support the business and IT become more flexible and scalable
• Decreased costs in other sectors of data management by the provision of central control devices
• Improved efficiency through the capability to reuse methods and data
• Increased confidence in data property and documentation of data methods
• Enhanced compliance with data management

Explain Data Encryption.

Answer: In the computing world, encryption means the transformation of data from a readable form into an encoded arrangement that can only be read or concocted after it’s been decrypted. Encryption is the fundamental building section of data security and is the most manageable and most important way to secure a computer system’s information that can’t be withdrawn and understood by someone who needs to use it for bad means.

What are the methods used for encryption?

Answer: A number of techniques are used to code and decode messages, and those arrangements grow as computer software and systems for preventing and keeping information. These techniques include:

• Symmetric Key Cipher: Also acknowledged as a secret key algorithm, this is a unique system of decoding the information that must be presented to the receiver before the information can be decoded. The key utilized to encode is identical to the one used to decode, which makes it best for singular users and closed practices. Unless, the key has to be conveyed to the receiver, which enhances the uncertainty of compromise if it’s blocked by a third party, such as a hacker. The advantage is that this process is much quicker than the asymmetric method.
• Asymmetric Cryptography: This system uses two separate keys — public and private — that are connected together mathematically. The keys are actually just large numbers that have been joined with each other but aren’t equal, hence the phrase asymmetric. The public key can be distributed to anyone, but the private key must continue a secret. Both can be used to encrypt a piece of information, and the opposite key from the one formerly used to encrypt that message is then utilized to decode it.

What do you understand Data Visualization?

Answer: Data Visualization can be defined as the information which has been withdrawn in some schematic structure, involving attributes or variables for the blocks of information. In another word, it is a logical way to visually express quantitative content. The data may be expressed in many various ways, such as a bar chart, pie chart, line graph, scatter plot, or map.

What are the basic principles of data visualization?

Answer: The basic principles of data visualization are:

• Determine a Clear Purpose
• Understand the Audience
• Use Visual Characteristics to Show the Data Properly
• Keep It Organized and Consistent

Name and explain the popular types of charts for data visualization?

Answer: The most popular types of charts for data visualization are:

• Line Charts: Line charts should be practiced to distinguish values over time, and are great for displaying both large and small differences. They can also be related to comparing changes to more than one group of data.
• Bar Charts: Bar charts are used to correlate quantitative data from various categories. They can be utilized to trace changes over time as well, but are properly used only when those changes are important.
• Scatter Plots: Scatter plots should be handled to present prices for two variables for a set of data. They’re great for examining the connections between the two sets.
• Pie Charts: Pie charts are used to display parts of a whole. They can’t show things like changes over time.

How do you think can one data visualization inclusive?

Answer: Color is applied extensively as a means to design and distinguish information. It is a key factor in user decisions. We should think about how people react to distinctive color combinations applied in charts, considering that they would have more influential preferences for palettes that had subtle color modifications since they would be extra aesthetically appealing.

There are methods that can improve the graph readability:

• Use colors that have high contrast.
• Complement the use of color with pattern or texture to convey different types of information.
• Use text or icons to label elements.

What is the need for data visualization?

Answer: We require data visualization because a visual review of information presents it easier to recognize designs and trends than seeming through thousands of rows on a spreadsheet. Since the scope of data analysis is to obtain insights, data is much more important when it is visualized. Even if a data analyst can draw insights from data without visualization, it will be tough to describe the purpose without visualization. Charts and graphs make transferring data findings easier if you can recognize the patterns without them.

What do you understand by the Data mining?

Answer: It the processing pipeline supports searching very huge collections of records to find items of interests.

Explain data processing.

Answer: Data processing is the process of assembling raw data and transcribing it into useful information. It is normally performed in a step-by-step method by a crew of data engineers and data scientists in an association. The raw data is obtained, filtered, sorted, prepared, analyzed, saved, and then displayed in a readable arrangement.

What are the six main actions in the data processing cycle?

Answer: The six main steps in the data processing cycle are:

• Collection- The acquisition of raw data is the initial level of the data processing cycle.
• Preparation- Data preparation or we can say data cleaning is the method of classifying and separating the raw data to eliminate irrelevant and inaccurate data.
• Input- In this step, the raw data is transformed into machine-readable form and maintained in the processing unit.
• Data Processing- At this level, the raw data is constrained to various data processing arrangements utilizing machine learning and artificial intelligence algorithms to make the wanted output.
• Output- The data is ultimately transferred and displayed to the user in a compelling form like tables, graphs, audio, vector files, video, documents, etc. This output can be saved and further concocted in the next data processing cycle. 
• Storage- The ultimate step of the data processing series is storage, where data and metadata are stored for additional use. This provides for quick admittance and retrieval of information whenever required, and also enables it to be used as input in the next data processing cycle undeviatingly.

Define data access patterns.

Answer: Access patterns or query patterns means how the users and the system access the information or data to satisfy business requirements.

Name the data access patterns.

Answer: Aiming to implement the access to the data managed by applications it was designed a lot of patterns, they being the main following:

• Table Data Gateway
• Data Mapper
• Data Access Object (DAO)
• Row Data Gateway
• Active Record
• Repository

What is Row Data Gateway?

Answer: The Row Data Gateway pattern is the class row data gateway, that is competent to maintain the data access of only one business model object situation, each instance of row data gateway represents the data of one business model object instance. The Row Data Gateway is responsible only for managing the storage of data, so to retrieve data from a database is used a class named finder, which is responsible for doing the necessary queries on the database. 

Explain the pattern Data Mapper.

Answer: In the Data Mapper pattern, the principal feature is the data mapper class, which is capable to understand business model objects and then save and load them from the database, decoupling the business model things from the database.

What do you understand by metadata catalog?

Answer: A metadata catalog is a compilation of all the data about your data. Metadata can incorporate the origin, data source, owner, and other properties of a data set. These assist you to acquire more about a data set and estimate if it is well-suited for your use case.

What is the distinction between data and metadata? 

Answer: Data is the information and knowledge that measures, describes or reports on something. On the other hand, Metadata is related information that gives context about that data.

Do you have any kind of certification to expand your opportunities as an AWS Data Analytics Specialist?

Answer: Usually, interviewers look for applicants who are solemn about improving their career options by producing the use of further tools like certifications. Certificates are obvious proof that the candidate has put in all attempts to learn new abilities, comprehend them, and put them into use at the most excellent of their capacity. Insert the certifications, if you have any, and do hearsay about them in brief, describing what you learned from the programs and how they’ve been important to you so far.

Do you have any prior experience serving in an identical industry like ours?

Answer: Here comes an outspoken question. It aims to evaluate if you have the industry-specific abilities that are required for the contemporary role. Even if you do not hold all of the skills and experience, make certain to completely describe how you can still make utilization of the skills and knowledge you’ve accomplished in the past to serve the company.

Why are you preparing for the AWS Data Analytics Specialist position in our company specifically?

Answer: By this question, the interviewer is attempting to see how well you can influence them concerning your knowledge in the subject, besides the requirement for practicing AWS Data Analytics Specialty methodologies. It is always an advantage to already know the job specification in particular, along with the company’s return and aspects, thereby achieving a comprehensive knowledge of what tools, every subject methodologies are needed to work in the role triumphantly.

We at Testprep training hope that this article help the candidate to successfully clear the AWS Data Analytics Specialty Job Interview! The candidate can also refer to the AWS Data Analytics Specialty practice test because Practice makes a man perfect!

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