Top Technical Interviews Questions and Answers for AWS Cloud, Java, Oracle

AWS Cloud Practitioner Essentials Questions and Answers

Ques: 1). What is AWS SAM, or AWS Serverless Application Model?

Answer:

An open source framework for creating serverless apps is called AWS Serverless Application Model (AWS SAM). It offers a concise syntax for expressing functions, APIs, databases, and mappings of event sources. You use YAML to represent the application you want and just need a few lines to declare each resource.

Ques: 2). How do we begin developing apps based on SAM?

Answer:

Use the AWS SAM CLI to begin developing SAM-based apps. You may locally create, test, and debug apps specified by SAM templates or by using the AWS Cloud Development Kit thanks to SAM CLI's Lambda-like execution environment (CDK). You can also design secure continuous integration and deployment (CI/CD) pipelines that adhere to best practises and interface with both AWS's built-in and external CI/CD systems using the SAM CLI to deploy your apps to AWS.

Under the Apache 2.0 licence, SAM and the SAM CLI are open-sourced. You may add updates and new features to SAM via GitHub, as well as SAM CLI.

Ques: 3). How much does it cost to utilise AWS SAM?

Answer:

The use of AWS SAM is free of charge. The same way you would if you had created the AWS resources manually, you pay for those produced through SAM. Only what you use, as you use it, is charged. There are no minimum costs or up-front obligations.

Ques: 4). AWS SAM is it open-source?

Answer:

Under the Apache 2.0 licence, AWS SAM and the SAM CLI are open-sourced. AWS SAM on GitHub and the AWS SAM CLI on GitHub both accept contributions for new features and improvements.

Ques: 5). A SAM template: what is it?

Answer:

The architecture of a serverless application is represented by a SAM template file, which is a YAML configuration. You specify all of the AWS resources that are a part of your serverless application using the template. Any resource that you may declare in an AWS CloudFormation template can also be declared in an AWS SAM template since AWS SAM templates are an extension of AWS CloudFormation templates.

Ques: 6). What is the AWS SAM CLI capable of?

Answer:

A command-line interface that facilitates the development of SAM-based apps is the AWS SAM CLI. You may deploy your serverless application to the AWS Cloud, generate a deployment package for it, and run Lambda functions locally using the AWS SAM CLI.

Ques: 7). How is AWS SAM put to use?

Answer:

To express functions, APIs, databases, and event source mappings, AWS SAM offers shorthand syntax. SAM extends and translates the SAM syntax into AWS CloudFormation syntax during deployment. After that, CloudFormation assigns dependable deployment capabilities to your resources.

AWS Elastic Block Store (EBS) Interview Questions and Answers

Ques: 8). AWS SAM CLI: What is it?

Answer:

You may locally develop, test, and debug serverless applications specified by AWS SAM templates using the SAM Command Line Interface (CLI). SAM CLI offers a local execution environment similar to Lambda and aids in problem detection.

AWS Secrets Manager Interview Questions and Answers

Ques: 9). How can I begin using AWS SAM?

Answer:

Install the AWS SAM CLI, and then use the sam init command to create a pre-configured AWS SAM template with example application code to get started with SAM.

AWS Solution Architect Interview Questions and Answers

Ques: 10). Which languages can I use with AWS SAM?

Answer:

Any runtime that is supported by AWS Lambda may be used to develop serverless apps using AWS SAM. Additionally, you may locally debug Lambda functions created in Node.js, Java, Python, and Go using the SAM CLI.

Ques: 11). How can I set up the AWS SAM CLI?

Answer:

Pip may be used to install AWS SAM CLI on Linux, Mac, or Windows.

Ques: 12). AWS SAM is supported by which AWS regions?

Answer:

All areas where AWS Lambda is offered also provide AWS SAM. You can consult the AWS Lambda region table that is accessible.

AWS Aurora Interview Questions and Answers

Ques: 13). What kinds of commands can the SAM CLI execute?

Answer:

A increasing number of commands are supported by SAM CLI. The sam init command creates sample application code in the language of your choosing together with pre-configured AWS SAM templates. By running your function code locally in an execution environment similar to Lambda, the sam local command facilitates local invocation and testing of your Lambda functions and SAM-based serverless apps. The sam local command may also be used to create test payloads locally, launch a local endpoint for API testing, or automate Lambda function testing. You may combine your application code and dependencies into a "deployment package" and then deploy your serverless application to the AWS Cloud using the sam package and sam deploy commands. Finally, the sam logs command enables you to fetch, tail, and filter logs for Lambda functions.

November 01, 2022

Top 20 AWS SQS Interview Questions and Answers

Ques: 1). What distinguishes Amazon SQS from Amazon MQ?

Answer:

Consider Amazon MQ if you want to rapidly and simply migrate your messaging to the cloud while still utilising it with your current apps. You may transition from any standards-based message broker to Amazon MQ without having to completely rewrite the messaging code in your apps since it supports industry-standard APIs and protocols. We advise you to take Amazon SQS and Amazon SNS into account while developing brand-new cloud-based apps. Amazon SQS and SNS are lightweight, fully managed message queue and topic services that offer straightforward, user-friendly APIs and almost indefinite scalability.

AWS(Amazon Web Services) Interview Questions and Answers

Ques: 2). What distinguishes Amazon SQS from Amazon Kinesis Streams?

Answer:

For storing messages as they go between applications or microservices, Amazon SQS provides a dependable, highly scalable hosted queue. It transfers data among various application components and aids in decoupling them. Common middleware components like dead-letter queues and poison-pill management are offered by Amazon SQS. Any programming language that the AWS SDK supports can access it, and it also offers a general web services API. Standard and FIFO queues are both supported by Amazon SQS.

Real-time processing of streaming large data and the ability to read and replay records to numerous Amazon Kinesis Applications are made possible by Amazon Kinesis Streams. The Amazon Kinesis Client Library (KCL) makes it simpler to create numerous apps that read from the same Amazon Kinesis stream by sending all records for a particular partition key to the same record processor (for example, to perform counting, aggregation, and filtering).

Ques: 3). Dead-letter queues: what are they?

Answer:

If the consumer application for the source queue is unable to effectively consume the messages, the source queue may send the messages to a dead-letter queue on Amazon SQS. You can manage the life cycle of unconsumed messages and handle message consumption errors more easily with dead-letter queues. To identify problems with consumer applications, you may create an alert for any messages that are delivered to a dead-letter queue, go through the logs for any errors that led to their delivery to the queue, and check the substance of the messages. You can redrive the messages from your dead-letter queue to the source queue after your consumer application has been restored.

AWS Cloud Practitioner Essentials Questions and Answers

Ques: 4). How dependable is the data storage in Amazon SQS?

Answer:

In order to prevent message inaccessibility due to a single computer, network, or Availability Zone (AZ) failure, Amazon SQS stores all message queues and messages in a single, highly-available AWS region with many redundant Availability Zones (AZs). See Regions and Availability Zones in the Amazon Relational Database Service User Guide for further details.

AWS Identity and Access Management (IAM) rules are similar to the policies used by Amazon SQS's resource-based permissions system in that both employ policies defined in the same language. For instance, both use variables.

The Transport Layer Security (TLS) and HTTP over SSL (HTTPS) protocols are supported by Amazon SQS. Most clients can automatically negotiate to use newer versions of TLS without any code or configuration change. Amazon SQS supports versions 1.0, 1.1, and 1.2 of the Transport Layer Security (TLS) protocol in all regions.

Ques: 5). Do you provide message purchasing using Amazon SQS?

Answer:

Yes. First-in, first-out (FIFO) queues maintain the precise sequence of messages delivered and received. You don't need to provide sequencing information in your messages if you utilise a FIFO queue. Standard queues include a loose-FIFO feature that makes an effort to maintain the message order. Receiving messages in the precise sequence they are sent is not guaranteed, however, because conventional queues are intended to be immensely scalable utilising a widely dispersed architecture.

AWS Simple Storage Service (S3) Interview Questions and Answers

Ques: 6). What advantages does Amazon SQS have over custom-built or pre-packaged message queuing systems?

Answer:

Compared to developing your own message queue management software or utilising open-source or commercial message queuing solutions, which take a substantial amount of setup time, Amazon SQS offers a number of benefits.

These options demand continuing system management and hardware maintenance resources. The requirement for redundant message storage, which guarantees that messages are not lost in the event of hardware failure, further increases the complexity of creating and operating these systems.

Amazon SQS, in comparison, needs very minimal configuration and no expense in terms of administration. On a huge scale, Amazon SQS processes billions of messages every day. Without any configuration, you may adjust the amount of traffic you send to Amazon SQS. Amazon SQS also provides extremely high message durability, giving you and your stakeholders added confidence.

Ques: 7). Is Amazon SQS compatible with other AWS services?

Answer:

Yes. By combining Amazon SQS with computing services like Amazon EC2, Amazon Elastic Container Service (ECS), and AWS Lambda as well as storage and database services like Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB, you can increase the scalability and flexibility of your applications.

Ques: 8). What is lengthy polling for Amazon SQS?

Answer:

You may obtain messages from your Amazon SQS queues using extended polling. Long polling doesn't respond until a message enters the message queue or the long poll times out, whereas conventional short polling responds, even if the message queue being polled is empty.

It is cheap to collect messages from your Amazon SQS queue as soon as they become available thanks to long polling. If you employ lengthy polling, you can cut down on the amount of empty receives, which might lower the cost of utilising SQS.

AWS Elastic Block Store (EBS) Interview Questions and Answers

Ques: 9). How can I monitor and control the expenses related to my Amazon SQS queues?

Answer:

Utilizing cost allocation tags, you may label and monitor your queues for resource and expense control. A key-value pair makes up a tag, which is a type of metadata label. You may, for instance, categorise and track your expenditures depending on the cost centres you use to tag your queues.

AWS Secrets Manager Interview Questions and Answers

Ques: 10). What advantages does SSE have for Amazon SQS?

Answer:

Sensitive data may be sent in encrypted queues using SSE. Using keys maintained by the AWS Key Management Service, SSE secures the contents of messages in Amazon SQS queues (AWS KMS). As soon as messages are received by Amazon SQS, SSE encrypts them. The messages are stored in encrypted form and Amazon SQS decrypts messages only when they are sent to an authorized consumer.

AWS Cloud Support Engineer Interview Question and Answers

Ques: 11). What distinguishes Amazon Simple Notification Service (SNS) from Amazon SQS?

Answer:

Applications can use Amazon SNS to deliver time-sensitive messages to a large number of subscribers instead of periodically checking or polling for updates. A message queuing service called Amazon SQS may be used to separate the sending and receiving parts of distributed applications so that messages can be exchanged using a polling mechanism.

AWS Solution Architect Interview Questions and Answers

Ques: 12). What number of copies will I get of a message?

Answer:

FIFO queues are made to ensure that no messages are ever introduced twice. In some circumstances, though, your message producer could add duplicates. For instance, if the producer sends a message, doesn't get a reply, and then sends the identical message again. The deduplication mechanism offered by the Amazon SQS APIs stops your message producer from sending duplicate messages. Within a 5-minute deduplication interval, the message producer's duplicates are eliminated.

You could occasionally get a duplicate copy of a message for regular queues (at-least-once delivery). If you utilise a normal queue, you must create idempotent apps (that is, they must not be affected adversely when processing the same message more than once).

Ques: 13). How are unprocessable messages handled by Amazon SQS?

Answer:

Dead letter queues in Amazon SQS may be set up using the console or the API to accept messages from other source queues. You must use RedriveAllowPolicy to specify the correct permissions for the dead letter queue redrive when configuring a dead letter queue.

The dead-letter queue redrive permission's parameters are included in RedriveAllowPolicy. As a JSON object, it specifies which source queues are permitted to declare dead-letter queues.

After creating a dead letter queue, messages are sent to it if the processing cannot be finished within a certain number of attempts. Dead letter queues can be used to collect unprocessable messages for subsequent examination.

AWS Aurora Interview Questions and Answers

Ques: 14). Does message metadata support Amazon SQS?

Answer:

Yes. A message from Amazon SQS may have up to 10 metadata characteristics. The substance of a message may be distinguished from the metadata that describes it using message attributes. Because your apps don't have to analyse a complete message before knowing how to process it, information is processed and stored more quickly and efficiently.

Name-type-value triples are used for Amazon SQS message characteristics. The available types are numeric, binary, and string (including integer, floating-point, and double).

AWS ActiveMQ Interview Questions and Answers

Ques: 15). Do I need to update my application in order to use the Java version of the AmazonSQSBufferedAsyncClient?

Answer:

No. A drop-in replacement for the current AmazonSQSAsyncClient is provided by the AmazonSQSBufferedAsyncClient for Java.

Your application will gain the advantages of automated batching and prefetching if you update it to utilise the most recent AWS SDK and switch your client to use the AmazonSQSBufferedAsyncClient for Java instead of the AmazonSQSAsyncClient.

AWS CloudFormation Interview Questions and Answers

AWS GuardDuty Questions and Answers

Ques: 16). What timeout setting should I use for my long-poll?

Answer:

A long-poll timeout should generally be limited to 20 seconds. Set your long-poll timeout as high as you can since it will result in fewer empty ReceiveMessageResponse objects being returned.

Set a shorter long-poll timeout, as low as 1 second, if the 20-second limit is insufficient for your application.

By default, all AWS SDKs operate with polls that last 20 seconds. You might need to change your Amazon SQS client to enable longer queries or to use a shorter long-poll timeout if you don't use an AWS SDK or if you set your AWS SDK to explicitly have a lower timeout.

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Ques: 17). How can I set the Amazon SQS limit message size?

Answer:

Use the console or the SetQueueAttributes method to set the MaximumMessageSize property to the desired value. The maximum amount of bytes that an Amazon SQS message may contain is specified by this parameter. Set this attribute's value to anything between 1 KB (1,024 bytes) to 262,144 bytes (256 KB).

Make advantage of the Amazon SQS Extended Client Library for Java to transmit messages bigger than 256 KB. With the help of this library, you are able to send an Amazon SQS message with a reference to a message payload in Amazon S3 that is up to 2 GB in size.

AWS Data Pipeline Interview Questions and Answers

Amazon CloudSearch Interview Questions and Answers

Ques: 18). Why do the actions of ReceiveMessage and DeleteMessage exist separately?

Answer:

Whether or not you get a message that Amazon SQS returns to you, the message remains in the message queue. The deletion request confirms that you have finished processing the message, and it is your responsibility to delete the message.

If you don't remove the message, Amazon SQS will resend it when it gets a new request to accept it.

AWS Transit Gateway Interview Questions and Answers

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Ques: 19). Is it possible to remove every message from a message queue without removing the queue itself?

Answer:

Yes. The PurgeQueue action allows you to remove every message from an Amazon SQS message queue.

All of the messages that have already been sent to a message queue are erased when you purge it. There is no need to change the message queue's configuration because your message queue and its properties are still there. Use the DeleteMessage or DeleteMessageBatch actions to specifically delete particular messages.

AWS(Amazon Web Services) Interview Questions and Answers

Ques: 20). How do messaging groups work?

Answer:

Within a FIFO queue, messages are organised into discrete, sequential "bundles." All messages are sent and received in exact sequence for each message group ID. Messages with various message group ID values, nevertheless, could be sent and received out of chronological order. A message must be linked to a message group ID. The action fails if a message group ID is not supplied.

If messages with the same message group ID are transmitted to a FIFO queue by several hosts (or separate threads on the same host), Amazon SQS distributes the messages in the order in which they arrive for processing.

October 07, 2022

Top 20 AWS MSK Interview Questions and Answers

Developers and DevOps managers can easily run Apache Kafka applications and Kafka Connect connectors on AWS without having to become experts in Apache Kafka administration thanks to Amazon Managed Streaming for Apache Kafka (Amazon MSK), an AWS streaming data service that manages Apache Kafka infrastructure and operations. Streaming data application development is sped up by Amazon MSK's built-in AWS connectors, enterprise-grade security capabilities, and ability to administer, maintain, and grow Apache Kafka clusters.

Ques: 1). What is streaming data in AWS MSK?

Answer:

The answer is that streaming data is a constant stream of brief recordings or events—typically only a few kilobytes in size—produced by tens of thousands of equipment, gadgets, websites, and software programmes. A wide range of data, including log files produced by users of your mobile or web applications, e-commerce purchases, in-game player activity, information from social networks, trading information from financial trading floors, geospatial services, security logs, metrics, and telemetry from connected devices or instrumentation in data centres are all examples of streaming data. Continuously gathering, processing, and delivering streaming data is made simple for you by streaming data services like Amazon MSK and Amazon Kinesis Data Streams.

Ques: 2). What does Amazon MSK really do as open-source service?

Answer:

Apache Kafka open-source versions may be easily installed and deployed on AWS with excellent availability and security thanks to Amazon MSK. Additionally, Amazon MSK provides AWS service integrations without the operational burden of maintaining an Apache Kafka cluster. While the service supports the setup, provisioning, AWS integrations, and ongoing maintenance of Apache Kafka clusters, Amazon MSK enables you to use open-source versions of Apache Kafka.

AWS Cloud Practitioner Essentials Questions and Answers

Ques: 3). What are Apache Kafka's fundamental ideas?

Answer:

Topics are how Apache Kafka stores records. Consumers read records from subjects, and data producers write records to topics. In Apache Kafka, each record is made up of a key, a value, a timestamp, and occasionally header metadata. Apache Kafka divides topics into replicas that are replicated over several brokers, or nodes. A highly available cluster of brokers running Apache Kafka may be created by placing brokers in different AWS availability zones. When it comes to managing state for services communicating with an Apache Kafka cluster, Apache Kafka depends on Apache ZooKeeper.

Ques: 4). How can I get access to the Apache Kafka broker logs?

Answer:

For provisioned clusters, broker log delivery is an option. Broker logs may be sent to Amazon Kinesis Data Firehose, Amazon Simple Storage Service (S3), and Amazon CloudWatch Logs. Among other places, Kinesis Data Firehose supports Amazon OpenSearch Service.

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Ques: 5). How can I keep track of consumer lag?

Answer:

The standard collection of metrics that Amazon MSK delivers to Amazon CloudWatch for all clusters includes topic-level consumer latency indicators. For these metrics to be obtained, no further setup is needed. You may also obtain consumer latency data at the partition level for provisioned clusters (partition dimension). On your cluster, turn on enhanced monitoring (PER PARTITION PER TOPIC). As an alternative, you may use a Prometheus server to activate Open Monitoring on your cluster and collect partition-level metrics from the cluster's brokers. Consumer latency measurements, like other Kafka metrics, are accessible through port 11001.

Ques: 6). How does Amazon MSK handle data replication?

Answer:

To replicate data between brokers, Amazon MSK leverages the leader-follower replication feature of Apache Kafka. Clusters with multi-AZ replication may be easily deployed using Amazon MSK, and you have the option to apply a specific replication technique for each topic. Every replication option by default deploys and isolates leader and follower brokers according to the replication technique chosen. A cluster of three brokers will be created by Amazon MSK (one broker in three AZs in a region), for instance, if you choose a three AZ broker replication strategy with one broker per AZ cluster. By default (unless you choose to override the topic replication factor), the topic replication factor will also be three.

Ques: 7). MSK Serverless: What is it?

Answer:

You may operate Apache Kafka clusters using MSK Serverless, a cluster type for Amazon MSK, without having to worry about managing computation and storage capacity. You just pay for the data volume that you stream and keep when using MSK Serverless, which allows you to execute your apps without needing to setup, configure, or optimise clusters.

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Ques: 8). What security features are available with MSK Serverless?

Answer:

Using service-managed keys obtained from the AWS Key Management Service, MSK Serverless encrypts all data in transit and at rest (KMS). AWS PrivateLink is used by clients to establish private connections to MSK Serverless, shielding your traffic from the public internet. IAM Access Control, another feature of MSK Serverless, allows you to control client authorization and client authentication for Apache Kafka resources like topics.

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Ques: 9). What do I require to provision a cluster of Amazon MSK?

Answer:

With each cluster you build for provided clusters, you must provision broker instances and broker storage. Storage throughput for storage volumes is an optional provision that may be used to expand I/O without the need for additional brokers. Nodes for Apache ZooKeeper are already included with each cluster you establish, so you don't need to supply them. You just construct a cluster as a resource for serverless clusters.

AWS Cloud Support Engineer Interview Question and Answers

Ques: 10). How does Amazon MSK handle authorization?

Answer:

If you are using IAM Access Control, Amazon MSK authorises actions based on the policies you create and its own authorizer. Apache Kafka employs access control lists (ACLs) for authorisation if you are utilising SASL/SCRAM or TLS certificate authentication. You must enable client authentication using SASL/SCRAM or TLS certificates in order to activate ACLs.

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Ques: 11). What is the maximum data throughput capacity supported by MSK Serverless?

Answer:

Up to 200 MBps of write throughput and 400 MBps of read capacity per cluster are offered by MSK Serverless. Additionally, MSK Serverless allots up to 5 MBps of immediate write capacity and 10 MBps of instant read capacity per partition to guarantee enough throughput availability for every partition in a cluster.

Ques: 12). What high availability measures does MSK Serverless take?

Answer:

When a partition is created, MSK Serverless makes two copies of it and stores them in various availability zones. To provide high availability, MSK serverless automatically finds and restores malfunctioning backend resources.

AWS Aurora Interview Questions and Answers

Ques: 13). How can I set up my first MSK cluster on Amazon?

Answer:

Using the AWS administration console or the AWS SDKs, you can quickly establish your first cluster. To construct an Amazon MSK cluster, first choose an AWS region in the Amazon MSK dashboard. Give your cluster a name, decide the Virtual Private Cloud (VPC) you want to use to run it, and select the subnets for each AZ. You may select a broker instance type, the number of brokers per AZ, and the amount of storage per broker when constructing a provisioned cluster.

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Ques: 14). Does Amazon MSK run in an Amazon VPC?

Answer:

Yes, Amazon MSK always operating inside an Amazon VPC that is overseen by the Amazon MSK service. When the cluster is configured, the Amazon MSK resources will be accessible to your own Amazon VPC, subnet, and security group. Elastic network interfaces (ENIs), which connect IP addresses from your VPC to your Amazon MSK resources, ensure that all network traffic stays within the AWS network and is not by default available to the internet.

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Ques: 15). Between my Apache Kafka clients and the Amazon MSK service, is data secured in transit?

Answer:

Yes, only clusters established using the CLI or AWS Management Console have in-transit encryption configured by default to TLS. For clients to communicate with clusters utilising TLS encryption, further setup is needed. By choosing the TLS/plaintext or plaintext options, you may modify the default encryption configuration for supplied clusters. Study up on MSK Encryption.

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Ques: 16). How much do the various CloudWatch monitoring levels cost?

Answer:

The size of your Apache Kafka cluster and the monitoring level you choose will determine how much it costs to monitor your cluster using Amazon CloudWatch. Amazon CloudWatch has a free tier and charges monthly based on metrics.

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Amazon CloudSearch Interview Questions and Answers

Ques: 17). Which monitoring tools are compatible with Prometheus' Open Monitoring?

Answer:

Open Monitoring is compatible with tools like Datadog, Lenses, New Relic, Sumo Logic, or a Prometheus server that are made to read from Prometheus exporters.

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Ques: 18). Are my clients' connections to an Amazon MSK cluster secure?

Answer:

By default, a private connection between your clients in your VPC and the Amazon MSK cluster is the only way data may be generated or consumed from an Amazon MSK cluster. But if you enable public access for your Amazon MSK cluster and use the public bootstrap-brokers string to connect to it, the connection—while authenticated, permitted, and encrypted—will no longer be regarded as private. If you enable public access, it is advised that you setup the cluster's security groups to include inbound TCP rules that permit public access from your trusted IP address and to make these rules as stringent as feasible.

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Ques: 19). Is it possible to move data from my current Apache Kafka cluster to Amazon MSK?

Answer:

Yes, you may duplicate data from clusters onto an Amazon MSK cluster using third-party tools or open-source tools like MirrorMaker, supported by Apache Kafka. To assist you with completing a migration, Amazon provides an Amazon MSK migration lab.

Ques: 20). How do I handle data processing for my MSK Serverless cluster?

Answer:

You can process data in your MSK Serverless cluster topics using any technologies that are Apache Kafka compliant. MSK Serverless interacts with AWS Lambda for event processing and Amazon Kinesis Data Analytics for stateful stream processing using Apache Flink. Kafka Connect sink connectors may be used to transmit data to any desired location.

April 19, 2022

Top 20 AWS Aurora Interview Questions and Answers

AWS Aurora is an Amazon cloud-based managed database service. This is one of the most extensively utilised data storage and processing services for low latency and transactional data. The AWS aurora service combines the benefits of open source databases such as MySQL and PostgreSQL with enterprise-level dependability and scalability. For efficient data availability, it uses a clustered technique with data replication in the AWS availability zone. It is much faster than native MySQL and PostgreSQL databases, and it requires little server maintenance. It has a large storage capacity and can expand up to 64 Terabytes of database size for enterprise use.

AWS Cloud Practitioner Interview Questions and Answers

Ques. 1): What is Amazon Aurora and how does it work?

Answer:

AWS Aurora is a cloud-based relational database that combines the performance and availability of typical enterprise databases with the ease of use and low cost of open source databases. It's five times faster than a typical MySQL database, and three times faster than a standard PostgreSQL database.

AWS Aurora helps provide commercial databases with security, availability, and dependability. It is fully managed by AWS Relational Database Service (RDS), which automates time-consuming administration activities including hardware provisioning, database setup, patching, and backups. It's a fault-tolerant, self-healing storage system that auto-scales up to 128TB per database instance and provides high performance and availability with up to 15 low-latency read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across three Availability Zones, among other features (AZs).

Ques. 2): What are Amazon Aurora DB clusters, and what do they do?

Answer:

An Amazon Aurora DB cluster is made up of one or more database instances and a cluster volume that stores the data for those databases.

An Aurora cluster volume is a virtual database storage volume that spans multiple Availability Zones and contains a copy of the DB cluster data in each. There are two sorts of database instances in an Aurora DB cluster:

Primary DB instance: Supports read and write operations and handles all data modifications to the cluster volume. There is only one primary DB instance in each Aurora DB cluster.

Aurora Replica: It connects to the same storage disc as the primary DB instance and only enables read operations. Each Aurora DB cluster can contain up to 15 Aurora Replicas in addition to the original DB instance. Aurora automatically switches to an Aurora Replica if the primary DB instance becomes unavailable. The Aurora Replicas' failover priority can be set. Aurora Replicas can also transfer read workloads from the primary DB instance.

Ques. 3): What are the benefits of using Aurora?

Answer:

The following are some of Aurora's benefits:

Enterprise-level security: Because Aurora is an Amazon service, you may be confident in its security and use the IAM capabilities.

Enterprise-level availability: It is ensured by multiple replications of database instances across several zones.

Enterprise-level scalability: With Aurora serverless, you can configure your database to scale up and down dynamically in response to application demand.

Enterprise-level performance: Open-source DB's simplicity and cost-effectiveness.

Aurora is interoperable with MySQL and PostgreSQL at the enterprise level. If your present application is built on MySQL or PostgreSQL, you can move it or utilise Amazon RDS to convert your database to Aurora Engine.

AWS Management Console: Amazon Management Console is easy to use with click and drag features to quickly set-up your Aurora Cluster.

Maintenance: Aurora has almost zero server maintenance. 5 times faster than MySQL and 3 times faster than PostgreSQL.

AWS Simple Storage Service (S3) Interview Questions and Answers

Ques. 4): What are Aurora's advantages?

Answer:

The following are some of the advantages of AWS Aurora:

High Performance and Scalability – We can quickly scale up and down our database deployment from a smaller to a larger instance.

Fully Managed - Because Amazon Relational Database Service (RDS) manages Aurora, we don't have to bother about database management activities like hardware provisioning, software patching, setup, configuration, or backups.

Highly Secure - Aurora is very secure, with various levels of security for your database.

Support for Database Migrations to the Cloud - Aurora is utilised as an attractive target for database migrations to the cloud.

MySQL and PostgreSQL Compatible - Aurora is entirely compatible with existing MySQL and PostgreSQL open source databases, and support for new releases is added on a regular basis.

High Availability and Durability - Aurora's high availability and durability make it simple to recover from physical storage failures.

Ques. 5): How Aurora Works?

Answer:

Primary DB and Aurora replica DB, as well as a cluster volume to handle the data for those DB instances, make up an Aurora DB cluster. Aurora cluster volume is a virtual database storage volume that spans multiple Availability Zones in order to better support global applications. The DB cluster data is duplicated in each zone.

All read and write operations are performed over cluster volume in the primary database. Each Aurora cluster will have one primary database instance.

It's just a copy of the primary database instance whose sole purpose is to provide data, i.e. solely read operations. To provide high availability in all Zones, a primary DB instance can have up to 15 replicas. In a fail-safe circumstance, Aurora will switch to a replica when a Primary DB is not accessible. Replicas aid in the reduction of read workload on the primary database. For replicas, you can set the priority of failover.

Aurora can have a multi-master cluster as well. All DB instances in a multi-master setup will be able to read and write data. In AWS language, these are known as reader and writer DB instances, and we can call this a multi-master replication.

You can also set up Amazon S3 to keep a backup of your database. Even in the worst-case scenario, where the entire cluster is down, your database remains safe.

You can utilise Aurora Serverless to automatically start scaling and shutting down the database to fit application demand for an unpredictable workload.

AWS Elastic Block Store (EBS) Interview Questions and Answers

Ques. 6): What are the advantages of using Amazon RDS with MySQL?

Answer:

Amazon RDS for MySQL has the following advantages:

Easy, managed deployments - : Simple, managed deployments are used to quickly launch and connect to a production-ready MySQL database.

High availability and read replicas - High availability and read replicas are utilised to ensure that our MySQL databases are available and durable.

Fast, dependable storage - utilised to provide two SSD-backed MySQL database storage alternatives.

Monitoring and metrics - Amazon RDS Enhanced Monitoring gives you access to more than 50 CPU, RAM, file system, and disc I/O metrics.

Backup and recovery - utilised to ensure that our MySQL database instance can be recovered.

Isolation and security – used to ensure that our MySQL databases are kept secure.

Ques. 7): What is the relationship between Aurora and Amazon RDS Engines?

Answer:

The following points will demonstrate how Amazon RDS' standard engines, such as MySQL and PostgreSQL, interact with Aurora:

When creating new database servers with Amazon RDS, you can select Aurora as a database engine.

If you're acquainted with Amazon RDS, Aurora should be simple to set up. You can utilise the Amazon RDS administration console to set up Aurora clusters, as well as the CLI commands and API to perform database maintenance activities like backup, recovery, and repair.

Aurora's automatic clustering, replication, and other administration operations are controlled over the entire cluster of database servers, not just one instance, allowing you to manage big MySQL and PostgreSQL servers efficiently and at a cheap cost.

Data from Amazon RDS for MySQL and PostgreSQL can be replicated or imported into Aurora using snapshots. Another feature is push-button migration, which may be used to migrate your Amazon RDS MySQL and PostgreSQL databases to Aurora.

Ques. 8): What are Endpoints and How Do I Use Them?

Answer:

When a user connects to an Aurora cluster, an endpoint is a combination of host name and port.

Endpoints are divided into four categories:

Cluster Endpoint: Cluster Endpoint is used to connect to the current primary database instance and to assist with write operations.

Custom endpoint: A custom endpoint is used to represent a set of DB instances selected by the user.

Reader Endpoint: Reader Endpoint is a read-only endpoint used to connect Aurora replicas.

Instance Endpoint: Instance Endpoint is used to connect to a specific database instance and to diagnose problems with capacity or performance in that instance.

AWS Cloud Interview Questions and Answers Part - 1

Ques. 9): How can we associate a IAM Role with an Aurora Cluster using CloudFormation?

Answer:

PostRunCommand:

Description: You must run this awscli command after the stack is created and may also need to reboot the cluster/instance.

Value: !Join [" ", [

"aws rds add-role-to-db-cluster --db-cluster-identifier",

!Ref AuroraSandboxCluster,

"--role-arn",

!GetAtt AuroraS3Role.Arn,

"--profile",

!FindInMap [ AccountNameMap, !Ref AccountNamespace, profile ]

]]

AWS Cloud Interview Questions and Answers Part - 2

Ques. 10): What are AWS Aurora's limitations?

Answer:

If you need additional features or have an older version of MySQL, you won't be able to use it because it only supports MySQL-5.6.10. Amazon will add new MySQL functionality to Aurora in the future, but you'll have to wait.

Because Aurora currently only supports InnoDB, you won't be able to use MyISAM tables.

With Aurora, you don't have the choice of using smaller RDS than r3.large.

AWS CloudFormation Interview Questions and Answers

Ques. 11): Is it possible for my application to fail over to the cross-region replica from my current primary?

Answer:

Yes, you can use the Amazon RDS console to promote your cross-region replica to the new primary. The promotion process for logical (binlog) replication takes a few minutes, depending on your workload. When you start the promotion process, the cross-region replication will halt.

You may promote a secondary region to take full read/write workloads in under a minute with Amazon Aurora Global Database.

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Ques. 12): What is Amazon RDS for MySQL and how does it work?

Answer:

AWS RDS for MySQL manages time-consuming database management activities including backups, software patching, monitoring, scaling, and replication, allowing you to focus on application development.

It is compatible with Amazon RDS Community Edition versions.

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Ques. 13): What does it mean to be "MySQL compatible"?

Answer:

Amazon Aurora is plug-and-play compatible with existing MySQL open-source databases, and new releases are added on a regular basis. This implies that using conventional import/export tools or snapshots, you can quickly move MySQL databases to and from Aurora. It also means that the majority of the code, apps, drivers, and utilities you already use with MySQL databases can be utilised with Aurora with little or no modification. When comparing Aurora with MySQL, keep in mind that the Amazon Aurora database engine is designed to be wire-compatible with MySQL 5.6 and 5.7, which uses the InnoDB storage engine. This makes switching applications between the two engines a breeze. Amazon Aurora does not support certain MySQL capabilities, such as the MyISAM storage engine.

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Ques. 14): How can I switch from MySQL to Amazon Aurora and back?

Answer:

There are various options available to you. To export data from MySQL and to import data into Amazon Aurora, use the normal mysqldump and mysqlimport utilities, respectively. You can also utilise the AWS Management Console to move an Amazon RDS for MySQL DB Snapshot to Amazon Aurora utilising Amazon RDS's DB Snapshot migration feature. Most customers have their migration completed in under an hour, while the time varies on the type and amount of the data set.

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Ques. 15): What does it mean to have "five times the performance of MySQL"?

Answer:

By tightly integrating the database engine with an SSD-based virtualized storage layer purpose-built for database workloads, Amazon Aurora improves MySQL performance by lowering writes to the storage system, avoiding lock contention, and eliminating delays caused by database process threads. Amazon Aurora offers over 500,000 SELECTs/sec and 100,000 UPDATEs/sec, five times faster than MySQL running the same benchmark on the same hardware, according to our tests with SysBench on r3.8xlarge instances.

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Ques. 16): What are the best practises for optimising my database workload for Amazon Aurora PostgreSQL-Compatible Edition?

Answer:

Amazon Aurora is designed to be PostgreSQL compatible, allowing existing PostgreSQL applications and tools to run without needing to be modified. However, Amazon Aurora outperforms PostgreSQL in the domain of highly concurrent workloads. We recommend building your applications to support a large number of concurrent queries and transactions in order to maximise your workload's throughput on Amazon Aurora.

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Ques. 17): What are the options for scaling the compute resources associated with my Amazon Aurora DB Instance?

Answer:

By selecting the desired DB Instance and clicking the Modify button in the AWS Management Console, you can scale the compute resources allocated to your DB Instance. Changing the DB Instance class modifies memory and CPU resources.

When you make modifications to your DB Instance class, they will be applied during the maintenance window you specify. You can also utilise the "Apply Immediately" flag to have your scaling requests applied right away. Both of these approaches will have a short-term impact on availability while the scaling operation is carried out. Remember that any other pending system modifications will be applied as well.

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Ques. 18): What is my plan of action if my database fails?

Answer:

Amazon Aurora keeps six copies of your data across three Availability Zones (AZs) and will attempt to recover your database in a healthy AZ without losing any data. You can restore from a DB Snapshot or perform a point-in-time restore procedure to a fresh instance if your data is unavailable within Amazon Aurora storage. For a point-in-time restoration procedure, the latest restoreable time can be up to five minutes in the past.