Top 20 AWS CodeGuru Interview Questions and Answers

Amazon CodeGuru is a developer tool that offers insightful suggestions to enhance code quality and pinpoint the most expensive lines of code in an application. Integrate CodeGuru into your current software development workflow to automate code reviews throughout application development, continuously track the performance of applications in use, and provide visual cues and recommendations on how to enhance code quality, accelerate applications, and lower overall costs.

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Ques. 1): How do I sign up for the Amazon CodeGuru Reviewer programme?

Answer:

To incorporate Amazon CodeGuru Reviewer recommendations into your code pull requests, go to the Amazon CodeGuru portal. To begin receiving suggestions from Amazon CodeGuru Reviewers, go to the CodeGuru interface and follow the instructions to link your AWS CodeCommit, GitHub, GitHub enterprise, and BitBucket repositories. As soon as it is turned on, Amazon CodeGuru Reviewer will automatically make wise suggestions as comments on the pull requests you create for the connected repositories.

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Ques. 2): How is CodeGuru Reviewer's open-source code analysis software integrated?

Answer:

Amazon Rules from three sources are incorporated by CodeGuru Reviewer. (2) A managed version of Bandit, an open source code analysis tool designed to find security issues in Python code, is integrated into CodeGuru Reviewer (3) A managed version of Infer, an open source code analysis tool designed to find concurrency and other issues in Java code, is also integrated (4). CodeGuru Reviewer rules are integral to the service and designed to use machine learning and automated reasoning to analyse code for code quality and security issues. CodeGuru Reviewer has a straightforward pricing structure that incorporates all studies carried out and examines your code using rules from all sources (as appropriate to the programming language).

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Ques. 3): What kinds of problems does the Amazon CodeGuru Reviewer Security Detector pick up?

Answer:

You can get assistance with five different types of code security issues from CodeGuru Reviewer: (1) When accessing the APIs of various AWS services, such as AWS EC2 and KMS, you can follow the security best practises by consulting the AWS API Security Best Practices. (2) You may verify popular Java cryptography libraries like Javax using the Java and Python Crypto Library Best Practices. Python's Crypto.Cipher and any other built-in or external hashing or crypto modules (such as cryptography) should be initialised and invoked properly. (3) Cross-site scripting, LDAP injection, and route traversal injection are a few security concerns specific to online apps that Secure Web Applications can help you check for. (4) Internal security knowledge is brought through AWS Security Best Practices, such as AWS Crypto recommendations, to your use cases.

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Ques. 4): How can my CI/CD workflow be improved by using CodeGuru?

Answer:

You can quickly automate the code review process for both code quality and security recommendations by incorporating CodeGuru Reviewer into your CI/CD workflow. You can set it up to run during planned pipeline runs, pull/merge requests, or push. The integration makes sure you never miss a suggestion and are always keeping an eye on the quality of your code and any potential security flaws.

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Ques. 5): I've already included a lot of logging into my code. Do I need to profile anymore?

Answer:

Because logging can only monitor latency (not CPU utilisation) and is labor-intensive to implement because developers must log every function in an application (without affecting application performance), it is only useful for a small number of scenarios, leaving developers without the resources they need to effectively monitor and troubleshoot applications in production. Here's where profiling is useful: Amazon Regardless of scenarios, CodeGuru Profiler is made to gather information on everything that transpired during that application's activity. Code patterns in your live application that affect performance are automatically found by CodeGuru Profiler using a knowledge base of frequently encountered performance inefficiencies.

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Ques. 6): What is the operation of Amazon CodeGuru Profiler?

Answer:

An agent, the profiler service, and intelligent recommendations make up Amazon CodeGuru Profiler. The agent operates as an in-process thread as a component of your application and is launched along with it via the command line. Every five minutes, it sends data from each of your agent-running service instances to the profiler service, which aggregates it. The profile data is then made available by Amazon CodeGuru Profiler in the form of interactive flame graphs, allowing you to see how well your application is doing. Additionally, Amazon CodeGuru Profiler regularly analyses the profiled data, compares it to Amazon and performance engineering best practises, and proactively notifies you of performance issues with wise advice. It also uses machine learning to continuously analyze application runtime data and alerts when it detects anomalies in the application profile and points to the anomalous lines of code.

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Ques. 7): What can I do with information from a heap summary?

Answer:

Two situations make advantage of heap summarization. You can first look for potential memory leaks. A leak that could result in out-of-memory issues and programme crashes may be indicated by a steadily rising memory use curve for one or more object types. When you want to reduce the memory footprint of your programme, that is the second situation. You can choose where to focus in this situation by looking at the breakdown of memory usage by object type. For instance, you can concentrate your research and optimization efforts on the areas of your application that are in charge of allocating and referring objects of a given kind if you are aware that an unexpectedly high amount of memory has been associated with a particular object type.

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Ques. 8): What is Amazon CodeGuru capable of?

Answer:

Both Amazon CodeGuru Reviewer and Amazon CodeGuru Profiler are parts of Amazon CodeGuru. To enhance the quality of the code, Amazon CodeGuru Reviewer employs machine learning and automated reasoning to spot key problems and obscure defects throughout application development. With its ability to pinpoint the most expensive lines of code and optimise performance for applications operating in production, Amazon CodeGuru Profiler considerably lowers operational costs.

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Ques. 9): Amazon CodeGuru Reviewer: What is it?

Answer:

An automatic code review service called Amazon CodeGuru Reviewer finds serious errors and breaks from industry standards in Java and Python programming. It reads the lines of code contained in a pull request and offers insightful recommendations based on best practises discovered from both the Amazon codebase and significant open source projects. Amazon CodeGuru Reviewer offers actionable suggestions for enhancing code quality and effortlessly interacts with current code review workflows on popular source control platforms like GitHub, GitHub Enterprise, Bitbucket, and AWS CodeCommit.

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Ques. 10): The operation of the Amazon CodeGuru Reviewer Security Detector.

Answer:

The CodeGuru Reviewer Security Detector analyses code statically to provide a control-flow diagram of every potential execution scenario. Once data flow across the control-flow graph has been detected, possible problems requiring long sequences of operations involving several methods and classes may be found. For instance, even though initialization and use take place in different ways, the security detector can tell whether Javax.Crypto.KeyGenerator or Python's crypto.secrets, both of which are symmetric secret key generators, are initialised before use.

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Ques. 11): What is a profiling group?

Answer:

A logical grouping you make up is a profiling group. It serves as the limit of a single application. For instance, a profiling group in a microservices architecture would combine the profiles of the microservices you have given to it and create a single profile for all of them.

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Ques. 12): What effects does CodeGuru Profiler have on the efficiency of programmes running on AWS Lambda?

Answer:

The agent for Amazon CodeGuru Profiler makes use of the CPU and memory resources allotted to AWS Lambda operations. It is optimised to have a negligible effect on the performance of your programme while it is running as an in-process thread. Consider raising the resources if your application uses the majority of them in an AWS Lambda function so that the agent can operate as intended.

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Ques. 13): What details about memory profiling can Amazon CodeGuru Profiler offer?

Answer:

In CodeGuru Profiler, heap summary data is provided. Heap summaries provide a consolidated view of memory usage per object type (such as String, int, char[]), and custom types, over a specified period of time (usually 5 minutes). CodeGuru keeps track of both the total number of objects and their sizes. The timeline graph used to display these metrics makes it simple to see memory use trends and peaks by object type.

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Ques. 14): What kinds of problems does the Amazon CodeGuru Reviewer find?

Answer:

The Amazon CodeGuru Reviewer scans concurrent code for concurrency problems, potential race circumstances, malicious or unsanitized inputs, improper handling of sensitive data like credentials, resource leaks, and race conditions and deadlocks. Additionally, it recommends best practises for using AWS, Java, and Python and finds duplicate code that might be unified for improved code maintainability.

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Ques. 15): What distinguishes the Amazon CodeGuru Profiler from standard APMs and standalone profilers?

Answer:

Traditional APMs offer helpful information on application performance, monitoring, and tracing. These APM capabilities are complemented by Amazon CodeGuru Profiler, which offers visualisations of the application's runtime data in addition to suggestions for fixing any performance issues it finds. Additionally, it makes use of machine learning to find and notify you to anomalies in your application profile and identify the offending lines of code. Without having a strong expertise in performance engineering, you may use Amazon CodeGuru Profiler to quickly identify the areas of code that provide the most potential for speed optimization and cost reductions and obtain advice on how to address them. AWS Lambda, EC2 instances, containers, and on-premises serverless computing platforms are all profiled by Amazon CodeGuru Profiler. Additionally, while Amazon CodeGuru Profiler was created to run continuously in production, under production traffic levels, and without affecting the application, other standalone profilers are only intended to run in test environments. This is helpful when resolving operational problems in the field, especially those that arise while using bare metal hosts.

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Ques. 16): Can I use the same application to obtain CPU and memory information?

Answer:

Yes, CodeGuru Profiler will begin collecting CPU and memory information for your programme as soon as you enable memory profiling and begin profiling. For a certain application, all the CPU and memory data can be obtained from a single profiling group.

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Ques. 17): Is my code accessible to Amazon CodeGuru Reviewer?

Answer:

To produce recommendations, Amazon CodeGuru Reviewer requires read-only access to your code. Our top priorities are your trust, privacy, and the security of your content. To prevent unauthorised access to or disclosure of your content and to make sure that our use conforms with our promises to you, we put in place the necessary safeguards, including encryption in transit.

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Ques. 18): Which application categories may I profile?

Answer:

Apps hosted on Amazon EC2, containerized applications running on Amazon ECS and Amazon EKS, as well as serverless applications running on AWS Fargate and AWS Lambda, are all compatible with Amazon CodeGuru Profiler. Additionally, you can use Amazon CodeGuru Profiler locally.

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Ques. 19): How is the Amazon CodeGuru Reviewer taught to provide wise suggestions?

Answer:

Rule mining and supervised machine learning models that combine logistic regression and neural networks are used to train the Amazon CodeGuru Reviewer.

For instance, the Amazon CodeGuru Reviewer scrapes Amazon code bases using search techniques and location-sensitive models to look for pull requests that contain AWS API calls when training for divergence from AWS best practises. It examines code modifications meant to raise the level of the code and compares them to information in the documentation. As a result, when Reviewer examines your code, it generates a new set of guidelines that it suggests to you as best practises.

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Ques. 20): Why should I utilise the security detector for Amazon CodeGuru Reviewers?

Answer:

It's like having a security expert evaluate your code 24/7 using CodeGuru Reviewer Security Detector. Before deployment, it aids in identifying security best practises.

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Top 20 AWS MemoryDB Questions and Answers

Redis-compatible, dependable, in-memory database service with lightning-fast performance is Amazon MemoryDB for Redis. For contemporary applications, such as those created with microservices architectures, MemoryDB enables you to achieve microsecond read latency, single-digit millisecond write latency, high throughput, and Multi-AZ durability. Redis' adaptable data structures and APIs are used in these applications' low latency and high scalability requirements as well as to facilitate rapid development. MemoryDB uses a distributed transactional log to provide in-memory speed as well as data durability, consistency, and recoverability by storing your complete dataset in memory. You may create high-performance applications without having to independently maintain a cache, durable database, or the necessary supporting infrastructure by using MemoryDB as a fully managed primary database.

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Ques. 1): Describe Redis.

Answer:

Strings, lists, sets, hashes, sorted sets, and other adaptable data structures are used by Redis, an open-source, in-memory key-value data store. Additionally, Redis has functionality for geospatial queries, pub/sub, custom Lua scripts, and other things. Redis provides lightning-fast performance and great throughput since it saves data in-memory. For the fifth year in a row, Redis is the "Most Loved Database" among Stack Overflow developers. It is also the top database chosen by the Cloud Native Computing Foundation (CNCF) End User Community. In order to promote and support cloud native computing, including containers and microservices, CNCF was established.

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Ques. 2): What is a MemoryDB cluster?

Answer:

A grouping of one or more nodes supporting a single dataset is known as a MemoryDB cluster. Each of the shards that make up a MemoryDB dataset has a primary node and a maximum of five optional replica nodes. A replica only handles read requests, whereas a primary node handles both read and write requests. When a primary node fails over to a replica node, the replica is elevated to the position of new primary node for that shard.

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Ques. 3): Does MemoryDB support high availability?

Answer:

MemoryDB does indeed provide high availability. A MemoryDB cluster with Multi-AZ availability can have up to 5 replicas spread across various AZs. MemoryDB will automatically failover and promote one of the replicas to act as the new primary and direct write traffic to it in the event of a primary node loss. MemoryDB also makes use of a distributed transactional log to guarantee that, even in the event of a primary node failure, the data on replicas is kept current. For unplanned failures, failover often occurs in less than 10 seconds, while for planned outages, it typically occurs in less than 200 milliseconds.

To reliably save data written to your database during database recovery, restart, failover, and eventual consistency between primaries and replicas, MemoryDB uses a distributed transactional log.

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Ques. 4): Can my MemoryDB cluster be backed up?

Answer:

Yes, you do make snapshots to backup your MemoryDB cluster's data and metadata. You have two options for creating snapshots: manually or automatically, using MemoryDB's automated snapshot scheduler once every day at a time you choose. You have the option to keep a snapshot in MemoryDB for up to 35 days after it is made. Amazon S3, which is built for 99.999999999% (11 9's) durability, is where snapshots are saved. Additionally, you have the option to delete your cluster after taking one final snapshot of it. Additionally, the service allows you to export MemoryDB snapshots to an Amazon S3 bucket.

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Ques. 5): Why is Amazon MemoryDB for Redis recommended to use data tiering?

Answer:

Use data tiering when you need a quicker, less expensive way to expand the data capacity for your MemoryDB clusters without sacrificing the availability of your applications. The workloads and programmes that can tolerate a bit extra latency the first time they utilise a less-frequently accessed item are the ones that benefit from data tiering the most. R6gd nodes, which have around a 5x larger total capacity (memory + SSD) while operating at peak efficiency, can cut storage expenses by more than 60% when compared to R6g nodes (memory only).

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Ques. 6): How can my MemoryDB cluster be updated?

Answer:

Your cluster's upgrades and maintenance are made simple by MemoryDB, which also offers two distinct processes for cluster upkeep. First, during the maintenance times that you set, MemoryDB automatically patches your cluster with some required upgrades. Second, MemoryDB uses service updates for some changes, which you can deploy immediately or plan for a later maintenance window. After a specific date, some service updates are automatically scheduled during a maintenance window. Your clusters' security, dependability, and operational performance are strengthened by cluster upgrades, and your cluster continues to be online and respond to read and write requests.

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Ques. 7): How is the consistency of MemoryDB different from that of free software Redis?

Answer:

On the primary node of each shard, open source Redis permits writes and strongly consistent reads, and eventually consistent readings from read replicas. If a primary node fails, these consistency qualities are not guaranteed because writes can get lost during a failover and hence go against the consistency paradigm.

MemoryDB's consistency model is comparable to Redis, an open source database. Clients can access their writes from primaries regardless of node failures with MemoryDB since data is not lost during failovers. In the multi-AZ transaction log, only information that has successfully been persisted is accessible. Lag metrics are still published to Amazon CloudWatch, and replica nodes are still eventually consistent.

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Ques. 8): How much throughput and latency can I get with MemoryDB?

Answer:

Depending on the node type, payload size, and number of client connections, MemoryDB's throughput and latency varies. On the principal node for a cluster shard, MemoryDB offers microsecond read latency, single-digit millisecond write latency, and read-after-write delay. Up to 1.3 GB/s read and 100 MB/s write throughput and 390K read and 100K write requests per second are supported by MemoryDB (based on internal testing on read-only and write-only workloads). A MemoryDB cluster divides data among one or more nodes, allowing you to expand your cluster by adding more replicas or shards to boost aggregate throughput.

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Ques. 9): When should I use MemoryDB for Redis instead of Amazon ElastiCache?

Answer:

For workloads that demand an incredibly quick, Redis-compatible primary database, MemoryDB for Redis provides a reliable, in-memory database. If your workload necessitates a robust database with lightning-fast speed, you should think about adopting MemoryDB (microsecond read and single-digit millisecond write latency). If you wish to create a Redis-based application with a primary, long-lasting database, MemoryDB might also be a good fit for your use case. Finally, you should think about adopting MemoryDB to reduce costs and simplify your application architecture by using a cache for durability and performance instead of a database.

Data from other databases and data stores that use Redis are frequently cached using the service ElastiCache for Redis. For caching workloads where you wish to speed up data access with your current primary database or data storage, you should think about ElastiCache for Redis (microsecond read and write performance). For use scenarios when you want to access data stored in a primary database or data store using the Redis data structures and APIs, you should also take ElastiCache for Redis into consideration.

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Ques. 10): How do I get started with using MemoryDB?

Answer:

Using the AWS Management Console, Command Line Interface (CLI), or Software Development Kit, you can begin by building a new MemoryDB cluster (SDK). Sign in and go to Amazon MemoryDB for Redis in the console to create a MemoryDB cluster. Choose "Get Started" and then "Create new cluster" from there.

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Ques. 11): Can I resize my MemoryDB cluster?

Answer:

Yes, you can change the horizontal and vertical sizes of your MemoryDB cluster. You can add or remove nodes to horizontally scale your cluster. Your dataset can be split among multiple shards by adding shards, and you can add more replica nodes to each shard to improve read throughput and availability. If you want to scale up your cluster, you may also delete replicas and shards. Changing the type of node you use will alter the memory and CPU resources available to each node, allowing you to expand your cluster vertically as well. Your cluster keeps running and continues to service read and write requests even when performing horizontal and vertical resizing operations.

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Ques. 12): How can I set up my MemoryDB cluster for authentication and authorization?

Answer:

Redis Access Control Lists (ACLs) are used by MemoryDB to manage cluster authorisation and authentication. With the aid of ACLs, you can provide unique permissions for various users within the same cluster. ACLs are groups of one or more users. For the purpose of authorising access to Redis commands and data, each user has a password and an access string.

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Ques. 13): How does Amazon MemoryDB for Redis' data tiering function?

Answer:

When available memory is used up, data tiering uses SSD storage in cluster nodes to continue processing data. Data tiering is automatically activated when employing cluster nodes with SSD storage, and MemoryDB controls data placement by transparently transferring things between memory and disc using a least-recently-used (LRU) strategy. MemoryDB automatically recognises the least-recently used items when memory is completely utilised and copies their values to disc to reduce cost. MemoryDB seamlessly copies an item's value to memory before completing a request when an application has to obtain it from the disc, doing so with no performance hit.

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Ques. 14): Can I migrate data from ElastiCache for Redis to my MemoryDB cluster?

Answer:

Yes, you can move data from MemoryDB to ElastiCache for Redis. Create an export to your S3 bucket of an image of your ElastiCache for Redis cluster first. Next, establish a fresh MemoryDB cluster and choose the backup to use for a restore. With the information from the snapshot and the Redis metadata, MemoryDB will establish a new cluster. See the MemoryDB documentation for more details on moving data from ElastiCache for Redis to MemoryDB.

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Ques. 15): How is the append-only file (AOF) feature of open source Redis different from MemoryDB's durability functionality?

Answer:

To store data durably, MemoryDB uses a distributed transactional log. MemoryDB offers quick database recovery and restart thanks to the distribution of data across several AZs. Additionally, MemoryDB provides consistent reads on primary nodes and eventual consistency for replica nodes.

An optional append-only file (AOF) feature in open source Redis allows for the long-term storage of data in a file on a primary node's hard drive. However, there is a chance of data loss because AOF saves data locally on primary nodes in a single availability zone. Additionally, there is a chance that replica consistency will be a problem in the event of a node loss.

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Ques. 16): Is Redis compatible with MemoryDB?

Answer:

Yes, MemoryDB supports the same set of Redis data types, parameters, and commands that you are accustomed to. MemoryDB also retains compatibility with open source Redis. This implies that you can utilise MemoryDB with the same application code, clients, and tools that you already use with Redis. All Redis data types, including strings, lists, sets, hashes, sorted sets, hyperloglogs, bitmaps, and streams, are supported by MemoryDB. Additionally, because MemoryDB administers your cluster for you, it supports the 200+ Redis commands with the exception of Redis admin commands.

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Ques. 17): My data is it encrypted by MemoryDB?

Answer:

Yes, MemoryDB supports both at-rest and in-transit data encryption. You can use a MemoryDB supplied key or customer managed keys (CMK) from AWS Key Management Service for encryption at rest. Your data is encrypted in memory using always-on 256-bit DRAM encryption when using Graviton2 instances for your MemoryDB cluster.

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Ques. 18): What does Amazon MemoryDB for Redis' data tiering mean?

Answer:

A new price-performance feature for MemoryDB called "Data Tiling for Amazon MemoryDB for Redis" automatically transfers less-frequently accessible data from memory to high-performance, locally attached solid-state drives (SSD). Data tiering boosts MemoryDB's capacity, streamlines cluster administration, and lowers total cost of ownership (TCO).

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Ques. 19): How is my data securely stored via MemoryDB?

Answer:

MemoryDB uses a distributed Multi-AZ transactional log to ensure data durability, consistency, and recoverability while storing your complete data set in memory. MemoryDB offers quick database recovery and restart thanks to the distribution of data across several AZs. MemoryDB can provide lightning-fast performance and excellent throughput by additionally keeping the data in memory.

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Ques. 20): What is the maximum size of a cluster that MemoryDB can support?

Answer:

A MemoryDB cluster of up to 500 nodes is created by you. With 250 primary nodes and one replica for high availability, this results in a maximum memory storage capacity of about 100 TB.

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