A company needs to extract the names of ingredients from recipe records that are stored as text files in an Amazon S3 bucket A web application will use the ingredient names to query an Amazon DynamoDB table and determine a nutrition score. The application can handle non-food records and errors The company does not have any employees who have machine learning knowledge to develop this solution Which solution will meet these requirements MOST cost-effectively?
A. Use S3 Event Notifications to invoke an AWS Lambda function when PutObjectrequests occur Program the Lambda function to analyze the object and extract theingredient names by using Amazon Comprehend Store the Amazon Comprehend output inthe DynamoDB table. B. Use an Amazon EventBridge rule to invoke an AWS Lambda function when PutObjectrequests occur. Program the Lambda function to analyze the object by using AmazonForecast to extract the ingredient names Store the Forecast output in the DynamoDB table. C. Use S3 Event Notifications to invoke an AWS Lambda function when PutObjectrequests occur Use Amazon Polly to create audio recordings of the recipe records. Savethe audio files in the S3 bucket Use Amazon Simple Notification Service (Amazon SNS) tosend a URL as a message to employees Instruct the employees to listen to the audio filesand calculate the nutrition score Store the ingredient names in the DynamoDB table. D. Use an Amazon EventBridge rule to invoke an AWS Lambda function when a PutObjectrequest occurs Program the Lambda function to analyze the object and extract theingredient names by using Amazon SageMaker Store the inference output from theSageMaker endpoint in the DynamoDB table.
Answer: A Explanation: This solution meets the following requirements: It is cost-effective, as it only uses serverless components that are charged basedon usage and do not require any upfront provisioning or maintenance.It is scalable, as it can handle any number of recipe records that are uploaded tothe S3 bucket without any performance degradation or manual intervention.It is easy to implement, as it does not require any machine learning knowledge orcomplex data processing logic. Amazon Comprehend is a natural languageprocessing service that can automatically extract entities such as ingredients fromtext files. The Lambda function can simply invoke the Comprehend API and storethe results in the DynamoDB table.It is reliable, as it can handle non-food records and errors gracefully. AmazonComprehend can detect the language and domain of the text files and return anappropriate response. The Lambda function can also implement error handlingand logging mechanisms to ensure the data quality and integrity.References:Using AWS Lambda with Amazon S3 - AWS LambdaWhat Is Amazon Comprehend? - Amazon ComprehendWorking with Tables - Amazon DynamoDB
Question # 342
A company has a new mobile app. Anywhere in the world, users can see local news on topics they choose. Users also can post photos and videos from inside the app. Users access content often in the first minutes after the content is posted. New content quickly replaces older content, and then the older content disappears. The local nature of the news means that users consume 90% of the content within the AWS Region where it is uploaded. Which solution will optimize the user experience by providing the LOWEST latency for content uploads?
A. Upload and store content in Amazon S3. Use Amazon CloudFront for the uploads. B. Upload and store content in Amazon S3. Use S3 Transfer Acceleration for the uploads. C. Upload content to Amazon EC2 instances in the Region that is closest to the user. Copythe data to Amazon S3. D. Upload and store content in Amazon S3 in the Region that is closest to the user. Usemultiple distributions of Amazon CloudFront.
Answer: B Explanation: The most suitable solution for optimizing the user experience by providing the lowest latency for content uploads is to upload and store content in Amazon S3 anduse S3 Transfer Acceleration for the uploads. This solution will enable the company toleverage the AWS global network and edge locations to speed up the data transferbetween the users and the S3 buckets.Amazon S3 is a storage service that provides scalable, durable, and highly available objectstorage for any type of data. Amazon S3 allows users to store and retrieve data fromanywhere on the web, and offers various features such as encryption, versioning, lifecyclemanagement, and replication1.S3 Transfer Acceleration is a feature of Amazon S3 that helps users transfer data to andfrom S3 buckets more quickly. S3 Transfer Acceleration works by using optimized networkpaths and Amazon’s backbone network to accelerate data transfer speeds. Users canenable S3 Transfer Acceleration for their buckets and use a distinct URL to access them,such as .s3-accelerate.amazonaws.com2.The other options are not correct because they either do not provide the lowest latency orare not suitable for the use case. Uploading and storing content in Amazon S3 and using Amazon CloudFront for the uploads is not correct because this solution is not designed foroptimizing uploads, but rather for optimizing downloads. Amazon CloudFront is a contentdelivery network (CDN) that helps users distribute their content globally with low latencyand high transfer speeds. CloudFront works by caching the content at edge locationsaround the world, so that users can access it quickly and easily from anywhere3. Uploadingcontent to Amazon EC2 instances in the Region that is closest to the user and copying thedata to Amazon S3 is not correct because this solution adds unnecessary complexity andcost to the process. Amazon EC2 is a computing service that provides scalable and securevirtual servers in the cloud. Users can launch, stop, or terminate EC2 instances as needed,and choose from various instance types, operating systems, and configurations4.Uploading and storing content in Amazon S3 in the Region that is closest to the user andusing multiple distributions of Amazon CloudFront is not correct because this solution is notcost-effective or efficient for the use case. As mentioned above, Amazon CloudFront is aCDN that helps users distribute their content globally with low latency and high transferspeeds. However, creating multiple CloudFront distributions for each Region would incuradditional charges and management overhead, and would not be necessary since 90% ofthe content is consumed within the same Region where it is uploaded3.References:What Is Amazon Simple Storage Service? - Amazon Simple Storage ServiceAmazon S3 Transfer Acceleration - Amazon Simple Storage ServiceWhat Is Amazon CloudFront? - Amazon CloudFrontWhat Is Amazon EC2? - Amazon Elastic Compute Cloud
Question # 343
An ecommerce application uses a PostgreSQL database that runs on an Amazon EC2 instance. During a monthly sales event, database usage increases and causes database connection issues for the application. The traffic is unpredictable for subsequent monthly sales events, which impacts the sales forecast. The company needs to maintain performance when there is an unpredictable increase in traffic. Which solution resolves this issue in the MOST cost-effective way?
A. Migrate the PostgreSQL database to Amazon Aurora Serverless v2. B. Enable auto scaling for the PostgreSQL database on the EC2 instance to accommodateincreased usage. C. Migrate the PostgreSQL database to Amazon RDS for PostgreSQL with a largerinstance type D. Migrate the PostgreSQL database to Amazon Redshift to accommodate increasedusage
Answer: A Explanation: Amazon Aurora Serverless v2 is a cost-effective solution that canautomatically scale the database capacity up and down based on the application’s needs. Itcan handle unpredictable traffic spikes without requiring any provisioning or managementof database instances. It is compatible with PostgreSQL and offers high performance,availability, and durability1. References: 1: AWS Ramp-Up Guide: Architect2, page 312:AWS Certified Solutions Architect - Associate exam guide3, page 9.
Question # 344
A company's marketing data is uploaded from multiple sources to an Amazon S3 bucket A series ot data preparation jobs aggregate the data for reporting The data preparation jobsneed to run at regular intervals in parallel A few jobs need to run in a specific order later The company wants to remove the operational overhead of job error handling retry logic, and state management Which solution will meet these requirements?
A. Use an AWS Lambda function to process the data as soon as the data is uploaded tothe S3 bucket Invoke Other Lambda functions at regularly scheduled intervals B. Use Amazon Athena to process the data Use Amazon EventBndge Scheduler to invokeAthena on a regular internal C. Use AWS Glue DataBrew to process the data Use an AWS Step Functions statemachine to run the DataBrew data preparation jobs D. Use AWS Data Pipeline to process the data. Schedule Data Pipeline to process the dataonce at midnight.
Answer: C Explanation: AWS Glue DataBrew is a visual data preparation tool that allows you toeasily clean, normalize, and transform your data without writing any code. You can createand run data preparation jobs on your data stored in Amazon S3, Amazon Redshift, orother data sources. AWS Step Functions is a service that lets you coordinate multiple AWSservices into serverless workflows. You can use Step Functions to orchestrate yourDataBrew jobs, define the order and parallelism of execution, handle errors and retries, andmonitor the state of your workflow. By using AWS Glue DataBrew and AWS StepFunctions, you can meet the requirements of the company with minimal operationaloverhead, as you do not need to write any code, manage any servers, or deal with complexdependencies.References:AWS Glue DataBrewAWS Step FunctionsOrchestrate AWS Glue DataBrew jobs using AWS Step Functions
Question # 345
A research company uses on-premises devices to generate data for analysis. The company wants to use the AWS Cloud to analyze the data. The devices generate .csv files and support writing the data to SMB file share. Company analysts must be able to use SQL commands to query the data. The analysts will run queries periodically throughout the day. Which combination of steps will meet these requirements MOST cost-effectively? (Select THREE.)
A. Deploy an AWS Storage Gateway on premises in Amazon S3 File Gateway mode. B. Deploy an AWS Storage Gateway on premises in Amazon FSx File Gateway mode. C. Set up an AWS Glue crawler to create a table based on the data that is in Amazon S3. D. Set up an Amazon EMR cluster with EMR Fife System (EMRFS) to query the data thatis in Amazon S3. Provide access to analysts. E. Set up an Amazon Redshift cluster to query the data that is in Amazon S3. Provideaccess to analysts. F. Set up Amazon Athena to query the data that is in Amazon S3. Provide access toanalysts.
Answer: A,C,F Explanation: To meet the requirements of the use case in a cost-effective way, thefollowing steps are recommended:Deploy an AWS Storage Gateway on premises in Amazon S3 File Gateway mode.This will allow the company to write the .csv files generated by the devices to anSMB file share, which will be stored as objects in Amazon S3 buckets. AWSStorage Gateway is a hybrid cloud storage service that integrates on-premisesenvironments with AWS storage. Amazon S3 File Gateway mode provides aseamless way to connect to Amazon S3 and access a virtually unlimited amount ofcloud storage1.Set up an AWS Glue crawler to create a table based on the data that is in AmazonS3. This will enable the company to use standard SQL to query the data stored inAmazon S3 buckets. AWS Glue is a serverless data integration service thatsimplifies data preparation and analysis. AWS Glue crawlers can automaticallydiscover and classify data from various sources, and create metadata tables in theAWS Glue Data Catalog2. The Data Catalog is a central repository that storesinformation about data sources and how to access them3.Set up Amazon Athena to query the data that is in Amazon S3. This will providethe company analysts with a serverless and interactive query service that cananalyze data directly in Amazon S3 using standard SQL. Amazon Athena isintegrated with the AWS Glue Data Catalog, so users can easily point Athena atthe data source tables defined by the crawlers. Amazon Athena charges only forthe queries that are run, and offers a pay-per-query pricing model, which makes ita cost-effective option for periodic queries4.The other options are not correct because they are either not cost-effective or not suitablefor the use case. Deploying an AWS Storage Gateway on premises in Amazon FSx FileGateway mode is not correct because this mode provides low-latency access to fullymanaged Windows file shares in AWS, which is not required for the use case. Setting upan Amazon EMR cluster with EMR File System (EMRFS) to query the data that is inAmazon S3 is not correct because this option involves setting up and managing a cluster ofEC2 instances, which adds complexity and cost to the solution. Setting up an AmazonRedshift cluster to query the data that is in Amazon S3 is not correct because this optionalso involves provisioning and managing a cluster of nodes, which adds overhead and costto the solution.References:What is AWS Storage Gateway?What is AWS Glue?AWS Glue Data CatalogWhat is Amazon Athena?
Question # 346
A company website hosted on Amazon EC2 instances processes classified data stored in The application writes data to Amazon Elastic Block Store (Amazon EBS) volumes The company needs to ensure that all data that is written to the EBS volumes is encrypted at rest. Which solution will meet this requirement?
A. Create an 1AM role that specifies EBS encryption Attach the role to the EC2 instances B. Create the EBS volumes as encrypted volumes Attach the EBS volumes to the EC2instances C. Create an EC2 instance tag that has a key of Encrypt and a value of True Tag allinstances that require encryption at the EBS level D. Create an AWS Key Management Service (AWS KMS) key policy that enforces EBSencryption in the account Ensure that the key policy is active
Answer: B Explanation: The simplest and most effective way to ensure that all data that is written tothe EBS volumes is encrypted at rest is to create the EBS volumes as encrypted volumes.You can do this by selecting the encryption option when you create a new EBS volume, orby copying an existing unencrypted volume to a new encrypted volume. You can alsospecify the AWS KMS key that you want to use for encryption, or use the default AWSmanagedkey. When you attach the encrypted EBS volumes to the EC2 instances, the datawill be automatically encrypted and decrypted by the EC2 host. This solution does notrequire any additional IAM roles, tags, or policies. References:Amazon EBS encryptionCreating an encrypted EBS volumeEncrypting an unencrypted EBS volume
Question # 347
A company has Amazon EC2 instances that run nightly batch jobs to process data. The EC2 instances run in an Auto Scaling group that uses On-Demand billing. If a job fails on one instance: another instance will reprocess the job. The batch jobs run between 12:00 AM and 06 00 AM local time every day. Which solution will provide EC2 instances to meet these requirements MOST cost-effectively'?
A. Purchase a 1-year Savings Plan for Amazon EC2 that covers the instance family of theAuto Scaling group that the batch job uses. B. Purchase a 1-year Reserved Instance for the specific instance type and operatingsystem of the instances in the Auto Scaling group that the batch job uses. C. Create a new launch template for the Auto Scaling group Set the instances to SpotInstances Set a policy to scale out based on CPU usage. D. Create a new launch template for the Auto Scaling group Increase the instance size Seta policy to scale out based on CPU usage.
Answer: C Explanation: This option is the most cost-effective solution because it leverages the Spot Instances, which are unused EC2 instances that are available at up to 90% discountcompared to On-Demand prices. Spot Instances can be interrupted by AWS when thedemand for On-Demand instances increases, but since the batch jobs are fault-tolerant andcan be reprocessed by another instance, this is not a major issue. By using a launchtemplate, the company can specify the configuration of the Spot Instances, such as theinstance type, the operating system, and the user data. By using an Auto Scaling group,the company can automatically scale the number of Spot Instances based on the CPUusage, which reflects the load of the batch jobs. This way, the company can optimize theperformance and the cost of the EC2 instances for the nightly batch jobs.A. Purchase a 1-year Savings Plan for Amazon EC2 that covers the instance family of theAuto Scaling group that the batch job uses. This option is not optimal because it requires acommitment to a consistent amount of compute usage per hour for a one-year term,regardless of the instance type, size, region, or operating system. This can limit the flexibility and scalability of the Auto Scaling group and result in overpaying for unusedcompute capacity. Moreover, Savings Plans do not provide a capacity reservation, whichmeans the company still needs to reserve capacity with On-Demand CapacityReservations and pay lower prices with Savings Plans.B. Purchase a 1-year Reserved Instance for the specific instance type and operatingsystem of the instances in the Auto Scaling group that the batch job uses. This option is notideal because it requires a commitment to a specific instance configuration for a one-yearterm, which can reduce the flexibility and scalability of the Auto Scaling group and result inoverpaying for unused compute capacity. Moreover, Reserved Instances do not provide acapacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Reserved Instances.D. Create a new launch template for the Auto Scaling group Increase the instance size Seta policy to scale out based on CPU usage. This option is not cost-effective because it doesnot take advantage of the lower prices of Spot Instances. Increasing the instance size canimprove the performance of the batch jobs, but it can also increase the cost of the On-Demand instances. Moreover, scaling out based on CPU usage can result in launchingmore instances than needed, which can also increase the cost of the system.References:1 Spot Instances - Amazon Elastic Compute Cloud2 Launch templates - Amazon Elastic Compute Cloud3 Auto Scaling groups - Amazon EC2 Auto Scaling[4] Savings Plans - Amazon EC2 Reserved Instances and Other AWS ReservationModels
Question # 348
A company hosts a three-tier web application in the AWS Cloud. A Multi-AZ Amazon RDS for MySQL server forms the database layer. Amazon ElastiCache forms the cache layer. The company wants a caching strategy that adds or updates data in the cache when a customer adds an item to the database. The data in the cache must always match the data in the database. Which solution will meet these requirements?
A. Implement the lazy loading caching strategy B. Implement the write-through caching strategy. C. Implement the adding TTL caching strategy. D. Implement the AWS AppConfig caching strategy.
Answer: B Explanation: A write-through caching strategy adds or updates data in the cachewhenever data is written to the database. This ensures that the data in the cache is alwaysconsistent with the data in the database. A write-through caching strategy also reduces thecache miss penalty, as data is always available in the cache when it is requested.However, a write-through caching strategy can increase the write latency, as data has to bewritten to both the cache and the database. A write-through caching strategy is suitable forapplications that require high data consistency and low read latency.A lazy loading caching strategy only loads data into the cache when it is requested, andupdates the cache when there is a cache miss. This can result in stale data in the cache,as data is not updated in the cache when it is changed in the database. A lazy loadingcaching strategy is suitable for applications that can tolerate some data inconsistency andhave a low cache miss rate.An adding TTL caching strategy assigns a time-to-live (TTL) value to each data item in the cache, and removes the data from the cache when the TTL expires. This can help preventstale data in the cache, as data is periodically refreshed from the database. However, anadding TTL caching strategy can also increase the cache miss rate, as data can be evictedfrom the cache before it is requested. An adding TTL caching strategy is suitable forapplications that have a high cache hit rate and can tolerate some data inconsistency.An AWS AppConfig caching strategy is not a valid option, as AWS AppConfig is a servicethat enables customers to quickly deploy validated configurations to applications of anysize and scale. AWS AppConfig does not provide a caching layer for web applications.References: Caching strategies - Amazon ElastiCache, Caching for high-volume workloadswith Amazon ElastiCache
Question # 349
A company wants to analyze and troubleshoot Access Denied errors and Unauthonzed errors that are related to 1AM permissions The company has AWS CloudTrail turned on Which solution will meet these requirements with the LEAST effort?
A. Use AWS Glue and write custom scripts to query CloudTrail logs for the errors B. Use AWS Batch and write custom scripts to query CloudTrail logs for the errors C. Search CloudTrail logs with Amazon Athena queries to identify the errors D. Search CloudTrail logs with Amazon QuickSight. Create a dashboard to identify the errors.
Answer: C Explanation: This solution meets the following requirements:It is the least effort, as it does not require any additional AWS services, customscripts, or data processing steps. Amazon Athena is a serverless interactive queryservice that allows you to analyze data in Amazon S3 using standard SQL. Youcan use Athena to query CloudTrail logs directly from the S3 bucket where theyare stored, without any data loading or transformation. You can also use the AWSManagement Console, the AWS CLI, or the Athena API to run and manage yourqueries.It is effective, as it allows you to filter, aggregate, and join CloudTrail log data usingSQL syntax. You can use various SQL functions and operators to specify thecriteria for identifying Access Denied and Unauthorized errors, such as the errorcode, the user identity, the event source, the event name, the event time, and theresource ARN. You can also use subqueries, views, and common tableexpressions to simplify and optimize your queries.It is flexible, as it allows you to customize and save your queries for future use.You can also export the query results to other formats, such as CSV or JSON, orintegrate them with other AWS services, such as Amazon QuickSight, for furtheranalysis and visualization.References:Querying AWS CloudTrail Logs - Amazon AthenaAnalyzing Data in S3 using Amazon Athena | AWS Big Data BlogTroubleshoot IAM permisson access denied or unauthorized errors | AWS re:Post
Question # 350
A global company runs its applications in multiple AWS accounts in AWS Organizations. The company's applications use multipart uploads to upload data to multiple Amazon S3 buckets across AWS Regions. The company wants to report on incomplete multipart uploads for cost compliance purposes. Which solution will meet these requirements with the LEAST operational overhead?
A. Configure AWS Config with a rule to report the incomplete multipart upload object count. B. Create a service control policy (SCP) to report the incomplete multipart upload objectcount. C. Configure S3 Storage Lens to report the incomplete multipart upload object count. D. Create an S3 Multi-Region Access Point to report the incomplete multipart upload objectcount.
Answer: C Explanation: S3 Storage Lens is a cloud storage analytics feature that provides organization-wide visibility into object storage usage and activity across multiple AWSaccounts in AWS Organizations. S3 Storage Lens can report the incomplete multipartupload object count as one of the metrics that it collects and displays on an interactivedashboard in the S3 console. S3 Storage Lens can also export metrics in CSV or Parquetformat to an S3 bucket for further analysis. This solution will meet the requirements with theleast operational overhead, as it does not require any code development or policy changes.References:1 explains how to use S3 Storage Lens to gain insights into S3 storage usage andactivity.2 describes the concept and benefits of multipart uploads.