Why Dumpsforsure is the best choice for Amazon Data-Engineer-Associate exam preparation?
Secure your position in Highly Competitive IT Industry:
Amazon Data-Engineer-Associate exam certification is the best way to demonstrate your understanding, capability and talent. DumpsforSure is here to provide you with best knowledge on Data-Engineer-Associate certification. By using our Data-Engineer-Associate questions & answers you can not only secure your current position but also expedite your growth process.
Verified by IT and Industry Experts:
We are devoted and dedicated to providing you with real and updated Data-Engineer-Associate exam dumps, along with explanations. Keeping in view the value of your money and time, all the questions and answers on Dumpsforsure has been verified by Amazon experts. They are highly qualified individuals having many years of professional experience.
Ultimate preparation Source:
Dumpsforsure is a central tool to help you prepare your Amazon Data-Engineer-Associate exam. We have collected real exam questions & answers which are updated and reviewed by professional experts regularly. In order to assist you understanding the logic and pass the Amazon exams, our experts added explanation to the questions.
Instant Access to the Real and Updated Amazon Data-Engineer-Associate Questions & Answers:
Dumpsforsure is committed to update the exam databases on regular basis to add the latest questions & answers. For your convenience we have added the date on the exam page showing the most latest update. Getting latest exam questions you'll be able to pass your Amazon Data-Engineer-Associate exam in first attempt easily.
Free Data-Engineer-Associate Dumps DEMO before Purchase:
Dumpsforsure is offering free Demo facility for our valued customers. You can view Dumpsforsure's content by downloading Data-Engineer-Associate free Demo before buying. It'll help you getting the pattern of the exam and form of Data-Engineer-Associate dumps questions and answers.
Three Months Free Updates:
Our professional expert's team is constantly checking for the updates. You are eligible to get 90 days free updates after purchasing Data-Engineer-Associate exam. If there will be any update found our team will notify you at earliest and provide you with the latest PDF file.
SAMPLE QUESTIONS
Question # 1
A data engineer needs Amazon Athena queries to finish faster. The data engineer noticesthat all the files the Athena queries use are currently stored in uncompressed .csv format.The data engineer also notices that users perform most queries by selecting a specificcolumn.Which solution will MOST speed up the Athena query performance?
A. Change the data format from .csvto JSON format. Apply Snappy compression. B. Compress the .csv files by using Snappy compression. C. Change the data format from .csvto Apache Parquet. Apply Snappy compression. D. Compress the .csv files by using gzjg compression.
Answer: C Explanation: Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats,such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equallyefficient for querying. Some data formats, such as CSV and JSON, are row-oriented,meaning that they store data as a sequence of records, each with the same fields. Roworientedformats are suitable for loading and exporting data, but they are not optimal foranalytical queries that often access only a subset of columns. Row-oriented formats alsodo not support compression or encoding techniques that can reduce the data size andimprove the query performance.On the other hand, some data formats, such as ORC and Parquet, are column-oriented,meaning that they store data as a collection of columns, each with a specific data type.Column-oriented formats are ideal for analytical queries that often filter, aggregate, or joindata by columns. Column-oriented formats also support compression and encodingtechniques that can reduce the data size and improve the query performance. Forexample, Parquet supports dictionary encoding, which replaces repeated values withnumeric codes, and run-length encoding, which replaces consecutive identical values witha single value and a count. Parquet also supports various compression algorithms, such asSnappy, GZIP, and ZSTD, that can further reduce the data size and improve the queryperformance. Therefore, changing the data format from CSV to Parquet and applying Snappycompression will most speed up the Athena query performance. Parquet is a columnorientedformat that allows Athena to scan only the relevant columns and skip the rest,reducing the amount of data read from S3. Snappy is a compression algorithm that reducesthe data size without compromising the query speed, as it is splittable and does not requiredecompression before reading. This solution will also reduce the cost of Athena queries, asAthena charges based on the amount of data scanned from S3.The other options are not as effective as changing the data format to Parquet and applyingSnappy compression. Changing the data format from CSV to JSON and applying Snappycompression will not improve the query performance significantly, as JSON is also a roworientedformat that does not support columnar access or encoding techniques.Compressing the CSV files by using Snappy compression will reduce the data size, but itwill not improve the query performance significantly, as CSV is still a row-oriented formatthat does not support columnar access or encoding techniques. Compressing the CSV filesby using gzjg compression will reduce the data size, but it willdegrade the queryperformance, as gzjg is not a splittable compression algorithm and requires decompressionbefore reading. References:Amazon AthenaChoosing the Right Data FormatAWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide,Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena
Question # 2
A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple usergroups need to access the raw data. The company must ensure that user groups canaccess only the PII that they require.Which solution will meet these requirements with the LEAST effort?
A. Use Amazon Athena to query the data. Set up AWS Lake Formation and create datafilters to establish levels of access for the company's IAM roles. Assign each user to theIAM role that matches the user's PII access requirements. B. Use Amazon QuickSight to access the data. Use column-level security features inQuickSight to limit the PII that users can retrieve from Amazon S3 by using AmazonAthena. Define QuickSight access levels based on the PII access requirements of theusers. C. Build a custom query builder UI that will run Athena queries in the background to accessthe data. Create user groups in Amazon Cognito. Assign access levels to the user groupsbased on the PII access requirements of the users. D. Create IAM roles that have different levels of granular access. Assign the IAM roles toIAM user groups. Use an identity-based policy to assign access levels to user groups at thecolumn level.
Answer: A Explanation: Amazon Athena is a serverless, interactive query service that enables you to analyze datain Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build,secure, and manage data lakes on AWS. You can use AWS Lake Formation to create datafilters that define the level of access for different IAM roles based on the columns, rows, ortags of the data. By using Amazon Athena to query the data and AWS Lake Formation tocreate data filters, the company can meet the requirements of ensuring that user groupscan access only the PII that they require with the least effort. The solution is to use AmazonAthena to query the data in the data lake that is in Amazon S3. Then, set up AWS LakeFormation and create data filters to establish levels of access for the company’s IAM roles.For example, a data filter can allow a user group to access only the columns that containthe PII that they need, such as name and email address, and deny access to the columnsthat contain the PII that they do not need, such as phone number and social securitynumber. Finally, assign each user to the IAM role that matches the user’s PII accessrequirements. This way, the user groups can access the data in the data lake securely andefficiently. The other options are either not feasible or not optimal. Using AmazonQuickSight to access the data (option B) would require the company to pay for theQuickSight service and to configure the column-level security features for each user.Building a custom query builder UI that will run Athena queries in the background to accessthe data (option C) would require the company to develop and maintain the UI and tointegrate it with Amazon Cognito. Creating IAM roles that have different levels of granularaccess (option D) would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema. References:Amazon AthenaAWS Lake FormationAWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide,Chapter 4: Data Analysis and Visualization, Section 4.3: Amazon Athena
Question # 3
A company receives call logs as Amazon S3 objects that contain sensitive customerinformation. The company must protect the S3 objects by using encryption. The companymust also use encryption keys that only specific employees can access.Which solution will meet these requirements with the LEAST effort?
A. Use an AWS CloudHSM cluster to store the encryption keys. Configure the process thatwrites to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects.Deploy an IAM policy that restricts access to the CloudHSM cluster. B. Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objectsthat contain customer information. Restrict access to the keys that encrypt the objects. C. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects thatcontain customer information. Configure an IAM policy that restricts access to the KMSkeys that encrypt the objects. D. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt theobjects that contain customer information. Configure an IAM policy that restricts access tothe Amazon S3 managed keys that encrypt the objects.
Answer: C Explanation: Option C is the best solution to meet the requirements with the least effortbecause server-side encryption with AWS KMS keys (SSE-KMS) is a feature that allowsyou to encrypt data at rest in Amazon S3 using keys managed by AWS Key ManagementService (AWS KMS). AWS KMS is a fully managed service that enables you to create andmanage encryption keys for your AWS services and applications. AWS KMS also allowsyou to define granular access policies for your keys, such as who can use them to encryptand decrypt data, and under what conditions. By using SSE-KMS, you canprotect your S3objects by using encryption keys that only specific employees can access, without having to manage the encryption and decryption process yourself.Option A is not a good solution because it involves using AWS CloudHSM, which is aservice that provides hardware security modules (HSMs) in the AWS Cloud. AWSCloudHSM allows you to generate and use your own encryption keys on dedicatedhardware that is compliant with various standards and regulations. However, AWSCloudHSM is not a fully managed service and requires more effort to set up and maintainthan AWS KMS. Moreover, AWS CloudHSM does not integrate with Amazon S3, so youhave to configure the process that writes to S3 to make calls to CloudHSM to encrypt anddecrypt the objects, which adds complexity and latency to the data protection process.Option B is not a good solution because it involves using server-side encryption withcustomer-provided keys (SSE-C), which is a feature that allows you to encrypt data at restin Amazon S3 using keys that you provide and manage yourself. SSE-C requires you tosend your encryption key along with each request to upload or retrieve an object. However,SSE-C does not provide any mechanism to restrict access to the keys that encrypt theobjects, so you have to implement your own key management and access control system,which adds more effort and risk to the data protection process.Option D is not a good solution because it involves using server-side encryption withAmazon S3 managed keys (SSE-S3), which is a feature that allows you to encrypt data atrest in Amazon S3 using keys that are managed by Amazon S3. SSE-S3 automaticallyencrypts and decrypts your objects as they are uploaded and downloaded from S3.However, SSE-S3 does not allow you to control who can access the encryption keys orunder what conditions. SSE-S3 uses a single encryption key for each S3 bucket, which isshared by all users who have access to the bucket. This means that you cannot restrictaccess to the keys that encrypt the objects by specific employees, which does not meet therequirements.References:AWS Certified Data Engineer - Associate DEA-C01 Complete Study GuideProtecting Data Using Server-Side Encryption with AWS KMS–ManagedEncryption Keys (SSE-KMS) - Amazon Simple Storage ServiceWhat is AWS Key Management Service? - AWS Key Management ServiceWhat is AWS CloudHSM? - AWS CloudHSMProtecting Data Using Server-Side Encryption with Customer-Provided EncryptionKeys (SSE-C) - Amazon Simple Storage ServiceProtecting Data Using Server-Side Encryption with Amazon S3-ManagedEncryption Keys (SSE-S3) - Amazon Simple Storage Service
Question # 4
A data engineer needs to maintain a central metadata repository that users access throughAmazon EMR and Amazon Athena queries. The repository needs to provide the schemaand properties of many tables. Some of the metadata is stored in Apache Hive. The dataengineer needs to import the metadata from Hive into the central metadata repository.Which solution will meet these requirements with the LEAST development effort?
A. Use Amazon EMR and Apache Ranger. B. Use a Hive metastore on an EMR cluster. C. Use the AWS Glue Data Catalog. D. Use a metastore on an Amazon RDS for MySQL DB instance.
Answer: C Explanation: The AWS Glue Data Catalog is an Apache Hive metastore-compatiblecatalog that provides a central metadata repository for various data sources and formats.You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMRand Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWSGlue crawlers to automatically discover and catalog the metadata from Hive, and use theAWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as theHive metastore. The other options are either more complex or require additional steps,such as setting up Apache Ranger for security, managing a Hive metastore on an EMRcluster or an RDS instance, or migrating the metadata manually. References:Using the AWS Glue Data Catalog as the metastore for Hive (Section: SpecifyingAWS Glue Data Catalog as the metastore)Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue DataCatalog)AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadatafrom an existing Hive metastore)AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide(Chapter 5, page 131)
Question # 5
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Sparkjobs to perform big data analysis. The company requires high reliability. A big data teammust follow best practices for running cost-optimized and long-running workloads onAmazon EMR. The team must find a solution that will maintain the company's current levelof performance.Which combination of resources will meet these requirements MOST cost-effectively?(Choose two.)
A. Use Hadoop Distributed File System (HDFS) as a persistent data store. B. Use Amazon S3 as a persistent data store. C. Use x86-based instances for core nodes and task nodes. D. Use Graviton instances for core nodes and task nodes. E. Use Spot Instances for all primary nodes.
Answer: B,D Explanation: The best combination of resources to meet the requirements of high reliability, cost-optimization, and performance for running Apache Spark jobs on AmazonEMR is to use Amazon S3 as a persistent data store and Graviton instances for core nodesand task nodes.Amazon S3 is a highly durable, scalable, and secure object storage service that can storeany amount of data for a variety of use cases, including big data analytics1. Amazon S3 isa better choice than HDFS as a persistent data store for Amazon EMR, as it decouples thestorage from the compute layer, allowing for more flexibility and cost-efficiency. Amazon S3also supports data encryption, versioning, lifecycle management, and cross-regionreplication1. Amazon EMR integrates seamlessly with Amazon S3, using EMR File System(EMRFS) to access data stored in Amazon S3 buckets2. EMRFS also supports consistentview, which enables Amazon EMR to provide read-after-write consistency for Amazon S3objects that are accessed through EMRFS2.Graviton instances are powered by Arm-based AWS Graviton2 processors that deliver upto 40% better price performance over comparable current generation x86-basedinstances3. Graviton instances are ideal for running workloads that are CPU-bound,memory-bound, or network-bound, such as big data analytics, web servers, and opensourcedatabases3. Graviton instances are compatible with Amazon EMR, and can beusedfor both core nodes and task nodes. Core nodes are responsible for running the data processing frameworks, such as Apache Spark, and storing data in HDFS or the local filesystem. Task nodes are optional nodes that can be added to a cluster to increase theprocessing power and throughput. By using Graviton instances for both core nodes andtask nodes, you can achieve higher performance and lower cost than using x86-basedinstances.Using Spot Instances for all primary nodes is not a good option, as it can compromise thereliability and availability of the cluster. Spot Instances are spare EC2 instances that areavailable at up to 90% discount compared to On-Demand prices, but they can beinterrupted by EC2 with a two-minute notice when EC2 needs the capacity back. Primarynodes are the nodes that run the cluster software, such as Hadoop, Spark, Hive, and Hue,and are essential for the cluster operation. If a primary node is interrupted by EC2, thecluster will fail or become unstable. Therefore, it is recommended to use On-DemandInstances or Reserved Instances for primary nodes, and use Spot Instances only for tasknodes that can tolerate interruptions. References:Amazon S3 - Cloud Object StorageEMR File System (EMRFS)AWS Graviton2 Processor-Powered Amazon EC2 Instances[Plan and Configure EC2 Instances][Amazon EC2 Spot Instances][Best Practices for Amazon EMR]
Question # 6
A company wants to implement real-time analytics capabilities. The company wants to useAmazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming dataat the rate of several gigabytes per second. The company wants to derive near real-timeinsights by using existing business intelligence (BI) and analytics tools.Which solution will meet these requirements with the LEAST operational overhead?
A. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command toload data from Amazon S3 directly into Amazon Redshift to make the data immediatelyavailable for real-time analysis. B. Access the data from Kinesis Data Streams by using SQL queries. Create materializedviews directly on top of the stream. Refresh the materialized views regularly to query themost recent stream data. C. Create an external schema in Amazon Redshift to map the data from Kinesis DataStreams to an Amazon Redshift object. Create a materialized view to read data from thestream. Set the materialized view to auto refresh. D. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis DataFirehose to stage the data in Amazon S3. Use the COPY command to load the data fromAmazon S3 to a table in Amazon Redshift.
Answer: C Explanation: This solution meets the requirements of implementing real-time analytics capabilities with the least operational overhead. By creating an external schema in AmazonRedshift, you can access the data from Kinesis Data Streams using SQL queries withouthaving to load the data into the cluster. By creating a materialized view on top of thestream, you can store the results of the query in the cluster and make them available foranalysis. By setting the materialized view to auto refresh, you can ensure that the view isupdated with the latest data from the stream at regular intervals. This way, you can derivenear real-time insights by using existing BI and analytics tools. References:Amazon Redshift streaming ingestionCreating an external schema for Amazon Kinesis Data StreamsCreating a materialized view for Amazon Kinesis Data Streams
Question # 7
A company stores details about transactions in an Amazon S3 bucket. The company wantsto log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.Which solution will meet this requirement with the LEAST operational effort?
A. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket toinvoke an AWS Lambda function. Program the Lambda function to write the event toAmazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to thelogs S3 bucket. B. Create a trail of management events in AWS CloudTraiL. Configure the trail to receivedata from the transactions S3 bucket. Specify an empty prefix and write-only events.Specify the logs S3 bucket as the destination bucket. C. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket toinvoke an AWS Lambda function. Program the Lambda function to write the events to thelogs S3 bucket. D. Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data fromthe transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logsS3 bucket as the destination bucket.
Answer: D Explanation: This solution meets the requirement of logging all writes to the S3 bucket into another S3 bucket with the least operational effort. AWS CloudTrail is a service thatrecords the API calls made to AWS services, including Amazon S3. By creating a trail ofdata events, you can capture the details of the requests that are made to the transactionsS3 bucket, such as the requester, the time, the IP address, and the response elements. Byspecifying an empty prefix and write-only events, you can filter the data events to onlyinclude the ones that write to the bucket. By specifying the logs S3 bucket as thedestination bucket, you can store the CloudTrail logs in another S3 bucket that is in thesame AWS Region. This solution does not require any additional coding or configuration,and it is more scalable and reliable than using S3 Event Notifications and Lambdafunctions. References:Logging Amazon S3 API calls using AWS CloudTrailCreating a trail for data eventsEnabling Amazon S3 server access logging
Question # 8
A data engineer has a one-time task to read data from objects that are in Apache Parquetformat in an Amazon S3 bucket. The data engineer needs to query only one column of thedata.Which solution will meet these requirements with the LEAST operational overhead?
A. Confiqure an AWS Lambda function to load data from the S3 bucket into a pandasdataframe- Write a SQL SELECT statement on the dataframe to query the requiredcolumn. B. Use S3 Select to write a SQL SELECT statement to retrieve the required column fromthe S3 objects. C. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column. D. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in AmazonAthena to query the required column.
Answer: B Explanation: Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only asubset of data from an S3 object by using simple SQL expressions. S3 Select works onobjects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid theneed to download and process the entire S3 object, which reduces the amount of datatransferred and the computation time. S3 Select is also easy to use and does not requireany additional services or resources.Option A is not a good solution because it involves writing custom code and configuring anAWS Lambda function to load data from the S3 bucket into a pandas dataframe and querythe required column. This option adds complexity and latency to the data retrieval processand requires additional resources and configuration.Moreover, AWS Lambda haslimitations on the execution time, memory, and concurrency, which may affect theperformance and reliability of the data retrieval process.Option C is not a good solution because it involves creating and running an AWS GlueDataBrew project to consume the S3 objects and query the required column. AWS GlueDataBrew is a visual data preparation tool that allows you to clean, normalize, andtransform data without writing code. However, in this scenario, the data is already inParquet format, which is a columnar storage format that is optimized for analytics.Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover,AWS Glue DataBrew adds extra time and cost to the data retrieval process and requiresadditional resources and configuration.Option D is not a good solution because it involves running an AWS Glue crawler on the S3objects and using a SQL SELECT statement in Amazon Athena to query the requiredcolumn. An AWS Glue crawler is a service that can scan data sources and create metadatatables in the AWS Glue Data Catalog. The Data Catalog is a central repository that storesinformation about the data sources, such as schema, format, and location. Amazon Athenais a serverless interactive query service that allows you to analyze data in S3 usingstandard SQL. However, in this scenario, the schema and format of the data are alreadyknown and fixed, so there is no need to run a crawler to discover them. Moreover, runninga crawler and using Amazon Athena adds extra time and cost to the data retrieval processand requires additional services and configuration.References:AWS Certified Data Engineer - Associate DEA-C01 Complete Study GuideS3 Select and Glacier Select - Amazon Simple Storage ServiceAWS Lambda - FAQsWhat Is AWS Glue DataBrew? - AWS Glue DataBrewPopulating the AWS Glue Data Catalog - AWS Glue What is Amazon Athena? - Amazon Athena
Question # 9
A retail company has a customer data hub in an Amazon S3 bucket. Employees from manycountries use the data hub to support company-wide analytics. A governance team mustensure that the company's data analysts can access data only for customers who arewithin the same country as the analysts.Which solution will meet these requirements with the LEAST operational effort?
A. Create a separate table for each country's customer data. Provide access to eachanalyst based on the country that the analyst serves. B. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the LakeFormation row-level security features to enforce the company's access policies. C. Move the data to AWS Regions that are close to the countries where the customers are.Provide access to each analyst based on the country that the analyst serves. D. Load the data into Amazon Redshift. Create a view for each country. Create separate1AM roles for each country to provide access to data from each country. Assign theappropriate roles to the analysts.
Answer: B Explanation: AWS Lake Formation is a service that allows you to easily set up, secure, and manage data lakes. One of the features of Lake Formation is row-level security, whichenables you to control access to specific rows or columns of data based on the identity orrole of the user. This feature is useful for scenarios where you need to restrict access tosensitive or regulated data, such as customer data from different countries. By registeringthe S3 bucket as a data lake location in Lake Formation, you can use the Lake Formationconsole or APIs to define and apply row-level security policies to the data in the bucket.You can also use Lake Formation blueprints to automate the ingestion and transformationof data from various sources into the data lake. This solution requires the least operationaleffort compared to the other options, as it does not involve creating or moving data, ormanaging multiple tables, views, or roles. References:AWS Lake FormationRow-Level SecurityAWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide,Chapter 4: Data Lakes and Data Warehouses, Section 4.2: AWS Lake Formation
Question # 10
A company uses Amazon RDS to store transactional data. The company runs an RDS DBinstance in a private subnet. A developer wrote an AWS Lambda function with defaultsettings to insert, update, or delete data in the DB instance.The developer needs to give the Lambda function the ability to connect to the DB instanceprivately without using the public internet.Which combination of steps will meet this requirement with the LEAST operationaloverhead? (Choose two.)
A. Turn on the public access setting for the DB instance. B. Update the security group of the DB instance to allow only Lambda function invocationson the database port. C. Configure the Lambda function to run in the same subnet that the DB instance uses. D. Attach the same security group to the Lambda function and the DB instance. Include aself-referencing rule that allows access through the database port. E. Update the network ACL of the private subnet to include a self-referencing rule thatallows access through the database port.
Answer: C,D Explanation: To enable the Lambda function to connect to the RDS DB instance privately without using the public internet, the best combination of steps is to configure the Lambdafunction to run in the same subnet that the DB instance uses, and attach the same securitygroup to the Lambda function and the DB instance. This way, the Lambda function and theDB instance can communicate within the same private network, and the security group canallow traffic between them on the database port. This solution has the least operationaloverhead, as it does not require any changes to the public access setting, the networkACL, or the security group of the DB instance.The other options are not optimal for the following reasons:A. Turn on the public access setting for the DB instance. This option is notrecommended, as it would expose the DB instance to the public internet, whichcan compromise the security and privacy of the data. Moreover, this option wouldnot enable the Lambda function to connect to the DB instance privately, as it wouldstill require the Lambda function to use the public internet to access the DBinstance.B. Update the security group of the DB instance to allow only Lambda functioninvocations on the database port. This option is not sufficient, as it would onlymodify the inbound rules of the security group of the DB instance, but not theoutbound rules of the security group of the Lambda function. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as itwould still require the Lambda function to use the public internet to access the DBinstance.E. Update the network ACL of the private subnet to include a self-referencing rulethat allows access through the database port. This option is not necessary, as thenetwork ACL of the private subnet already allows all traffic within the subnet bydefault. Moreover, this option would not enable the Lambda function to connect tothe DB instance privately, as it would still require the Lambda function to use thepublic internet to access the DB instance.References:1: Connecting to an Amazon RDS DB instance2: Configuring a Lambda function to access resources in a VPC3: Working with security groups: Network ACLs
