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20 Questions

Data analytics Quiz

1 :-

A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient’s protected health information (PHI) from the streaming data and store the data in durable storage. Which solution meets these requirements with the least operational overhead?

Ingest The Data Using Amazon Kinesis Data Streams Which Invokes An Aws Lambda Function Using Kinesis Client Library (kcl) To Remove All Phi. Write The Data In Amazon S3. Ingest The Data Using Amazon Kinesis Data Firehose To Write The Data To Amazon S3. Have Amazon S3 Trigger An Aws Lambda Function That Parses The Sensor Data To Remove All Phi In Amazon S3. Ingest The Data Using Amazon Kinesis Data Streams To Write The Data To Amazon S3. Have The Data Stream Launch An Aws Lambda Function That Parses The Sensor Data And Removes All Phi In Amazon S3. Ingest The Data Using Amazon Kinesis Data Firehose To Write The Data To Amazon S3. Implement A Transformation Aws Lambda Function That Parses The Sensor Data To Remove All Phi.

2 :-

A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort. Which solution meets these requirements?

Use Amazon Kinesis Data Firehose To Store The Data In Amazon S3. Use Amazon Quicksight To Build The Dashboard. Use Apache Spark Streaming On Amazon Emr To Read The Data In Near-real Time. Develop A Custom Application For The Dashboard By Using D3.js. Use Amazon Kinesis Data Firehose To Push The Data Into An Amazon Opensearch Service (amazon Elasticsearch Service) Cluster. Visualize The Data By Using An Opensearch Dashboards (kibana). Use Aws Glue Streaming Etl To Store The Data In Amazon S3. Use Amazon Quicksight To Build The Dashboard.

3 :-

A financial services company needs to aggregate daily stock trade data from the exchanges into a data store. The company requires that data be streamed directly into the data store, but also occasionally allows data to be modified using SQL. The solution should integrate complex, analytic queries running with minimal latency. The solution must provide a business intelligence dashboard that enables viewing of the top contributors to anomalies in stock prices. Which solution meets the company’s requirements?

Use Amazon Kinesis Data Firehose To Stream Data To Amazon S3. Use Amazon Athena As A Data Source For Amazon Quicksight To Create A Business Intelligence Dashboard. Use Amazon Kinesis Data Streams To Stream Data To Amazon Redshift. Use Amazon Redshift As A Data Source For Amazon Quicksight To Create A Business Intelligence Dashboard. Use Amazon Kinesis Data Firehose To Stream Data To Amazon Redshift. Use Amazon Redshift As A Data Source For Amazon Quicksight To Create A Business Intelligence Dashboard. Use Amazon Kinesis Data Streams To Stream Data To Amazon S3. Use Amazon Athena As A Data Source For Amazon Quicksight To Create A Business Intelligence Dashboard.

4 :-

A company stores its sales and marketing data that includes personally identifiable information (PII) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run analytics reports with the data. To meet compliance requirements, the company must ensure the data is not publicly accessible throughout this process. A data engineer has secured Amazon S3 but must ensure the individual EMR clusters created by the analysts are not exposed to the public internet. Which solution should the data engineer to meet this compliance requirement with LEAST amount of effort?

Create An Emr Security Configuration And Ensure The Security Configuration Is Associated With The Emr Clusters When They Are Created. Check The Security Group Of The Emr Clusters Regularly To Ensure It Does Not Allow Inbound Traffic From Ipv4 0.0.0.0/0 Or Ipv6 ::/0. Enable The Block Public Access Setting For Amazon Emr At The Account Level Before Any Emr Cluster Is Created. Use Aws Waf To Block Public Internet Access To The Emr Clusters Across The Board

5 :-

An ecommerce company is migrating its business intelligence environment from on premises to the AWS Cloud. The company will use Amazon Redshift in a public subnet and Amazon QuickSight. The tables already are loaded into Amazon Redshift and can be accessed by a SQL tooThe company starts QuickSight for the first time. During the creation of the data source, a data analytics specialist enters all the information and tries to validate the connection. An error with the following message occurs: “Creating a connection to your data source timed out." How should the data analytics specialist resolve this error?

Grant The Select Permission On Amazon Redshift Tables. Add The Quicksight Ip Address Range Into The Amazon Redshift Security Group. Create An Iam Role For Quicksight To Access Amazon Redshift Use A Quicksight Admin User For Creating The Dataset.

6 :-

A company wants to optimize the cost of its data and analytics platform. The company is ingesting a number of .csv and JSON files in Amazon S3 from various data sources. Incoming data is expected to be 50 GB each day. The company is using Amazon Athena to query the raw data in Amazon S3 directly. Most queries aggregate data from the past 12 months, and data that is older than 5 years is infrequently queried. The typical query scans about 500 MB of data and is expected to return results in less than 1 minute. The raw data must be retained indefinitely for compliance requirements. Which solution meets the company’s requirements?

Use An Aws Glue Etl Job To Compress Partition And Convert The Data Into A Columnar Data Format. Use Athena To Query The Processed Dataset. Configure A Lifecycle Policy To Move The Processed Data Into The Amazon S3 Standard-infrequent Access (s3 Standard-ia) Storage Class 5 Years After Object Creation. Configure A Second Lifecycle Policy To Move The Raw Data Into Amazon S3 Glacier For Long-term Archival 7 Days After Object Creation. Use An Aws Glue Etl Job To Partition And Convert The Data Into A Row-based Data Format. Use Athena To Query The Processed Dataset. Configure A Lifecycle Policy To Move The Data Into The Amazon S3 Standard-infrequent Access (s3 Standard-ia) Storage Class 5 Years After Object Creation. Configure A Second Lifecycle Policy To Move The Raw Data Into Amazon S3 Glacier For Long-term Archival 7 Days After Object Creation. Use An Aws Glue Etl Job To Compress Partition And Convert The Data Into A Columnar Data Format. Use Athena To Query The Processed Dataset. Configure A Lifecycle Policy To Move The Processed Data Into The Amazon S3 Standard-infrequent Access (s3 Standard-ia) Storage Class 5 Years After The Object Was Last Accessed. Configure A Second Lifecycle Policy To Move The Raw Data Into Amazon S3 Glacier For Long-term Archival 7 Days After The Last Date The Object Was Accessed. Use An Aws Glue Etl Job To Partition And Convert The Data Into A Row-based Data Format. Use Athena To Query The Processed Dataset. Configure A Lifecycle Policy To Move The Data Into The Amazon S3 Standard-infrequent Access (s3 Standard-ia) Storage Class 5 Years After The Object Was Last Accessed. Configure A Second Lifecycle Policy To Move The Raw Data Into Amazon S3 Glacier For Long-term Archival 7 Days After The Last Date The Object Was Accessed.

7 :-

An energy company collects voltage data in real time from sensors that are attached to buildings. The company wants to receive notifications when a sequence of two voltage drops is detected within 10 minutes of a sudden voltage increase at the same building. All notifications must be delivered as quickly as possible. The system must be highly available. The company needs a solution that will automatically scale when this monitoring feature is implemented in other cities. The notification system is subscribed to an Amazon Simple Notification Service (Amazon SNS) topic for remediation. Which solution will meet these requirements?

Create An Amazon Managed Streaming For Apache Kafka Cluster To Ingest The Data. Use An Apache Spark Streaming With Apache Kafka Consumer Api In An Automatically Scaled Amazon Emr Cluster To Process The Incoming Data. Use The Spark Streaming Application To Detect The Known Event Sequence And Send The Sns Message. Create A Rest-based Web Service By Using Amazon Api Gateway In Front Of An Aws Lambda Function. Create An Amazon Rds For Postgresql Database With Sufficient Provisioned Iops To Meet Current Demand. Configure The Lambda Function To Store Incoming Events In The Rds For Postgresql Database Query The Latest Data To Detect The Known Event Sequence And Send The Sns Message. Create An Amazon Kinesis Data Firehose Delivery Stream To Capture The Incoming Sensor Data.use An Aws Lambda Transformation Function To Detect The Known Event Sequence And Send The Sns Message. Create An Amazon Kinesis Data Stream To Capture The Incoming Sensor Data. Create Another Stream For Notifications. Set Up Aws Application Auto Scaling On Both Streams. Create An Amazon Kinesis Data Analytics For Java Application To Detect The Known Event Sequence And Add A Message To The Message Stream Configure An Aws Lambda Function To Poll The Message Stream And Publish To The Sns Topic.

8 :-

A central government organization is collecting events from various internal applications using Amazon Managed Streaming for Apache Kafka (Amazon MSK). The organization has configured a separate Kafka topic for each application to separate the data. For security reasons, the Kafka cluster has been configured to only allow TLS encrypted data and it encrypts the data at rest. A recent application update showed that one of the applications was configured incorrectly, resulting in writing data to a Kafka topic that belongs to another application. This resulted in multiple errors in the analytics pipeline as data from different applications appeared on the same topic. After this incident, the organization wants to prevent applications from writing to a topic different than the one they should write to. Which solution meets these requirements with the least amount of effort?

Create A Different Amazon Ec2 Security Group For Each Application. Configure Each Security Group To Have Access To A Specific Topic In The Amazon Msk Cluster. Attach The Security Group To Each Application Based On The Topic That The Applications Should Read And Write To. Install Kafka Connect On Each Application Instance And Configure Each Kafka Connect Instance To Write To A Specific Topic Only. Use Kafka Acls And Configure Read And Write Permissions For Each Topic. Use The Distinguished Name Of The Clients’ Tls Certificates As The Principal Of The Acl. Create A Different Amazon Ec2 Security Group For Each Application. Create An Amazon Msk Cluster And Kafka Topic For Each Application. Configure Each Security Group To Have Access To The Specific Cluster.

9 :-

A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental data. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only. Which solution achieves these required access patterns to minimize costs and administrative tasks?

Consolidate All Aws Accounts Into One Account. Create Different S3 Buckets For Each Department And Move All The Data From Every Account To The Central Data Lake Account. Migrate The Individual Data Catalogs Into A Central Data Catalog And Apply Fine-grained Permissions To Give To Each User The Required Access To Tables And Databases In Aws Glue And Amazon S3. Keep The Account Structure And The Individual Aws Glue Catalogs On Each Account. Add A Central Data Lake Account And Use Aws Glue To Catalog Data From Various Accounts. Configure Crossaccount Access For Aws Glue Crawlers To Scan The Data In Each Departmental S3 Bucket To Identify The Schema And Populate The Catalog. Add The Senior Data Analysts Into The Central Account And Apply Highly Detailed Access Controls In The Data Catalog And Amazon S3. Set Up An Individual Aws Account For The Central Data Lake. Use Aws Lake Formation To Catalogthe Cross-account Locations. On Each Individual S3 Bucket Modify The Bucket Policy To Grant S3 Permissions To The Lake Formation Service-linked Role. Use Lake Formation Permissions To Add Fine-grained Access Controls To Allow Senior Analysts To View Specific Tables And Columns. Set Up An Individual Aws Account For The Central Data Lake And Configure A Central S3 Bucket. Use An Aws Lake Formation Blueprint To Move The Data From The Various Buckets Into The Central S3 Bucket. On Each Individual Bucket Modify The Bucket Policy To Grant S3 Permissions To The Lake Formation Service-linked Role. Use Lake Formation Permissions To Add Fine-grained Access Controls For Both Associate And Senior Analysts To View Specific Tables And Columns.

10 :-

A company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and need consistent performance. Some of the data is queried more frequently than the rest. The company wants a solution that meets its performance requirements in a cost-effective manner. Which solution meets these requirements?

Use Amazon Managed Streaming For Apache Kafka To Ingest The Data To Save It To Amazon S3. Use Amazon Athena To Perform Sql Queries Over The Ingested Data. Use Amazon Managed Streaming For Apache Kafka To Ingest The Data To Save It To Amazon Redshift. Enable Amazon Redshift Workload Management (wlm) To Prioritize Workloads. Use Amazon Kinesis Data Firehose To Ingest The Data To Save It To Amazon Redshift. Enableamazon Redshift Workload Management (wlm) To Prioritize Workloads. Use Amazon Kinesis Data Firehose To Ingest The Data To Save It To Amazon S3. Load Frequently Queried Data To Amazon Redshift Using The Copy Command. Use Amazon Redshift Spectrum For Less Frequently Queried Data.

11 :-

A streaming application is reading data from Amazon Kinesis Data Streams and immediately writing the data to an Amazon S3 bucket every 10 seconds. The application is reading data from hundreds of shards. The batch interval cannot be changed due to a separate requirement. The data is being accessed by Amazon Athena. Users are seeing degradation in query performance as time progresses. Which action can help improve query performance?

Merge The Files In Amazon S3 To Form Larger Files. Increase The Number Of Shards In Kinesis Data Streams. Add More Memory And Cpu Capacity To The Streaming Application. Write The Files To Multiple S3 Buckets.

12 :-

A banking company is currently using an Amazon Redshift cluster with dense storage (DS) nodesto store sensitive data. An audit found that the cluster is unencrypted. Compliance requirements state that a database with sensitive data must be encrypted through a hardware security module (HSM) with automated key rotation. Which combination of steps is required to achieve compliance?

Set Up A Trusted Connection With Hsm Using A Client And Server Certificate With Automatic Key Rotation. Modify The Cluster With An Hsm Encryption Option And Automatic Key Rotation Create A New Hsm-encrypted Amazon Redshift Cluster And Migrate The Data To The New Cluster Enable Hsm With Key Rotation Through The Aws Cli. Enable Elliptic Curve Diffie-hellman Ephemeral (ecdhe) Encryption In The Hsm.

13 :-

A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in anAmazon DynamoDB table. Which solution meets the requirements for the event collection and enrichment?

Use A Cloudwatch Logs Subscription To Send The Data To Amazon Kinesis Data Firehose. Use Aws Lambda To Transform The Data In The Kinesis Data Firehose Delivery Stream And Enrich It With The Data In The Dynamodb Table. Configure Amazon S3 As The Kinesis Data Firehose Delivery Destination. Export The Raw Logs To Amazon S3 On An Hourly Basis Using The Aws Cli. Use Aws Glue Crawlers To Catalog The Logs. Set Up An Aws Glue Connection For The Dynamodb Table And Set Up An Aws Glue Etl Job To Enrich The Data. Store The Enriched Data In Amazon S3. Configure The Application To Write The Logs Locally And Use Amazon Kinesis Agent To Send The Data To Amazon Kinesis Data Streams. Configure A Kinesis Data Analytics Sql Application With The Kinesis Data Stream As The Source. Join The Sql Application Input Stream With Dynamodb Records And Then Store The Enriched Output Stream In Amazon S3 Using Amazon Kinesis Data Firehose. Export The Raw Logs To Amazon S3 On An Hourly Basis Using The Aws Cli. Use Apache Spark Sql On Amazon Emr To Read The Logs From Amazon S3 And Enrich The Records With The Data From Dynamodb. Store The Enriched Data In Amazon S3.

14 :-

An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and us_5_digit_zip_code. A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application. How should the data analyst meet this requirement while minimizing costs?

Store The Contents Of The Mapping File In An Amazon Dynamodb Table. Preprocess The Records As They Arrive In The Kinesis Data Analytics Application With An Aws Lambda Function That Fetches The Mapping And Supplements Each Record To Include The Territory Code If One Exists. Change The Sql Query In The Application To Include The New Field In The Select Statement. Store The Mapping File In An Amazon S3 Bucket And Configure The Reference Data Column Headers For The .csv File In The Kinesis Data Analytics Application. Change The Sql Query In The Application To Include A Join To The File’s S3 Amazon Resource Name (arn) And Add The Territory Code Field To The Select Columns. Store The Mapping File In An Amazon S3 Bucket And Configure It As A Reference Data Source For The Kinesis Data Analytics Application. Change The Sql Query In The Application To Include A Join To The Reference Table And Add The Territory Code Field To The Select Columns. Store The Contents Of The Mapping File In An Amazon Dynamodb Table. Change The Kinesis Data Analytics Application To Send Its Output To An Aws Lambda Function That Fetches The Mapping Andsupplements Each Record To Include The Territory Code If One Exists. Forward The Record From The Lambda Function To The Original Application Destination.

15 :-

A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users. The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB. Which configuration will provide the MOST cost-effective solution that meets these requirements?

Load The Data Into An Amazon Redshift Cluster By Using The Copy Command. Configure 50 Author Users And 1 000 Reader Users. Use Quicksight Enterprise Edition. Configure An Amazon Redshift Data Source With A Direct Query Option. Use Quicksight Standard Edition. Configure 50 Author Users And 1 000 Reader Users. Configure An Athena Data Source With A Direct Query Option. Use Quicksight Enterprise Edition. Configure 50 Author Users And 1 000 Reader Users. Configure An Athena Data Source And Import The Data Into Spice. Automatically Refresh Every 24 Hours. Use Quicksight Enterprise Edition. Configure 1 Administrator And 1 000 Reader Users. Configure An S3 Data Source And Import The Data Into Spice. Automatically Refresh Every 24 Hours.

16 :-

A media company has a streaming playback application. The company needs to collect and analyze data to provide near-real-time feedback on playback issues within 30 seconds. The company requires a consumer application to identify playback issues, such as decreased quality during a specified time frame. The data will be streamed in JSON format. The schema can change over time. Which solution will meet these requirements?

Send The Data To Amazon Kinesis Data Firehose With Delivery To Amazon S3. Configure An S3 Event To Invoke An Aws Lambda Function To Process And Analyze The Data. Send The Data To Amazon Managed Streaming For Apache Kafka. Configure Amazon Kinesis Data Analytics For Sql Application As The Consumer Application To Process And Analyze The Data. Send The Data To Amazon Kinesis Data Firehose With Delivery To Amazon S3. Configure Amazon S3 To Initiate An Event For Aws Lambda To Process And Analyze The Data. Send The Data To Amazon Kinesis Data Streams. Configure An Amazon Kinesis Data Analytics For Apache Flink Application As The Consumer Application To Process And Analyze The Data.

17 :-

A company is migrating its existing on-premises ETL jobs to Amazon EMR. The code consists of a series of jobs written in Java. The company needs to reduce overhead for the system administrators without changing the underlying code. Due to the sensitivity of the data, compliance requires that the company use root device volume encryption on all nodes in the cluster. Corporate standards require that environments be provisioned though AWS CloudFormation when possible. Which solution satisfies these requirements?

Install Open-source Hadoop On Amazon Ec2 Instances With Encrypted Root Device Volumes. Configure The Cluster In The Cloudformation Template. Use A Cloudformation Template To Launch An Emr Cluster. In The Configuration Section Of The Cluster Define A Bootstrap Action To Enable Tls. Create A Custom Ami With Encrypted Root Device Volumes. Configure Amazon Emr To Use The Custom Ami Using The Customamild Property In The Cloudformation Template. Use A Cloudformation Template To Launch An Emr Cluster. In The Configuration Section Of The Cluster Define A Bootstrap Action To Encrypt The Root Device Volume Of Every Node.

18 :-

A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company. The data engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step Functions for process orchestration, and Amazon CloudWatch for job scheduling. More testing facilities were recently added, and the time to process files is increasing. What will MOST efficiently decrease the data processing time?

Use Aws Lambda To Group The Small Files Into Larger Files. Write The Files Back To Amazon S3. Process The Files Using Aws Glue And Load Them Into Amazon Redshift Tables. Use The Aws Glue Dynamic Frame File Grouping Option While Ingesting The Raw Input Files. Process The Files And Load Them Into Amazon Redshift Tables. Use The Amazon Redshift Copy Command To Move The Files From Amazon S3 Into Amazon Redshift Tables Directly. Process The Files In Amazon Redshift. Use Amazon Emr Instead Of Aws Glue To Group The Small Input Files. Process The Files In Amazon Emr And Load Them Into Amazon Redshift Tables.

19 :-

A market data company aggregates external data sources to create a detailed view of product consumption in different countries. The company wants to sell this data to external parties through a subscription. To achieve this goal, the company needs to make its data securely available to external parties who are also AWS users. What should the company do to meet these requirements with the LEAST operational overhead?

Store The Data In Amazon S3. Share The Data By Using Presigned Urls For Security. Store The Data In Amazon S3. Share The Data By Using S3 Bucket Acls. Upload The Data To Aws Data Exchange For Storage. Share The Data By Using Presigned Urls For Security. Upload The Data To Aws Data Exchange For Storage. Share The Data By Using The Aws Data Exchange Sharing Wizard.

20 :-

A large financial company is running its ETL process. Part of this process is to move data from Amazon S3 into an Amazon Redshift cluster. The company wants to use the most cost-efficient method to load the dataset into Amazon Redshift. Which combination of steps would meet these requirements?

Use The Copy Command With The Manifest File To Load Data Into Amazon Redshift. Use S3distcp To Load Files Into Amazon Redshift. Use Temporary Staging Tables During The Loading Process. Use The Unload Command To Upload Data Into Amazon Redshift. Use Amazon Redshift Spectrum To Query Files From Amazon S3

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Data analytics Quiz

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