Quiz IT certification exam

Question 1

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?

Accepted Answer

Store the data in Amazon S3. Share the data by using presigned URLs for security.

Answer

Store the data in Amazon S3. Share the data by using S3 bucket ACLs.

Answer

Upload the data to AWS Data Exchange for storage. Share the data by using presigned URLs for security.

Answer

Upload the data to AWS Data Exchange for storage. Share the data by using the AWS Data Exchange sharing wizard.

Question 2

A company uses Amazon Redshift as its data warehouse. A new table has columns that contain sensitive data. The data in the table will eventually be referenced by several existing queries that run many times a day. A data analyst needs to load 100 billion rows of data into the new table. Before doing so, the data analyst must ensure that only members of the auditing group can read the columns containing sensitive data. How can the data analyst meet these requirements with the lowest maintenance overhead?

Accepted Answer

Load all the data into the new table and grant the auditing group permission to read from the table. Create a view of the new table that contains all the columns, except for those considered sensitive, and grant the appropriate users read-only permissions to the table.

Answer

Load all the data into the new table and grant the auditing group permission to read from the table. Load all the data except for the columns containing sensitive data into a second table. Grant the appropriate users read-only permissionsto the second table.

Answer

Load all the data into the new table and grant the auditing group permission to read from the table. Use the GRANT SQL command to allow read-only access to a subset of columns to the appropriate users.

Answer

Load all the data into the new table and grant all users read-only permissions to non-sensitive columns. Attach an IAM policy to the auditing group with explicit ALLOW access to the sensitive data columns.

Question 3

A hospital is building a research data lake to ingest data from electronic health records (EHR) systems from multiple hospitals and clinics. The EHR systems are independent of each other and do not have a common patient identifier. The data engineering team is not experienced in machine learning (ML) and has been asked to generate a unique patient identifier for the ingested records. Which solution will accomplish this task?

Accepted Answer

An AWS Glue ETL job with the FindMatches transform

Answer

Amazon Kendra

Answer

Amazon SageMaker Ground Truth

Answer

An AWS Glue ETL job with the ResolveChoice transform

Question 4

An online retailer is rebuilding its inventory management system and inventory reordering system to automatically reorder products by using Amazon Kinesis Data Streams. The inventory management system uses the Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Kinesis Client Library (KCL) to consume data from the stream. The stream has been configured to scale as needed. Just before production deployment, the retailer discovers that the inventory reordering system is receiving duplicated data. Which factors could be causing the duplicated data? (Choose two.)

Accepted Answer

The stream’s value for the IteratorAgeMilliseconds metric is too high.

Accepted Answer

The AggregationEnabled configuration property was set to true.

Answer

The producer has a network-related timeout.

Answer

There was a change in the number of shards, record processors, or both.

Answer

The max_records configuration property was set to a number that is too high.

Question 5

A marketing company is storing its campaign response data in Amazon S3. A consistent set of sources has generated the data for each campaign. The data is saved into Amazon S3 as .csv files. A business analyst will use Amazon Athena to analyze each campaign’s data. The company needs the cost of ongoing data analysis with Athena to be minimized. Which combination of actions should a data analytics specialist take to meet these requirements? (Choose two.)

Accepted Answer

Convert the .csv files to Apache Avro.

Accepted Answer

Partition the data by campaign.

Answer

Convert the .csv files to Apache Parquet.

Answer

Partition the data by source.

Answer

Compress the .csv files.

Question 6

A company wants to research user turnover by analyzing the past 3 months of user activities. With millions of users, 1.5 TB of uncompressed data is generated each day. A 30-node Amazon Redshift cluster with 2.56 TB of solid state drive (SSD) storage for each node is required to meet the query performance goals.The company wants to run an additional analysis on a year’s worth of historical data to examine trends indicating which features are most popular. This analysis will be done once a week. What is the MOST cost-effective solution?

Accepted Answer

Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by date. Then use Amazon Redshift Spectrum for the additional analysis.

Answer

Increase the size of the Amazon Redshift cluster to 120 nodes so it has enough storage capacity to hold 1 year of data.Then use Amazon Redshift for the additional analysis.

Answer

Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by date. Then provision a persistent Amazon EMR cluster and use Apache Prestofor the additional analysis.

Answer

Resize the cluster node type to the dense storage node type (DS2) for an additional 16 TB storage capacity on each individual node in the Amazon Redshift cluster. Then use Amazon Redshift for the additional analysis.

Question 7

An online retail company uses Amazon Redshift to store historical sales transactions. The company is required to encrypt data at rest in the clusters to comply with the Payment Card Industry Data Security Standard (PCI DSS). A corporate governance policy mandates management of encryption keys using an on-premises hardware security module (HSM). Which solution meets these requirements?

Accepted Answer

Create a VPC and establish a VPN connection between the VPC and the on-premises network. Create an HSM connection and client certificate for the on-premises HSM. Launch a cluster in the VPC with the option to use the onpremisesHSM to store keys.

Answer

Create and manage encryption keys using AWS CloudHSM Classic. Launch an Amazon Redshift cluster in a VPC with the option to use CloudHSM Classic for key management.

Answer

Create an HSM connection and client certificate for the on-premises HSM. Enable HSM encryption on the existing unencrypted cluster by modifying the cluster. Connect to the VPC where the Amazon Redshift cluster resides from theonpremises network using a VPN.

Answer

Create a replica of the on-premises HSM in AWS CloudHSM. Launch a cluster in a VPC with the option to use CloudHSM to store keys.

Question 8

A web retail company wants to implement a near-real-time clickstream analytics solution. The company wants to analyze the data with an open-source package. The analytics application will process the raw data only once, but other applications will need immediate access to the raw data for up to 1 year.Which solution meets these requirements with the LEAST amount of operational effort?

Accepted Answer

Use Amazon Kinesis Data Streams to collect the data. Use Amazon Kinesis Data Analytics with Apache Flink to process the data in real time. Set the retention period of the Kinesis data stream to 8,760 hours.

Answer

Use Amazon Kinesis Data Streams to collect the data. Use Amazon EMR with Apache Flink to consume and process the data from the Kinesis data stream. Set the retention period of the Kinesis data stream to 8.760 hours.

Answer

Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to collect the data. Use Amazon EMR with Apache Flink to consume and process the data from the Amazon MSK stream. Set the log retention hours to 8,760.

Answer

Use Amazon Kinesis Data Streams to collect the data. Use Amazon EMR with Apache Flink to consume and process the data from the Kinesis data stream. Create an Amazon Kinesis Data Firehose delivery stream to store the data inAmazon S3. Set an S3 Lifecycle policy to delete the data after 365 days.

Question 9

A company’s data analyst needs to ensure that queries run in Amazon Athena cannot scan more than a prescribed amount of data for cost control purposes. Queries that exceed the prescribed threshold must be canceled immediately. What should the data analyst do to achieve this?

Accepted Answer

For each workgroup, set the workgroup-wide data usage control limit to the prescribed threshold.

Answer

Configure Athena to invoke an AWS Lambda function that terminates queries when the prescribed threshold is crossed.

Answer

For each workgroup, set the control limit for each query to the prescribed threshold.

Answer

Enforce the prescribed threshold on all Amazon S3 bucket policies

Question 10

A retail company is using an Amazon S3 bucket to host an ecommerce data lake. The company is using AWS Lake Formation to manage the data lake.A data analytics specialist must provide access to a new business analyst team. The team will use Amazon Athena from the AWS Management Console to query data from existing web_sales and customer tables in the ecommerce database. The team needs read-only access and the ability to uniquely identify customers by using first and last names. However, the team must not be able to see any other personally identifiable data. The table structure is as follows:<img alt="Amazon DAS-C01 image Question 50 2464 09162024005525000000"   width="617" height="316" src="https://examgecko.com/getfile/f56789d6-1a47-4780-86ce-41aa4ebbd986">Which combination of steps should the data analytics specialist take to provide the required permission by using the principle of least privilege? (Choose three.)

Accepted Answer

In AWS Lake Formation, grant the business_analyst group the SELECT permission for the web_sales table.

Accepted Answer

In AWS Lake Formation, grant the business_analyst group SELECT and ALTER permissions for the customer table.Under columns, choose filter type “Include columns” with columns fisrt_name and last_name.

Accepted Answer

Create users under a business_analyst IAM group. Create a policy that allows the lakeformation:GetDataAccess action, the athena:* action, and the glue:Get* action. In addition, allow the s3:GetObject action, the s3:PutObject action, andthe s3:GetBucketLocation action for the Athena query results S3 bucket.

Answer

In AWS Lake Formation, grant the business_analyst group SELECT and ALTER permissions for the web_sales table.

Answer

In AWS Lake Formation, grant the business_analyst group the SELECT permission for the customer table. Under columns, choose filter type “Include columns” with columns fisrt_name, last_name, and customer_id.

Answer

Create users under a business_analyst IAM group. Create a policy that allows the lakeformation:GetDataAccess action, the athena:* action, and the glue:Get* action.

Question 11

A reseller that has thousands of AWS accounts receives AWS Cost and Usage Reports in an Amazon S3 bucket. The reports are delivered to the S3 bucket in the following format://yyyymmdd-yyyymmdd/.parquetAn AWS Glue crawler crawls the S3 bucket and populates an AWS Glue Data Catalog with a table. Business analysts use Amazon Athena to query the table and create monthly summary reports for the AWS accounts. The business analysts are experiencing slow queries because of the accumulation of reports from the last 5 years. The business analysts want the operations team to make changes to improve query performance. Which action should the operations team take to meet these requirements?

Accepted Answer

Change the file format to .csv.zip

Answer

Partition the data by date and account ID

Answer

Partition the data by month and account ID

Answer

Partition the data by account ID, year, and month

Question 12

A company uses Amazon Redshift to store its data. The reporting team runs ad-hoc queries to generate reports from the Amazon Redshift database. The reporting team recently started to experience inconsistencies in report generation. Adhoc queries used to generate reports that would typically take minutes to run can take hours to run. A data analytics specialist debugging the issue finds that ad-hoc queries are stuck in the queue behind long-running queries. How should the data analytics specialist resolve the issue?

Accepted Answer

Create Amazon Simple Queue Service (Amazon SQS) queues with different priorities. Assign queries to a queue based on priority.

Answer

Create partitions in the tables queried in ad-hoc queries.

Answer

Configure automatic workload management (WLM) from the Amazon Redshift console.

Answer

Run the VACUUM command for all tables in the database.

Question 13

A mobile gaming company wants to capture data from its gaming app and make the data available for analysis immediately.The data record size will be approximately 20 KB. The company is concerned about achieving optimal throughput from each device. Additionally, the company wants to develop a data stream processing application with dedicated throughput for each consumer.Which solution would achieve this goal?

Accepted Answer

Have the app call the PutRecords API to send data to Amazon Kinesis Data Streams. Host the stream-processing application on Amazon EC2 with Auto Scaling.

Answer

Have the app call the PutRecords API to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature while consuming the data.

Answer

Have the app call the PutRecordBatch API to send data to Amazon Kinesis Data Firehose. Submit a support case to enable dedicated throughput on the account.

Answer

Have the app use Amazon Kinesis Producer Library (KPL) to send data to Kinesis Data Firehose. Use the enhanced fanout feature while consuming the data.

Question 14

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?

Accepted Answer

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 cross-account access for AWS Glue crawlers toscan 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.

Answer

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 catalogand apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.

Answer

Set up an individual AWS account for the central data lake. Use AWS Lake Formation to catalog the cross-account locations. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formationservicelinked role. Use Lake Formation permissions to add fine-grained access controls to allow senior analysts to view specific tables and columns.

Answer

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.

Question 15

A manufacturing company is storing data from its operational systems in Amazon S3. The company’s business analysts need to perform one-time queries of the data in Amazon S3 with Amazon Athena. The company needs to access the Athena network from the on-premises network by using a JDBC connection. The company has created a VPC Security policies mandate that requests to AWS services cannot traverse the Internet.Which combination of steps should a data analytics specialist take to meet these requirements? (Choose two.)

Accepted Answer

Establish an AWS Direct Connect connection between the on-premises network and the VPC.

Accepted Answer

Deploy Athena within a private subnet.

Answer

Configure the JDBC connection to connect to Athena through Amazon API Gateway.

Answer

Configure the JDBC connection to use a gateway VPC endpoint for Amazon S3.

Answer

Configure the JDBC connection to use an interface VPC endpoint for Athena.

Question 16

A company receives data from its vendor in JSON format with a timestamp in the file name. The vendor uploads the data to an Amazon S3 bucket, and the data is registered into the company’s data lake for analysis and reporting. The company has configured an S3 Lifecycle policy to archive all files to S3 Glacier after 5 days.The company wants to ensure that its AWS Glue crawler catalogs data only from S3 Standard storage and ignores the archived files. A data analytics specialist must implement a solution to achieve this goal without changing the current S3 bucket configuration.Which solution meets these requirements?

Accepted Answer

Use the exclude patterns feature of AWS Glue to identify the S3 Glacier files for the crawler to exclude.

Answer

Schedule an automation job that uses AWS Lambda to move files from the original S3 bucket to a new S3 bucket for S3 Glacier storage.

Answer

Use the excludeStorageClasses property in the AWS Glue Data Catalog table to exclude files on S3 Glacier storage.

Answer

Use the include patterns feature of AWS Glue to identify the S3 Standard files for the crawler to include.

Question 17

A US-based sneaker retail company launched its global website. All the transaction data is stored in Amazon RDS and curated historic transaction data is stored in Amazon Redshift in the us-east-1 Region. The business intelligence (BI) team wants to enhance the user experience by providing a dashboard for sneaker trends.The BI team decides to use Amazon QuickSight to render the website dashboards. During development, a team in Japan provisioned Amazon QuickSight in ap-northeast-1. The team is having difficulty connecting Amazon QuickSight from apnortheast- 1 to Amazon Redshift in us-east-1.Which solution will solve this issue and meet the requirements?

Accepted Answer

Create a VPC endpoint from the Amazon QuickSight VPC to the Amazon Redshift VPC so Amazon QuickSight can access data from Amazon Redshift.

Answer

In the Amazon Redshift console, choose to configure cross-Region snapshots and set the destination Region as apnortheast- 1. Restore the Amazon Redshift Cluster from the snapshot and connect to Amazon QuickSight launched inapnortheast- 1.

Answer

Create an Amazon Redshift endpoint connection string with Region information in the string and use this connection string in Amazon QuickSight to connect to Amazon Redshift.

Answer

Create a new security group for Amazon Redshift in us-east-1 with an inbound rule authorizing access from the appropriate IP address range for the Amazon QuickSight servers in ap-northeast-1.

Question 18

A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.Which approach would allow the developers to solve the issue with minimal coding effort?

Accepted Answer

Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.

Answer

Have the ETL jobs read the data from Amazon S3 using a DataFrame.

Answer

Enable job bookmarks on the AWS Glue jobs.

Answer

Create custom logic on the ETL jobs to track the processed S3 objects.

Question 19

A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in nearreal 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?

Accepted Answer

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.

Answer

Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Answer

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).

Answer

Use AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Question 20

A real estate company maintains data about all properties listed in a market. The company receives data about new property listings from vendors who upload the data daily as compressed files into Amazon S3. The company’s leadership team wants to see the most up-to-date listings as soon as the data is uploaded to Amazon S3. The data analytics team must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The team also must provide the ability to perform one-time queries and analytical reporting in a scalable manner. Which solution meets these requirements MOST cost-effectively?

Accepted Answer

Use Amazon EMR for processing incoming data. Use AWS Step Functions for workflow orchestration. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.

Answer

Use Amazon EMR for processing incoming data. Use AWS Step Functions for workflow orchestration. Use Apache Hive for one-time queries and analytical reporting. Bulk ingest the data in Amazon OpenSearch Service (AmazonElasticsearch Service). Use OpenSearch Dashboards (Kibana) on Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboard.

Answer

Use AWS Glue for processing incoming data. Use AWS Step Functions for workflow orchestration. Use Amazon Redshift Spectrum for one-time queries and analytical reporting. Use OpenSearch Dashboards (Kibana) on AmazonOpenSearch Service (Amazon Elasticsearch Service) for the dashboard.

Answer

Use AWS Glue for processing incoming data. Use AWS Lambda and S3 Event Notifications for workflow orchestration.Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.

Question 21

A software company hosts an application on AWS, and new features are released weekly. As part of the application testing process, a solution must be developed that analyzes logs from each Amazon EC2 instance to ensure that the application is working as expected after each deployment. The collection and analysis solution should be highly available with the ability to display new information with minimal delays. Which method should the company use to collect and analyze the logs?

Accepted Answer

Use Amazon CloudWatch subscriptions to get access to a real-time feed of logs and have the logs delivered to Amazon Kinesis Data Streams to further push the data to Amazon OpenSearch Service (Amazon Elasticsearch Service) andOpenSearch Dashboards (Kibana).

Answer

Enable detailed monitoring on Amazon EC2, use Amazon CloudWatch agent to store logs in Amazon S3, and use Amazon Athena for fast, interactive log analytics.

Answer

Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Streams to further push the data to Amazon OpenSearch Service (Amazon Elasticsearch Service) and visualize usingAmazon QuickSight.

Answer

Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Firehose to further push the data to Amazon OpenSearch Service (Amazon Elasticsearch Service) and OpenSearchDashboards (Kibana).

Question 22

A financial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of sluggish performance.A data analyst notes the following:Approximately 90% of queries are submitted 1 hour after the market opens. Hadoop Distributed File System (HDFS) utilization never exceeds 10%. Which solution would help address the performance issues?

Accepted Answer

Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy toscale in the instance groups based on the CloudWatch CapacityRemainingGB metric.

Answer

Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy toscale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.

Answer

Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automatic scalingpolicy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric.

Answer

Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automaticscaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric.

Question 23

A company hosts an on-premises PostgreSQL database that contains historical data. An internal legacy application uses the database for read-only activities. The company’s business team wants to move the data to a data lake in Amazon S3 as soon as possible and enrich the data for analytics.The company has set up an AWS Direct Connect connection between its VPC and its on-premises network. A data analytics specialist must design a solution that achieves the business team’s goals with the least operational overhead. Which solution meets these requirements?

Accepted Answer

Create an Amazon RDS for PostgreSQL database and use AWS Database Migration Service (AWS DMS) to migrate the data into Amazon RDS. Use AWS Data Pipeline to copy and enrich the data from the Amazon RDS forPostgreSQL table and move the data to Amazon S3. Use Amazon Athena to query the data.

Answer

Upload the data from the on-premises PostgreSQL database to Amazon S3 by using a customized batch upload process.Use the AWS Glue crawler to catalog the data in Amazon S3. Use an AWS Glue job to enrich and store the result in a separate S3 bucket in Apache Parquet format. Use Amazon Athena to query the data.

Answer

Configure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises database. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet format. Create an AmazonRedshift cluster and use Amazon Redshift Spectrum to query the data.

Answer

Configure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises database. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet format. Use Amazon Athena toquery the data.

Question 24

An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)

Accepted Answer

Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.

Accepted Answer

Compress the objects to reduce the data transfer I/O.

Accepted Answer

Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.

Answer

Use an S3 bucket in the same account as Athena.

Answer

Use an S3 bucket in the same Region as Athena.

Answer

Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.

Question 25

A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support nearrealtime data.Which visualization solution will meet these requirements?

Accepted Answer

Select Amazon OpenSearch Service (Amazon Elasticsearch Service) as the endpoint for Kinesis Data Firehose. Set up an OpenSearch Dashboards (Kibana) using the data in Amazon OpenSearch Service (Amazon ES) with the desiredanalyses and visualizations.

Answer

Select Amazon S3 as the endpoint for Kinesis Data Firehose. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.

Answer

Select Amazon Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.

Answer

Select Amazon S3 as the endpoint for Kinesis Data Firehose. Use AWS Glue to catalog the data and Amazon Athena to query it. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.

Question 26

A technology company has an application with millions of active users every day. The company queries daily usage data with Amazon Athena to understand how users interact with the application. The data includes the date and time, the location ID, and the services used. The company wants to use Athena to run queries to analyze the data with the lowest latency possible. Which solution meets these requirements?

Accepted Answer

Store the data in Apache ORC format with the location ID as the partition, with the data sorted by the date and time.

Answer

Store the data in Apache Avro format with the date and time as the partition, with the data sorted by the location ID.

Answer

Store the data in Apache Parquet format with the date and time as the partition, with the data sorted by the location ID.

Answer

Store the data in .csv format with the location ID as the partition, with the data sorted by the date and time.

Question 27

A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running adhoc queries, and a data engineering group to run stored procedures and ETL processes. The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes. Which Amazon Redshift feature meets the requirements for this task?

Accepted Answer

Materialized views

Answer

Concurrency scaling

Answer

Short query acceleration (SQA)

Answer

Workload management (WLM)

Question 28

A retail company is building its data warehouse solution using Amazon Redshift. As a part of that effort, the company is loading hundreds of files into the fact table created in its Amazon Redshift cluster. The company wants the solution to achieve the highest throughput and optimally use cluster resources when loading data into the company’s fact table. How should the company meet these requirements?

Accepted Answer

Use S3DistCp to load multiple files into the Hadoop Distributed File System (HDFS) and use an HDFS connector to ingest the data into the Amazon Redshift cluster.

Answer

Use multiple COPY commands to load the data into the Amazon Redshift cluster.

Answer

Use LOAD commands equal to the number of Amazon Redshift cluster nodes and load the data in parallel into each node.

Answer

Use a single COPY command to load the data into the Amazon Redshift cluster.

Question 29

A company is migrating from an on-premises Apache Hadoop cluster to an Amazon EMR cluster. The cluster runs only during business hours. Due to a company requirement to avoid intraday cluster failures, the EMR cluster must be highly available. When the cluster is terminated at the end of each business day, the data must persist. Which configurations would enable the EMR cluster to meet these requirements? (Choose three.)

Accepted Answer

Hadoop Distributed File System (HDFS) for storage

Accepted Answer

AWS Glue Data Catalog as the metastore for Apache Hive

Accepted Answer

Multiple master nodes in multiple Availability Zones

Answer

EMR File System (EMRFS) for storage

Answer

MySQL database on the master node as the metastore for Apache Hive

Answer

Multiple master nodes in a single Availability Zone

Question 30

An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities.Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an application running on Amazon EC2 processes the data and makes search options and reports available for visualization by editors and marketers. The company wants to make website clicks and aggregated data available to editors and marketers in minutes to enable them to connect with users more effectively.Which options will help meet these requirements in the MOST efficient way? (Choose two.)

Accepted Answer

Use Amazon OpenSearch Service (Amazon Elasticsearch Service) deployed on Amazon EC2 to aggregate, filter, and process the data. Refresh content performance dashboards in near-real time.

Accepted Answer

Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon OpenSearch Service (Amazon Elasticsearch Service).

Answer

Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon OpenSearch Service (Amazon Elasticsearch Service).

Answer

Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon OpenSearch Service (Amazon Elasticsearch Service) from Amazon S3.

Answer

Use OpenSearch Dashboards (Kibana) to aggregate, filter, and visualize the data stored in Amazon OpenSearch Service (Amazon Elasticsearch Service). Refresh content performance dashboards in near-real time.

Question 31

A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations. Station A, which has 10 sensorsStation B, which has five sensorsThese weather stations were placed by onsite subject-matter experts.Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created.Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B. Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?

Accepted Answer

Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.

Answer

Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream. C. Modify the partition key to use the sensor ID instead of the station name.

Answer

Reduce the number of sensors in Station A from 10 to 5 sensors.

Question 32

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?

Accepted Answer

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.

Answer

Install open-source Hadoop on Amazon EC2 instances with encrypted root device volumes. Configure the cluster in the CloudFormation template.

Answer

Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a bootstrap action to enable TLS.

Answer

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.

Question 33

A company provides an incentive to users who are physically active. The company wants to determine how active the users are by using an application on their mobile devices to track the number of steps they take each day. The company needs to ingest and perform near-real-time analytics on live data. The processed data must be stored and must remain available for 1 year for analytics purposes. Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

Ingest the data into Amazon DynamoDB by using an Amazon API Gateway API as a DynamoDB proxy. Use an AWS Step Functions workflow to create a transient Amazon EMR cluster every hour and process the new data fromDynamoDOutput the processed data to Amazon Redshift to run analytics calculations. Archive the data from Amazon Redshift after 1 year.

Answer

Use Amazon Cognito to write the data from the application to Amazon DynamoD

Answer

Use an AWS Step Functions workflow to create a transient Amazon EMR cluster every hour and process the new data from DynamoDB. Output theprocessed data to Amazon Redshift for analytics. Archive the data from Amazon Redshift after 1 year.

Answer

Ingest the data into Amazon Kinesis Data Streams by using an Amazon API Gateway API as a Kinesis proxy. Run Amazon Kinesis Data Analytics on the stream data. Output the processed data into Amazon S3 by using Amazon KinesisData Firehose. Use Amazon Athena to run analytics calculations. Use S3 Lifecycle rules to transition objects to S3 Glacier after 1 year.

Answer

Write the data from the application into Amazon S3 by using Amazon Kinesis Data Firehose. Use Amazon Athena to run the analytics on the data in Amazon S3. Use S3 Lifecycle rules to transition objects to S3 Glacier after 1 year.

Question 34

A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the data analyst notices that, sometimes, the messages arrive out of order for account_id.Upon further investigation, the data analyst discovers the messages that are out of order seem to be arriving from different shards for the same account_id and are seen when a stream resize runs. What is an explanation for this behavior and what is the solution?

Accepted Answer

There are multiple shards in a stream and order needs to be maintained in the shard. The data analyst needs to make sure there is only a single shard in the stream and no stream resize runs.

Answer

The hash key generation process for the records is not working correctly. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.

Answer

The records are not being received by Kinesis Data Streams in order. The producer should use the PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering parameter.

Answer

Question 35

A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables.A trips fact table for information on completed rides.A drivers dimension table for driver profiles.A customers fact table holding customer profile information.The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes. What table design provides optimal query performance?

Accepted Answer

Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.

Answer

Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.

Answer

Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.

Answer

Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.

Question 36

An education provider’s learning management system (LMS) is hosted in a 100 TB data lake that is built on Amazon S3. The provider’s LMS supports hundreds of schools. The provider wants to build an advanced analytics reporting platform using Amazon Redshift to handle complex queries with optimal performance. System users will query the most recent 4 months of data 95% of the time while 5% of the queries will leverage data from the previous 12 months. Which solution meets these requirements in the MOST cost-effective way?

Accepted Answer

Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Ensure the S3 Standard storage class is in use with objects in the data lake.

Answer

Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacierstorage.

Answer

Leverage DS2 nodes for the Amazon Redshift cluster. Migrate all data from Amazon S3 to Amazon Redshift.Decommission the data lake.

Answer

Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift federated queries to join cluster data with the data lake to reduce costs. Ensure the S3 Standard storage class is in use with objects in the datalake.

Question 37

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? (Choose two.)

Accepted Answer

Use temporary staging tables during the loading process.

Accepted Answer

Use Amazon Redshift Spectrum to query files from Amazon S3.

Answer

Use the COPY command with the manifest file to load data into Amazon Redshift.

Answer

Use S3DistCp to load files into Amazon Redshift.

Answer

Use the UNLOAD command to upload data into Amazon Redshift.

Question 38

A telecommunications company is looking for an anomaly-detection solution to identify fraudulent calls. The company currently uses Amazon Kinesis to stream voice call records in a JSON format from its on-premises database to Amazon S3.The existing dataset contains voice call records with 200 columns. To detect fraudulent calls, the solution would need to look at 5 of these columns only. The company is interested in a cost-effective solution using AWS that requires minimal effort and experience in anomalydetection algorithms. Which solution meets these requirements?

Accepted Answer

Use an AWS Glue job to transform the data from JSON to Apache Parquet. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Use Amazon Athena to create a table with a subset of columns. UseAmazon QuickSight to visualize the data and then use Amazon QuickSight machine learning-powered anomaly detection.

Answer

Use Kinesis Data Firehose to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls and store the output in Amazon RDS. Use Amazon Athena to build a dataset andAmazon QuickSight to visualize the results.

Answer

Use an AWS Glue job to transform the data from JSON to Apache Parquet. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Use Amazon SageMaker to build an anomaly detection model that candetect fraudulent calls by ingesting data from Amazon S3.

Answer

Use Kinesis Data Analytics to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls. Connect Amazon QuickSight to Kinesis Data Analytics to visualize the anomalyscores.

Question 39

A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster using a separate COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake. Which solution meets these requirements?

Accepted Answer

Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.

Answer

Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.

Answer

Use an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.

Answer

Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.

Question 40

A financial services company is building a data lake solution on Amazon S3. The company plans to use analytics offerings from AWS to meet user needs for one-time querying and business intelligence reports. A portion of the columns will contain personally identifiable information (PII) Only authorized users should be able to see plaintext PII data. What is the MOST operationally efficient solution that meets these requirements?

Accepted Answer

Register the S3 locations with AWS Lake Formation. Create an AWS Glue job to create an ETL workflow that removes the PII columns from the data and creates a separate copy of the data in another data lake S3 bucket. Register thenew S3 locations with Lake Formation. Grant users the permissions to each data lake data based on whether the users are authorized to see PII data.

Answer

Define a bucket policy for each S3 bucket of the data lake to allow access to users who have authorization to see PII data. Catalog the data by using AWS Glue. Create two IAM roles. Attach a permissions policy with access to PIIcolumns to one role. Attach a policy without these permissions to the other role.

Answer

Register the S3 locations with AWS Lake Formation. Create two IAM roles. Use Lake Formation data permissions to grant Select permissions to all of the columns for one role. Grant Select permissions to only columns that contain non-PII data for the other role.

Answer

Register the S3 locations with AWS Lake Formation. Create two IAM roles. Attach a permissions policy with access to PII columns to one role. Attach a policy without these permissions to the other role. For each downstream analyticsservice, use its native security functionality and the IAM roles to secure the PII data.

Amazon DAS-C01 Practice Test 2

Amazon DAS-C01 Practice Tests