Quiz IT certification exam

Question 1

A company stores revenue data in Amazon Redshift. A data analyst needs to create a dashboard so that the company’s sales team can visualize historical revenue and accurately forecast revenue for the upcoming months. Which solution will MOST cost-effectively meet these requirements?

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Accepted Answer

Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a forecasting widget Publish the analysis as a dashboard.

Answer

Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a custom field in QuickSight that applies a linear regression function to the data. Publish the analysis as a dashboard.

Answer

Create a JavaScript dashboard by using D3.js charts and the data in Amazon Redshift. Export the data to Amazon SageMaker. Run a Python script to run a regression model to forecast revenue. Import the data back into AmazonRedshift.Add the new forecast information to the dashboard.

Answer

Create an Amazon SageMaker model for forecasting. Integrate the model with an Amazon QuickSight dataset. Create a widget for the dataset. Publish the analysis as a dashboard.

Question 2

A company is planning to do a proof of concept for a machine learning (ML) project using Amazon SageMaker with a subset of existing on-premises data hosted in the company’s 3 TB data warehouse. For part of the project, AWS Direct Connect is established and tested. To prepare the data for ML, data analysts are performing data curation. The data analysts want to perform multiple step, including mapping, dropping null fields, resolving choice, and splitting fields. The company needs the fastest solution to curate the data for this project. Which solution meets these requirements?

Accepted Answer

Ingest data into Amazon S3 using AWS DMS. Use AWS Glue to perform data curation and store the data in Amazon S3 for ML processing.

Answer

Ingest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon EMR cluster. Store the curated data in Amazon S3 for ML processing.

Answer

Create custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML processing.

Answer

Take a full backup of the data store and ship the backup files using AWS Snowball. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.

Question 3

An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query. Which steps will create the required logs?

Accepted Answer

Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.

Answer

Enable Amazon Redshift Enhanced VPC Routing. Enable VPC Flow Logs to monitor traffic.

Answer

Allow access to the Amazon Redshift database using AWS IAM only. Log access using AWS CloudTrail.

Answer

Enable and download audit reports from AWS Artifact.

Question 4

A company recently created a test AWS account to use for a development environment. The company also created a production AWS account in another AWS Region. As part of its security testing, the company wants to send log data from Amazon CloudWatch Logs in its production account to an Amazon Kinesis data stream in its test account. Which solution will allow the company to accomplish this goal?

Accepted Answer

Create a subscription filter in the production account’s CloudWatch Logs to target the Kinesis data stream in the test account as its destination. In the test account, create an IAM role that grants access to the Kinesis data stream and theCloudWatch Logs resources in the production account.

Answer

In the test account, create an IAM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account. Create a destination data stream in Kinesis Data Streams in the test account with anIAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account.

Answer

In the test account, create an IAM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account. Create a destination data stream in Kinesis Data Streams in the test account with anIAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account.

Answer

Create a destination data stream in Kinesis Data Streams in the test account with an IAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account. Create a subscription filter in theproduction account’s CloudWatch Logs to target the Kinesis data stream in the test account as its destination.

Question 5

A social media company is using business intelligence tools to analyze data for forecasting. The company is using Apache Kafka to ingest data. The company wants to build dynamic dashboards that include machine learning (ML) insights to forecast key business trends.The dashboards must show recent batched data that is not more than 75 minutes old. Various teams at the company want to view the dashboards by using Amazon QuickSight with ML insights.Which solution will meet these requirements?

Accepted Answer

Configure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to store the data in Amazon S3 with a max buffer size of 60 seconds. Use SPICE in QuickSight Enterprise edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.

Answer

Replace Kafka with Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use AWS Data Exchange to store the data in Amazon S3. Use SPICE in QuickSight Enterprise edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.

Answer

Replace Kafka with an Amazon Kinesis data stream. Use AWS Data Exchange to store the data in Amazon S3. Use SPICE in QuickSight Standard edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.

Answer

Configure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to store the data in Amazon S3 with a max buffer size of 60 seconds. Refresh the data in QuickSight Standard edition SPICE from Amazon S3 by using a scheduled AWS Lambda function. Configure the Lambda function to run every 75 minutes and to invoke the QuickSight API to create a dynamic dashboard that includes forecasting and ML insights.

Question 6

A large media company is looking for a cost-effective storage and analysis solution for its daily media recordings formatted with embedded metadata. Daily data sizes range between 10-12 TB with stream analysis required on timestamps, video resolutions, file sizes, closed captioning, audio languages, and more. Based on the analysis,processing the datasets is estimated to take between 30-180 minutes depending on the underlying framework selection. The analysis will be done by using business intelligence (Bl) tools that can be connected to data sources with AWS or Java Database Connectivity (JDBC) connectors.Which solution meets these requirements?

Accepted Answer

Store the video files in Amazon S3 and use AWS Lambda to extract the metadata from the files and load it to Amazon S3. Use Amazon Athena to provide the data to be analyzed by the BI tools.

Answer

Store the video files in Amazon DynamoDB and use AWS Lambda to extract the metadata from the files and load it to DynamoDB. Use DynamoDB to provide the data to be analyzed by the Bl tools.

Answer

Store the video files in Amazon DynamoDB and use Amazon EMR to extract the metadata from the files and load it to Apache Hive. Use Apache Hive to provide the data to be analyzed by the Bl tools.

Answer

Store the video files in Amazon S3 and use AWS Glue to extract the metadata from the files and load it to Amazon Redshift. Use Amazon Redshift to provide the data to be analyzed by the Bl tools.

Question 7

A company hosts its analytics solution on premises. The analytics solution includes a server that collects log files. The analytics solution uses an Apache Hadoop cluster to analyze the log files hourly and to produce output files. All the files are archived to another server for a specified duration.The company is expanding globally and plans to move the analytics solution to multiple AWS Regions in the AWS Cloud. The company must adhere to the data archival and retention requirements of each country where the data is stored.Which solution will meet these requirements?

Accepted Answer

Create an Amazon S3 bucket in each Region to collect log files. Create an Amazon EMR cluster. Submit steps on the EMR cluster for analysis. Store the output files in a target S3 bucket in each Region. Use S3 Lifecycle rules on each target S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.

Answer

Create an Amazon S3 bucket in one Region to collect the log files. Use S3 event notifications to invoke an AWS Glue job for log analysis. Store the output files in the target S3 bucket. Use S3 Lifecycle rules on the target S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.

Answer

Create a Hadoop Distributed File System (HDFS) file system on an Amazon EMR cluster in one Region to collect the log files. Set up a bootstrap action on the EMR cluster to run an Apache Spark job. Store the output files in a target Amazon S3 bucket. Schedule a job on one of the EMR nodes to delete files that no longer need to be retained.

Answer

Create an Amazon Kinesis Data Firehose delivery stream in each Region to collect log data. Specify an Amazon S3 bucket in each Region as the destination. Use S3 Storage Lens for data analysis. Use S3 Lifecycle rules on each destination S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.

Question 8

A company collects data from parking garages. Analysts have requested the ability to run reports in near real time about the number of vehicles in each garage.The company wants to build an ingestion pipeline that loads the data into an Amazon Redshift cluster. The solution must alert operations personnel when the number of vehicles in a particular garage exceeds a specific threshold. The alerting query will use garage threshold values as a static reference. The threshold values are stored inAmazon S3.What is the MOST operationally efficient solution that meets these requirements?

Accepted Answer

Use an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Create an Amazon Kinesis Data Analytics application that uses the same delivery stream as an input source. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and to compare the number of vehicles in a particular garage to the corresponding threshold value. Configure an AWS Lambda function to publish an Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

Answer

Use an Amazon Kinesis data stream to collect the data. Use an Amazon Kinesis Data Firehose delivery stream to deliver the data to Amazon Redshift. Create another Kinesis data stream to temporarily store the threshold values from Amazon S3. Send the delivery stream and the second data stream to Amazon Kinesis Data Analytics to compare the number of vehicles in a particular garage to the corresponding threshold value. Configure an AWS Lambda function to publish an C. Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

Answer

Use an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Automatically initiate an AWS Lambda function that queries the data in Amazon Redshift. Configure the Lambda function to compare the number of vehicles in a particular garage to the corresponding threshold value from Amazon S3. Configure the Lambda function to also publish an Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

Answer

Use an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Create an Amazon Kinesis Data Analytics application that uses the same delivery stream as an input source. Use Kinesis Data Analytics to compare the number of vehicles in a particular garage to the corresponding threshold value that is stored in a table as an in-application stream. Configure an AWS Lambda function as an output for the application to publish an Amazon Simple Queue Service (Amazon SQS) notification if the number of vehicles exceeds the threshold.

Question 9

A company plans to store quarterly financial statements in a dedicated Amazon S3 bucket. The financial statements must not be modified or deleted after they are saved to the S3 bucket.Which solution will meet these requirements?

Accepted Answer

Create the S3 bucket with S3 Object Lock in governance mode.

Answer

Create the S3 bucket with MFA delete enabled.

Answer

Create the S3 bucket with S3 Object Lock in compliance mode.

Answer

Create S3 buckets in two AWS Regions. Use S3 Cross-Region Replication (CRR) between the buckets.

Question 10

An event ticketing website has a data lake on Amazon S3 and a data warehouse on Amazon Redshift. Two datasets exist: events data and sales data. Each dataset has millions of records.The entire events dataset is frequently accessed and is stored in Amazon Redshift. However, only the last 6 months of sales data is frequently accessed and is stored in Amazon Redshift. The rest of the sales data is available only in Amazon S3.A data analytics specialist must create a report that shows the total revenue that each event has generated in the last 12 months. The report will be accessed thousands of times each week.Which solution will meet these requirements with the LEAST operational effort?

Accepted Answer

Create a materialized view in Amazon Redshift with the autorefresh option. Use Amazon Redshift Spectrum to include sales data that is older than 6 months.

Answer

Create an AWS Glue job to access sales data that is older than 6 months from Amazon S3 and to access event and sales data from Amazon Redshift. Load the results into a new table in Amazon Redshift.

Answer

Create a stored procedure to copy sales data that is older than 6 months and newer than 12 months from Amazon S3 to Amazon Redshift. Create a materialized view with the autorefresh option

Answer

Create an AWS Lambda function to copy sales data that is older than 6 months and newer than 12 months to an Amazon Kinesis Data Firehose delivery stream. Specify Amazon Redshift as the destination of the delivery stream. Create a materialized view with the autorefresh option.

Question 11

A machinery company wants to collect data from sensors. A data analytics specialist needs to implement a solution that aggregates the data in near-real time and saves the data to a persistent data store. The data must be stored in nested JSON format and must be queried from the data store with a latency of single-digit milliseconds.Which solution will meet these requirements?

Accepted Answer

Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data from Kinesis Data Firehose in Amazon DynamoDB.

Answer

Use Amazon Kinesis Data Streams to receive the data from the sensors. Use Amazon Kinesis Data Analytics to read the stream, aggregate the data, and send the data to an AWS Lambda function. Configure the Lambda function to store the data in Amazon DynamoDB.

Answer

Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use Amazon Kinesis Data Analytics to aggregate the data. Use an AWS Lambda function to read the data from Kinesis Data Analytics and store the data in Amazon S3.

Answer

Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data in Amazon S3.

Question 12

A financial company uses Amazon Athena to query data from an Amazon S3 data lake. Files are stored in the S3 data lake in Apache ORC format. Data analysts recently introduced nested fields in the data lake ORC files, and noticed that queries are taking longer to run in Athena. A data analysts discovered that more data than what is required is being scanned for the queries.What is the MOST operationally efficient solution to improve query performance?

Accepted Answer

Use the Athena query engine V2 and push the query filter to the source ORC file.

Answer

Flatten nested data and create separate files for each nested dataset.

Answer

Use Apache Parquet format instead of ORC format.

Answer

Recreate the data partition strategy and further narrow down the data filter criteria.

Question 13

A gaming company is building a serverless data lake. The company is ingesting streaming data into Amazon Kinesis Data Streams and is writing the data to Amazon S3 through Amazon Kinesis Data Firehose. The company is using 10 MB as the S3 buffer size and is using 90 seconds as the buffer interval. The company runs an AWS Glue ET L job to merge and transform the data to a different format before writing the data back to Amazon S3.Recently, the company has experienced substantial growth in its data volume. The AWS Glue ETL jobs are frequently showing an OutOfMemoryError error.Which solutions will resolve this issue without incurring additional costs? (Select TWO.)

Accepted Answer

Place the small files into one S3 folder. Define one single table for the small S3 files in AWS Glue Data Catalog. Rerun the AWS Glue ET L jobs against this AWS Glue table.

Accepted Answer

Use the groupFiIes setting in the AWS Glue ET L job to merge small S3 files and rerun AWS Glue E TL jobs.

Answer

Create an AWS Lambda function to merge small S3 files and invoke them periodically. Run the AWS Glue ETL jobs after successful completion of the Lambda function.

Answer

Run the S3DistCp utility in Amazon EMR to merge a large number of small S3 files before running the AWS Glue ETL jobs.

Answer

Update the Kinesis Data Firehose S3 buffer size to 128 MB. Update the buffer interval to 900 seconds.

Question 14

A company uses Amazon Connect to manage its contact center. The company uses Salesforce to manage its customer relationship management (CRM) data. The company must build a pipeline to ingest data from Amazon Connect and Salesforce into a data lake that is built on Amazon S3.Which solution will meet this requirement with the LEAST operational overhead?

Accepted Answer

Use Amazon Kinesis Data Firehose to ingest the Amazon Connect data. Use Amazon Kinesis Data Streams to ingest the Salesforce data.

Answer

Use Amazon Kinesis Data Streams to ingest the Amazon Connect data. Use Amazon AppFlow to ingest the Salesforce data.

Answer

Use Amazon Kinesis Data Firehose to ingest the Amazon Connect data. Use Amazon AppFlow to ingest the Salesforce data.

Answer

Use Amazon AppFlow to ingest the Amazon Connect data. Use Amazon Kinesis Data Firehose to ingest the Salesforce data.

Question 15

A company is designing a data warehouse to support business intelligence reporting. Users will access the executive dashboard heavily each Monday and Friday morning for I hour. These read-only queries will run on the active Amazon Redshift cluster, which runs on dc2.8xIarge compute nodes 24 hours a day, 7 days a week. There are three queues set up in workload management: Dashboard, ETL, and System. The Amazon Redshift cluster needs to process the queries without wait time.What is the MOST cost-effective way to ensure that the cluster processes these queries?

Accepted Answer

Enable concurrency scaling for the Dashboard workload queue.

Answer

Perform a classic resize to place the cluster in read-only mode while adding an additional node to the cluster.

Answer

Enable automatic workload management.

Answer

Perform an elastic resize to add an additional node to the cluster.

Question 16

A marketing company has an application that stores event data in an Amazon RDS database. The company is replicating this data to Amazon Redshift for reporting and business intelligence (BI) purposes. New event data is continuously generated and ingested into the RDS database throughout the day and captured by a change data capture (CDC) replication task in AWS Database Migration Service (AWS DMS). The company requires that the new data be replicated to Amazon Redshift in near-real time.Which solution meets these requirements?

Accepted Answer

Use Amazon Kinesis Data Streams as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Streams and perform an upsert into the Redshift cluster.

Answer

Use Amazon S3 as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.

Answer

Use Amazon DynamoDB as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.

Answer

Use Amazon Kinesis Data Firehose as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Firehose and perform an upsert into the Redshift cluster.

Question 17

A company is creating a data lake by using AWS Lake Formation. The data that will be stored in the data lake contains sensitive customer information and must be encrypted at rest using an AWS Key Management Service (AWS KMS) customer managed key to meet regulatory requirements.How can the company store the data in the data lake to meet these requirements?

Accepted Answer

Store the data in an Amazon S3 bucket by using server-side encryption with AWS KMS (SSE-KMS). Register the S3 location with Lake Formation.

Answer

Store the data in an encrypted Amazon Elastic Block Store (Amazon EBS) volume. Register the Amazon EBS volume with Lake Formation.

Answer

Encrypt the data on the client side and store the encrypted data in an Amazon S3 bucket. Register the S3 location with Lake Formation.

Answer

Store the data in an Amazon S3 Glacier Flexible Retrieval vault bucket. Register the S3 Glacier Flexible Retrieval vault with Lake Formation.

Question 18

An ecommerce company uses Amazon Aurora PostgreSQL to process and store live transactional data and uses Amazon Redshift for its data warehouse solution. A nightly ET L job has been implemented to update the Redshift cluster with new data from the PostgreSQL database. The business has grown rapidly and so has the size and cost of the Redshift cluster. The company's data analytics team needs to create a solution to archive historical data and only keep the most recent 12 months of data in AmazonRedshift to reduce costs. Data analysts should also be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data.Which combination of tasks will meet these requirements? (Select THREE.)

Accepted Answer

Configure the Amazon Redshift Federated Query feature to query live transactional data in the PostgreSQL database.

Accepted Answer

Schedule a monthly job to copy data older than 12 months to Amazon S3 by using the UNLOAD command, and then delete that data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.

Accepted Answer

Create a late-binding view in Amazon Redshift that combines live, current, and historical data from different sources.

Answer

Configure Amazon Redshift Spectrum to query live transactional data in the PostgreSQL database.

Answer

Schedule a monthly job to copy data older than 12 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command, and then delete that data from the Redshift cluster. Configure Redshift Spectrum to access historical data with S3 Glacier Flexible Retrieval.

Answer

Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.

Question 19

A large marketing company needs to store all of its streaming logs and create near-real-time dashboards. The dashboards will be used to help the company make critical business decisions and must be highly available.Which solution meets these requirements?

Accepted Answer

Store the streaming logs in Amazon OpenSearch Service deployed across three Availability Zones and with three dedicated master nodes. Create the dashboards by using OpenSearch Dashboards.

Answer

Store the streaming logs in Amazon S3 with replication to an S3 bucket in a different Availability Zone. Create the dashboards by using Amazon QuickSight.

Answer

Deploy an Amazon Redshift cluster with at least three nodes in a VPC that spans two Availability Zones. Store the streaming logs and use the Redshift cluster as a source to create the dashboards by using Amazon QuickSight.

Answer

Store the streaming logs in Amazon S3 with replication to an S3 bucket in a different Availability Zone. Every time a new log is added in the bucket, invoke an AWS Lambda function to update the dashboards in Amazon QuickSight.

Question 20

A company's system operators and security engineers need to analyze activities within specific date ranges of AWS CloudTrail logs. All log files are stored in an Amazon S3 bucket, and the size of the logs is more than 5 T B. The solution must be cost-effective and maximize query performance.Which solution meets these requirements?

Accepted Answer

Create an AWS Glue job to copy the logs from the S3 source bucket to a new S3 bucket and create a table using Apache Parquet file format, Snappy as compression codec, and partition by date. Use Amazon Athena to query the table and partitions.

Answer

Copy the logs to a new S3 bucket with a prefix structure of <PARTITION COLUMN_NAME>. Use the date column as a partition key. Create a table on Amazon Athena based on the objects in the new bucket. Automatically add metadata partitions by using the MSCK REPAIR TABLE command in Athena. Use Athena to query the table and partitions.

Answer

Create a table on Amazon Athena. Manually add metadata partitions by using the ALTER TABLE ADD PARTITION statement, and use multiple columns for the partition key. Use Athena to query the table and partitions.

Answer

Launch an Amazon EMR cluster and use Amazon S3 as a data store for Apache HBase. Load the logs from the S3 bucket to an HBase table on Amazon EMR. Use Amazon Athena to query the table and partitions.

Question 21

A company has multiple data workflows to ingest data from its operational databases into its data lake on Amazon S3. The workflows use AWS Glue and Amazon EMR for data processing and ETL. The company wants to enhance its architecture to provide automated orchestration and minimize manual intervention Which solution should the company use to manage the data workflows to meet these requirements?

Accepted Answer

AWS Step Functions

Answer

AWS Glue workflows

Answer

AWS Lambda

Answer

AWS Batch

Question 22

A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company's data analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall data.The amount of data that is ingested into Amazon S3 has increased to 5 PB over time. The query latency also has increased. The company needs to segment the data to reduce the amount of data that is scanned.Which solutions will improve query performance? (Select TWO.)

Accepted Answer

Configure Athena to use S3 Select to load only the files of the data subset.

Accepted Answer

Create the data subset in Apache Parquet format each day by using the Athena CREATE TABLE AS SELECT (CTAS) statement. Query the Parquet data.

Answer

Use MySQL Workbench on an Amazon EC2 instance. Connect to Athena by using a JDBC connector. Run the query from MySQL Workbench instead of Athena directly.

Answer

Run a daily AWS Glue ETL job to convert the data files to Apache Parquet format and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data each day.

Answer

Create an S3 gateway endpoint. Configure VPC routing to access Amazon S3 through the gateway endpoint.

Question 23

A business intelligence (Bl) engineer must create a dashboard to visualize how often certain keywords are used in relation to others in social media posts about a public figure. The Bl engineer extracts the keywords from the posts and loads them into an Amazon Redshift table. The table displays the keywords and the count corresponding to each keyword.The Bl engineer needs to display the top keywords with more emphasis on the most frequently used keywords.Which visual type in Amazon QuickSight meets these requirements?

Accepted Answer

Word clouds

Answer

Bar charts

Answer

Circle packing

Answer

Heat maps

Question 24

A company uses Amazon Redshift for its data warehouse. The company is running an ET L process that receives data in data parts from five third-party providers. The data parts contain independent records that are related to one specific job. The company receives the data parts at various times throughout each day.A data analytics specialist must implement a solution that loads the data into Amazon Redshift only after the company receives all five data parts.Which solution will meet these requirements?

Accepted Answer

Create an Amazon Kinesis Data Firehose delivery stream. Program a Python condition that will invoke a buffer flush when all five data parts are received.

Answer

Create an Amazon S3 bucket to receive the data. Use S3 multipart upload to collect the data from the different sources and to form a single object before loading the data into Amazon Redshift.

Answer

Use an AWS Lambda function that is scheduled by cron to load the data into a temporary table in Amazon Redshift. Use Amazon Redshift database triggers to consolidate the final data when all five data parts are ready.

Answer

Create an Amazon S3 bucket to receive the data. Create an AWS Lambda function that is invoked by S3 upload events. Configure the function to validate that all five data parts are gathered before the function loads the data into Amazon Redshift.

Question 25

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 (Pll). 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 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

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 PII columns to one role. Attach a policy without these permissions to the other role.

Answer

Register the S3 locations with AWS Lake Formation. Create an AWS Glue job to create an E TL workflow that removes the Pll columns from the data and creates a separate copy of the data in another data lake S3 bucket. Register the new 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

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

Question 26

A company developed a new voting results reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket. The company is now seeking a solution to perform this infrequent data analysis with data visualization capabilities in a way that requires minimal development effort.Which solution MOST cost-effectively meets these requirements?

Accepted Answer

Use an AWS Glue crawler to create and update a table in the AWS Glue data catalog from the logs. Use Amazon Athena to perform ad-hoc analyses. Develop data visualizations by using Amazon QuickSight.

Answer

Configure Kinesis Data Firehose to deliver the logs to an Amazon OpenSearch Service cluster. Use OpenSearch Service REST APIs to analyze the data. Visualize the data by building an OpenSearch Service dashboard.

Answer

Create an AWS Lambda function to convert the logs to CSV format. Add the Lambda function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform a one-time analysis of the logs by using SQL queries. Develop data visualizations by using Amazon QuickSight.

Answer

Create an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform a one-time analysis of the logs. Develop data visualizations by using Amazon QuickSight.

Question 27

A data analyst notices the following error message while loading data to an Amazon Redshift cluster:'The bucket you are attempting to access must be addressed using the specified endpoint.'What should the data analyst do to resolve this issue?

Accepted Answer

Specify the correct AWS Region for the Amazon S3 bucket by using the REGION option with the COPY command.

Answer

Change the Amazon S3 object's ACL to grant the S3 bucket owner full control of the object.

Answer

Launch the Redshift cluster in a VPC.

Answer

Configure the timeout settings according to the operating system used to connect to the Redshift cluster.

Question 28

A company has a fitness tracker application that generates data from subscribers. The company needs real-time reporting on this data. The data is sent immediately, and the processing latency must be less than 1 second. The company wants to perform anomaly detection on the data as the data is collected. The company also requires a solution that minimizes operational overhead.Which solution meets these requirements?

Accepted Answer

Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics

Answer

Amazon EMR cluster with Apache Spark streaming, Spark SQL, and Spark's machine learning library (MLIib)

Answer

Amazon Kinesis Data Firehose with Amazon S3 and Amazon Athena

Answer

Amazon Kinesis Data Firehose with Amazon QuickSight

Question 29

A large company has several independent business units. Each business unit is responsible for its own data, but needs to share data with other units for collaboration.Each unit stores data in an Amazon S3 data lake created with AWS Lake Formation. To create dashboard reports, the marketing team wants to join its data stored in an Amazon Redshift cluster with the sales team customer table stored in the data lake. The sales team has a large number of tables and schemas, but the marketing team should only have access to the customer table. The solution must be secure and scalable.Which set of actions meets these requirements?

Accepted Answer

The sales team shares the AWS Glue Data Catalog customer table with the marketing team in read-only mode using the Lake Formation tag-based access control (LF-TBAC) method. The sales team updates the AWS Glue Data Catalog resource policy to add relevant permissions for the marketing team. The marketing team creates a resource link to the shared customer table. The marketing team joins its data with the customer table using Amazon Redshift Spectrum.

Answer

The sales team shares the AWS Glue Data Catalog customer table with the marketing team in read-only mode using the named resource method. The marketing team accepts the datashare using AWS Resource Access Manager (AWS RAM) and creates a resource link to the shared customer table. The marketing team joins its data with the customer table using Amazon Redshift Spectrum.

Answer

The marketing team creates an S3 cross-account replication between the sales team's S3 bucket as the source and the marketing team's S3 bucket as the destination. The marketing team runs an AWS Glue crawler on the replicated data in its AWS account to create an AWS Glue Data Catalog customer table.

Answer

The marketing team joins its data with the customer table using Amazon Redshift Spectrum. The marketing team creates an AWS Lambda function in the sales team's account to replicate data between the sale team's S3 bucket as the source and the marketing team's S3 bucket as the destination. The marketing team runs an AWS Glue crawler on the replicated data in its AWS account to create an AWS Glue Data Catalog customer table. The marketing team joins its data with the customer table using Amazon Redshift Spectrum

Question 30

A company collects and transforms data files from third-party providers by using an on-premises SFTP server. The company uses a Python script to transform the dat a.The company wants to reduce the overhead of maintaining the SFTP server and storing large amounts of data on premises. However, the company does not want to change the existing upload process for the third-party providers.Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Use AWS Transfer Family to create an SFTP server that includes a publicly accessible endpoint. Configure the new server to use Amazon S3 storage. Change the server name to match the name of the on-premises SFTP server. Schedule a Python shell job in AWS Glue to use the existing Python script to run periodically and transform the uploaded files.

Answer

Deploy the Python script on an Amazon EC2 instance. Install a third-party SFTP server on the EC2 instance. Schedule the script to run periodically on the EC2 instance to perform a data transformation on new files. Copy the transformed files to Amazon S3.

Answer

Create an Amazon S3 bucket that includes a separate prefix for each provider. Provide the S3 URL to each provider for its respective prefix. Instruct the providers to use the S3 COPY command to upload data. Configure an AWS Lambda function that transforms the data when new files are uploaded.

Answer

Use AWS Transfer Family to create an SFTP server that includes a publicly accessible endpoint. Configure the new server to use Amazon S3 storage. Change the server name to match the name of the on-premises SFTP server. Use AWS Data Pipeline to schedule a transient Amazon EMR cluster with an Apache Spark step to periodically transform the files.

Question 31

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?

Accepted Answer

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.

Answer

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.

Answer

Install Kafka Connect on each application instance and configure each Kafka Connect instance to write to a specific topic only.

Answer

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.

Question 32

A company uses Amazon Redshift as its data warehouse. The Redshift cluster is not encrypted. A data analytics specialist needs to use hardware security module (HSM) managed encryption keys to encrypt the data that is stored in the Redshift cluster.Which combination of steps will meet these requirements? (Select THREE.)

Accepted Answer

Stop all write operations on the source cluster. Unload data from the source cluster.

Accepted Answer

Copy the data to a new target cluster that is encrypted with an HSM from AWS CloudHSM.

Accepted Answer

Rename the source cluster and the target cluster after the migration so that the target cluster is using the original endpoint.

Answer

Copy the data to a new target cluster that is encrypted with AWS Key Management Service (AWS KMS).

Answer

Modify the source cluster by activating AWS CloudHSM encryption. Configure Amazon Redshift to automatically migrate data to a new encrypted cluster.

Answer

Modify the source cluster by activating encryption from an external HSM. Configure Amazon Redshift to automatically migrate data to a new encrypted cluster.

Question 33

An analytics team uses Amazon OpenSearch Service for an analytics API to be used by data analysts. The OpenSearch Service cluster is configured with three master nodes. The analytics team uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and a customized data pipeline to ingest and store 2 months of data in an OpenSearch Service cluster. The cluster stopped responding, which is regularly causing timeout requests. The analytics team discovers the cluster is handling too many bulk indexing requests.Which actions would improve the performance of the OpenSearch Service cluster? (Select TWO.)

Accepted Answer

Reduce the number of API bulk requests on the OpenSearch Service cluster and reduce the size of each bulk request.

Accepted Answer

Scale out the OpenSearch Service cluster by increasing the number of nodes.

Answer

Reduce the number of API bulk requests on the OpenSearch Service cluster, but increase the size of each bulk request.

Answer

Increase the number of master nodes for the OpenSearch Service cluster. Scale down the pipeline component that is used to ingest the data into the OpenSearch Service cluster.

Question 34

A data analytics specialist has a 50 GB data file in .csv format and wants to perform a data transformation task. The data analytics specialist is using the Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to perform the transformation. The resulting output will be used to query the data from Amazon Redshift Spectrum.Which CTAS statement should the data analytics specialist use to provide the MOST efficient performance? Amazon DAS-C01 image Question 194 2608 09162024005525000000

Amazon DAS-C01 image Question 194 2608 09162024005525000000

Accepted Answer

Option B

Answer

Option A

Answer

Option C

Answer

Option D

Question 35

A healthcare company ingests patient data from multiple data sources and stores it in an Amazon S3 staging bucket. An AWS Glue ETL job transforms the data, which is written to an S3-based data lake to be queried using Amazon Athena. The company wants to match patient records even when the records do not have a common unique identifier.Which solution meets this requirement?

Accepted Answer

Train and use the AWS Glue FindMatches ML transform in the ETLjob

Answer

Use Amazon Macie pattern matching as part of the ETLjob

Answer

Train and use the AWS Glue PySpark filter class in the ETLjob

Answer

Partition tables and use the ETL job to partition the data on patient name

Question 36

An online food delivery company wants to optimize its storage costs. The company has been collecting operational data for the last 10 years in a data lake that was built on Amazon S3 by using a Standard storage class. The company does not keep data that is older than 7 years. The data analytics team frequently uses data from the past 6 months for reporting and runs queries on data from the last 2 years about once a month. Data that is more than 2 years old is rarely accessed and is only used for audit purposes.Which combination of solutions will optimize the company's storage costs? (Select TWO.)

Accepted Answer

Create an S3 Lifecycle configuration rule to transition data that is older than 6 months to the S3 Standard-Infrequent Access (S3 Standard-IA) storage class.

Accepted Answer

Create another S3 Lifecycle configuration rule to transition data that is older than 2 years to the S3 Glacier Deep Archive storage class. Create an S3 Lifecycle configuration rule to transition data that is older than 6 months to the S3 One Zone-Infrequent Access (S3 One Zone-IA) storage class.

Answer

Create another S3 Lifecycle configuration rule to transition data that is older than 2 years to the S3 Glacier Flexible Retrieval storage class.

Answer

Use the S3 Intelligent-Tiering storage class to store data instead of the S3 Standard storage class.

Answer

reate an S3 Lifecycle expiration rule to delete data that is older than 7 years.

Answer

Create an S3 Lifecycle configuration rule to transition data that is older than 7 years to the S3 Glacier Deep Archive storage class.

Question 37

A company receives datasets from partners at various frequencies. The datasets include baseline data and incremental data. The company needs to merge and store all the datasets without reprocessing the data.Which solution will meet these requirements with the LEAST development effort?

Accepted Answer

Use an AWS Glue job with job bookmarks enabled to process the datasets. Store the data in Amazon S3.

Answer

Use an AWS Glue job with a temporary table to process the datasets. Store the data in an Amazon RDS table.

Answer

Use an Apache Spark job in an Amazon EMR cluster to process the datasets. Store the data in EMR File System (EMRFS).

Answer

Use an AWS Lambda function to process the datasets. Store the data in Amazon S3.

Question 38

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 service 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? (Select TWO.)

Accepted Answer

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

Accepted Answer

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

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

Deploy Athena within a private subnet.

Question 39

A company has a process that writes two datasets in CSV format to an Amazon S3 bucket every 6 hours. The company needs to join the datasets, convert the data to Apache Parquet, and store the data within another bucket for users to query using Amazon Athena. The data also needs to be loaded to Amazon Redshift for advanced analytics. The company needs a solution that is resilient to the failure of any individual job component and can be restarted in case of an error.Which solution meets these requirements with the LEAST amount of operational overhead?

Accepted Answer

Create an AWS Glue job using PySpark that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift. Use an AWS Glue workflow to orchestrate the AWS Glue job.

Answer

Use AWS Step Functions to orchestrate an Amazon EMR cluster running Apache Spark. Use PySpark to generate data frames of the datasets in Amazon S3, transform the data, join the data, write the data back to Amazon S3, and load the data to Amazon Redshift.

Answer

Create an AWS Glue job using Python Shell that generates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift. Use an AWS Glue workflow to orchestrate the AWS Glue job at the desired frequency.

Answer

Use AWS Step Functions to orchestrate the AWS Glue job. Create an AWS Glue job using Python Shell that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift.

Question 40

An IOT company is collecting data from multiple sensors and is streaming the data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Each sensor type has its own topic, and each topic has the same number of partitions.The company is planning to turn on more sensors. However, the company wants to evaluate which sensor types are producing the most data so that the company can scale accordingly. The company needs to know which sensor types have the largest values for the following metrics: ByteslnPerSec and MessageslnPerSec.Which level of monitoring for Amazon MSK will meet these requirements?

Accepted Answer

PER TOPIC PER BROKER level

Answer

DEFAULT level

Answer

PER BROKER level

Answer

PER TOPIC level

Amazon DAS-C01 Practice Test 5

Amazon DAS-C01 Practice Tests