NEW QUESTION 1
A power utility company is deploying thousands of smart meters to obtain real-time updates about power consumption. The company is using Amazon Kinesis Data Streams to collect the data streams from smart meters. The consumer application uses the Kinesis Client Library (KCL) to retrieve the stream data. The company has only one consumer application.
The company observes an average of 1 second of latency from the moment that a record is written to the stream until the record is read by a consumer application. The company must reduce this latency to 500 milliseconds.
Which solution meets these requirements?

• A. Use enhanced fan-out in Kinesis Data Streams.
• B. Increase the number of shards for the Kinesis data stream.
• C. Reduce the propagation delay by overriding the KCL default settings.
• D. Develop consumers by using Amazon Kinesis Data Firehose.

Answer: C

Explanation:
The KCL defaults are set to follow the best practice of polling every 1 second. This default results in average propagation delays that are typically below 1 second.

NEW QUESTION 2
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?

• A. Create a different Amazon EC2 security group for each applicatio
• B. Configure each security group to have access to a specific topic in the Amazon MSK cluste
• C. Attach the security group to each application based on the topic that the applications should read and write to.
• D. Install Kafka Connect on each application instance and configure each Kafka Connect instance to write to a specific topic only.
• E. Use Kafka ACLs and configure read and write permissions for each topi
• F. Use the distinguished name of the clients’ TLS certificates as the principal of the ACL.
• G. Create a different Amazon EC2 security group for each applicatio
• H. Create an Amazon MSK cluster and Kafka topic for each applicatio
• I. Configure each security group to have access to the specific cluster.

Answer: B

NEW QUESTION 3
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?

• A. Ingest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon EMR cluste
• B. Store the curated data in Amazon S3 for ML processing.
• C. Create custom ETL jobs on-premises to curate the dat
• D. Use AWS DMS to ingest data into Amazon S3 for ML processing.
• E. Ingest data into Amazon S3 using AWS DM
• F. Use AWS Glue to perform data curation and store the data in Amazon S3 for ML processing.
• G. Take a full backup of the data store and ship the backup files using AWS Snowbal
• H. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.

Answer: C

NEW QUESTION 4
A marketing company is using Amazon EMR clusters for its workloads. The company manually installs third party libraries on the clusters by logging in to the master nodes. A data analyst needs to create an automated solution to replace the manual process.
Which options can fulfill these requirements? (Choose two.)

• A. Place the required installation scripts in Amazon S3 and execute them using custom bootstrap actions.
• B. Place the required installation scripts in Amazon S3 and execute them through Apache Spark in Amazon EMR.
• C. Install the required third-party libraries in the existing EMR master nod
• D. Create an AMI out of that master node and use that custom AMI to re-create the EMR cluster.
• E. Use an Amazon DynamoDB table to store the list of required application
• F. Trigger an AWS Lambda function with DynamoDB Streams to install the software.
• G. Launch an Amazon EC2 instance with Amazon Linux and install the required third-party libraries on the instanc
• H. Create an AMI and use that AMI to create the EMR cluster.

Answer: AE

Explanation:
https://aws.amazon.com/about-aws/whats-new/2017/07/amazon-emr-now-supports-launching-clusters-with-cust https://docs.aws.amazon.com/de_de/emr/latest/ManagementGuide/emr-plan-bootstrap.html

NEW QUESTION 5
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.)

• A. Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.
• B. Use an S3 bucket in the same account as Athena.
• C. Compress the objects to reduce the data transfer I/O.
• D. Use an S3 bucket in the same Region as Athena.
• E. Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.
• F. Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.

Answer: CDF

Explanation:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

NEW QUESTION 6
A data analyst is using AWS Glue to organize, cleanse, validate, and format a 200 GB dataset. The data analyst triggered the job to run with the Standard worker type. After 3 hours, the AWS Glue job status is still RUNNING. Logs from the job run show no error codes. The data analyst wants to improve the job execution time without overprovisioning.
Which actions should the data analyst take?

• A. Enable job bookmarks in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the executor-cores job parameter.
• B. Enable job metrics in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the maximum capacity job parameter.
• C. Enable job metrics in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the spark.yarn.executor.memoryOverhead job parameter.
• D. Enable job bookmarks in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the num-executors job parameter.

Answer: B

NEW QUESTION 7
A transport company wants to track vehicular movements by capturing geolocation records. The records are 10 B in size and up to 10,000 records are captured each second. Data transmission delays of a few minutes are acceptable, considering unreliable network conditions. The transport company decided to use Amazon Kinesis Data Streams to ingest the data. The company is looking for a reliable mechanism to send data to Kinesis Data Streams while maximizing the throughput efficiency of the Kinesis shards.
Which solution will meet the company’s requirements?

• A. Kinesis Agent
• B. Kinesis Producer Library (KPL)
• C. Kinesis Data Firehose
• D. Kinesis SDK

Answer: B

NEW QUESTION 8
A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in both Regions. The solution should be as low-cost as possible.
What should the company do to achieve this goal?

• A. Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena queries in us-west-2.
• B. Run the AWS Glue crawler in us-west-2 to catalog datasets in all Region
• C. Once the data is crawled, run Athena queries in us-west-2.
• D. Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog in us-west-2 and run Athena queries.
• E. Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2.Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2.

Answer: B

NEW QUESTION 9
A large retailer has successfully migrated to an Amazon S3 data lake architecture. The company’s marketing team is using Amazon Redshift and Amazon QuickSight to analyze data, and derive and visualize insights. To ensure the marketing team has the most up-to-date actionable information, a data analyst implements nightly refreshes of Amazon Redshift using terabytes of updates from the previous day.
After the first nightly refresh, users report that half of the most popular dashboards that had been running correctly before the refresh are now running much slower. Amazon CloudWatch does not show any alerts.
What is the MOST likely cause for the performance degradation?

• A. The dashboards are suffering from inefficient SQL queries.
• B. The cluster is undersized for the queries being run by the dashboards.
• C. The nightly data refreshes are causing a lingering transaction that cannot be automatically closed by Amazon Redshift due to ongoing user workloads.
• D. The nightly data refreshes left the dashboard tables in need of a vacuum operation that could not be automatically performed by Amazon Redshift due to ongoing user workloads.

Answer: D

Explanation:
https://github.com/awsdocs/amazon-redshift-developer-guide/issues/21

NEW QUESTION 10
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?

• A. Create and manage encryption keys using AWS CloudHSM Classi
• B. Launch an Amazon Redshift cluster in a VPC with the option to use CloudHSM Classic for key management.
• C. Create a VPC and establish a VPN connection between the VPC and the on-premises networ
• D. Create an HSM connection and client certificate for the on-premises HS
• E. Launch a cluster in the VPC with the option to use the on-premises HSM to store keys.
• F. Create an HSM connection and client certificate for the on-premises HS
• G. Enable HSM encryption on the existing unencrypted cluster by modifying the cluste
• H. Connect to the VPC where the Amazon Redshift cluster resides from the on-premises network using a VPN.
• I. Create a replica of the on-premises HSM in AWS CloudHS
• J. Launch a cluster in a VPC with the option to use CloudHSM to store keys.

Answer: B

NEW QUESTION 11
A company has 1 million scanned documents stored as image files in Amazon S3. The documents contain typewritten application forms with information including the applicant first name, applicant last name, application date, application type, and application text. The company has developed a machine learning algorithm to extract the metadata values from the scanned documents. The company wants to allow internal data analysts to analyze and find applications using the applicant name, application date, or application text. The original images should also be downloadable. Cost control is secondary to query performance.
Which solution organizes the images and metadata to drive insights while meeting the requirements?

• A. For each image, use object tags to add the metadat
• B. Use Amazon S3 Select to retrieve the files based on the applicant name and application date.
• C. Index the metadata and the Amazon S3 location of the image file in Amazon Elasticsearch Service.Allow the data analysts to use Kibana to submit queries to the Elasticsearch cluster.
• D. Store the metadata and the Amazon S3 location of the image file in an Amazon Redshift tabl
• E. Allow the data analysts to run ad-hoc queries on the table.
• F. Store the metadata and the Amazon S3 location of the image files in an Apache Parquet file in Amazon S3, and define a table in the AWS Glue Data Catalo
• G. Allow data analysts to use Amazon Athena to submit custom queries.

Answer: B

Explanation:
https://aws.amazon.com/blogs/machine-learning/automatically-extract-text-and-structured-data-from-documents

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

• A. Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data forma
• B. Use Athena to query the processed datase
• C. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creatio
• D. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.
• E. Use an AWS Glue ETL job to partition and convert the data into a row-based data forma
• F. Use Athena to query the processed datase
• G. Configure a lifecycle policy to move the data into the Amazon S3 Standard- Infrequent Access (S3 Standard-IA) storage class 5 years after object creatio
• H. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.
• I. Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data forma
• J. Use Athena to query the processed datase
• K. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accesse
• L. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier forlong-term archival 7 days after the last date the object was accessed.
• M. Use an AWS Glue ETL job to partition and convert the data into a row-based data forma
• N. Use Athena to query the processed datase
• O. Configure a lifecycle policy to move the data into the Amazon S3 Standard- Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accesse
• P. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after the last date the object was accessed.

Answer: A

NEW QUESTION 13
A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion events every day.
How should this data be stored for optimal performance?

• A. In Apache ORC partitioned by date and sorted by source IP
• B. In compressed .csv partitioned by date and sorted by source IP
• C. In Apache Parquet partitioned by source IP and sorted by date
• D. In compressed nested JSON partitioned by source IP and sorted by date

Answer: A

NEW QUESTION 14
A company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and need consistent performance. Some of the data is queried more frequently than the rest. The company wants a solution that meets its performance requirements in a cost-effective manner.
Which solution meets these requirements?

• A. Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon S3. Use Amazon Athena to perform SQL queries over the ingested data.
• B. Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon Redshift.Enable Amazon Redshift workload management (WLM) to prioritize workloads.
• C. Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon Redshif
• D. Enable Amazon Redshift workload management (WLM) to prioritize workloads.
• E. Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon S3. Load frequently queried data to Amazon Redshift using the COPY comman
• F. Use Amazon Redshift Spectrum for less frequently queried data.

Answer: B

NEW QUESTION 15
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 ad-hoc 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?

• A. Concurrency scaling
• B. Short query acceleration (SQA)
• C. Workload management (WLM)
• D. Materialized views

Answer: D

Explanation:

Materialized views:

NEW QUESTION 16
A data engineering team within a shared workspace company wants to build a centralized logging system for all weblogs generated by the space reservation system. The company has a fleet of Amazon EC2 instances that process requests for shared space reservations on its website. The data engineering team wants to ingest all weblogs into a service that will provide a near-real-time search engine. The team does not want to manage the maintenance and operation of the logging system.
Which solution allows the data engineering team to efficiently set up the web logging system within AWS?

• A. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatc
• B. Choose Amazon Elasticsearch Service as the end destination of the weblogs.
• C. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Data Firehose delivery stream to CloudWatc
• D. Choose Amazon Elasticsearch Service as the end destination of the weblogs.
• E. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatc
• F. Configure Splunk as the end destination of the weblogs.
• G. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Firehose delivery stream to CloudWatc
• H. Configure Amazon DynamoDB as the end destinationof the weblog

Answer: B

Explanation:
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CWL_ES_Stream.html

NEW QUESTION 17
A company wants to run analytics on its Elastic Load Balancing logs stored in Amazon S3. A data analyst needs to be able to query all data from a desired year, month, or day. The data analyst should also be able to query a subset of the columns. The company requires minimal operational overhead and the most
cost-effective solution.
Which approach meets these requirements for optimizing and querying the log data?

• A. Use an AWS Glue job nightly to transform new log files into .csv format and partition by year, month, and da
• B. Use AWS Glue crawlers to detect new partition
• C. Use Amazon Athena to query data.
• D. Launch a long-running Amazon EMR cluster that continuously transforms new log files from Amazon S3 into its Hadoop Distributed File System (HDFS) storage and partitions by year, month, and da
• E. Use Apache Presto to query the optimized format.
• F. Launch a transient Amazon EMR cluster nightly to transform new log files into Apache ORC format and partition by year, month, and da
• G. Use Amazon Redshift Spectrum to query the data.
• H. Use an AWS Glue job nightly to transform new log files into Apache Parquet format and partition by year, month, and da
• I. Use AWS Glue crawlers to detect new partition
• J. Use Amazon Athena to querydata.

Answer: C

NEW QUESTION 18
A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of three dense storage nodes. Due to a recent business acquisition, the company needs to load an additional 4 TB of user data into Amazon Redshift. The engineering team will combine all the user data and apply complex calculations that require I/O intensive resources. The company needs to adjust the cluster's capacity to support the change in analytical and storage requirements.
Which solution meets these requirements?

• A. Resize the cluster using elastic resize with dense compute nodes.
• B. Resize the cluster using classic resize with dense compute nodes.
• C. Resize the cluster using elastic resize with dense storage nodes.
• D. Resize the cluster using classic resize with dense storage nodes.

Answer: C

NEW QUESTION 19
A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.
How should the data analyst resolve the issue?

• A. Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.
• B. Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.
• C. Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.
• D. Edit the permissions for the new S3 bucket from within the S3 console.

Answer: B

NEW QUESTION 20
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