NEW QUESTION 1
A large company receives files from external parties in Amazon EC2 throughout the day. At the end of the day, the files are combined into a single file, compressed into a gzip file, and uploaded to Amazon S3. The total size of all the files is close to 100 GB daily. Once the files are uploaded to Amazon S3, an AWS Batch program executes a COPY command to load the files into an Amazon Redshift cluster.
Which program modification will accelerate the COPY process?

• A. Upload the individual files to Amazon S3 and run the COPY command as soon as the files become available.
• B. Split the number of files so they are equal to a multiple of the number of slices in the Amazon Redshift cluste
• C. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
• D. Split the number of files so they are equal to a multiple of the number of compute nodes in the Amazon Redshift cluste
• E. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
• F. Apply sharding by breaking up the files so the distkey columns with the same values go to the same file.Gzip and upload the sharded files to Amazon S3. Run the COPY command on the files.

Answer: B

NEW QUESTION 2
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 3
A company uses Amazon Elasticsearch Service (Amazon ES) to store and analyze its website clickstream data. The company ingests 1 TB of data daily using Amazon Kinesis Data Firehose and stores one day’s worth of data in an Amazon ES cluster.
The company has very slow query performance on the Amazon ES index and occasionally sees errors from Kinesis Data Firehose when attempting to write to the index. The Amazon ES cluster has 10 nodes running a single index and 3 dedicated master nodes. Each data node has 1.5 TB of Amazon EBS storage attached and the cluster is configured with 1,000 shards. Occasionally, JVMMemoryPressure errors are found in the cluster logs.
Which solution will improve the performance of Amazon ES?

• A. Increase the memory of the Amazon ES master nodes.
• B. Decrease the number of Amazon ES data nodes.
• C. Decrease the number of Amazon ES shards for the index.
• D. Increase the number of Amazon ES shards for the index.

Answer: C

Explanation:
https://aws.amazon.com/premiumsupport/knowledge-center/high-jvm-memory-pressure-elasticsearch/

NEW QUESTION 4
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 5
A company wants to improve user satisfaction for its smart home system by adding more features to its recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesis Data Streams using the Kinesis Producer Library (KPL) in Java. Statistics from a set of failed sensors showed that, when a sensor is malfunctioning, its recorded data is not always sent to the cloud.
The company needs a solution that offers near-real-time analytics on the data from the most updated sensors. Which solution enables the company to meet these requirements?

• A. Set the RecordMaxBufferedTime property of the KPL to "1" to disable the buffering on the sensor side.Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL scrip
• B. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON fil
• C. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.
• D. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Jav
• E. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL scrip
• F. Direct the output of KDA application to a Kinesis Data Firehose delivery stream, enable the data transformation feature to flatten the JSON file, and set the Kinesis Data Firehose destination to an Amazon Elasticsearch Service cluster.
• G. Set the RecordMaxBufferedTime property of the KPL to "0" to disable the buffering on the sensor side.Connect for each stream a dedicated Kinesis Data Firehose delivery stream and enable the data transformation feature to flatten the JSON file before sending it to an Amazon S3 bucke
• H. Load the S3 data into an Amazon Redshift cluster.
• I. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API withthe AWS SDK for Jav
• J. Use AWS Glue to fetch and process data from the stream using the Kinesis Client Library (KCL). Instantiate an Amazon Elasticsearch Service cluster and use AWS Lambda to directly push data into it.

Answer: B

Explanation:
https://docs.aws.amazon.com/streams/latest/dev/developing-producers-with-kpl.html
The KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly.

NEW QUESTION 6
A company has developed an Apache Hive script to batch process data stared in Amazon S3. The script needs to run once every day and store the output in Amazon S3. The company tested the script, and it completes within 30 minutes on a small local three-node cluster.
Which solution is the MOST cost-effective for scheduling and executing the script?

• A. Create an AWS Lambda function to spin up an Amazon EMR cluster with a Hive execution ste
• B. Set KeepJobFlowAliveWhenNoSteps to false and disable the termination protection fla
• C. Use Amazon CloudWatch Events to schedule the Lambda function to run daily.
• D. Use the AWS Management Console to spin up an Amazon EMR cluster with Python Hu
• E. Hive, and Apache Oozi
• F. Set the termination protection flag to true and use Spot Instances for the core nodes of the cluste
• G. Configure an Oozie workflow in the cluster to invoke the Hive script daily.
• H. Create an AWS Glue job with the Hive script to perform the batch operatio
• I. Configure the job to run once a day using a time-based schedule.
• J. Use AWS Lambda layers and load the Hive runtime to AWS Lambda and copy the Hive script.Schedule the Lambda function to run daily by creating a workflow using AWS Step Functions.

Answer: C

NEW QUESTION 7
A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.
Which approach can provide the visuals that senior leadership requested with the least amount of effort?

• A. Use Amazon QuickSight with Amazon Athena as the data sourc
• B. Use heat maps as the visual type.
• C. Use Amazon QuickSight with Amazon S3 as the data sourc
• D. Use heat maps as the visual type.
• E. Use Amazon QuickSight with Amazon Athena as the data sourc
• F. Use pivot tables as the visual type.
• G. Use Amazon QuickSight with Amazon S3 as the data sourc
• H. Use pivot tables as the visual type.

Answer: A

NEW QUESTION 8
A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort.
Which solution meets these requirements?

• A. Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.
• B. Use Apache Spark Streaming on Amazon EMR to read the data in near-real tim
• C. Develop a custom application for the dashboard by using D3.js.
• D. Use Amazon Kinesis Data Firehose to push the data into an Amazon Elasticsearch Service (Amazon ES) cluste
• E. Visualize the data by using a Kibana dashboard.
• F. Use AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Answer: B

NEW QUESTION 9
A company owns facilities with IoT devices installed across the world. The company is using Amazon Kinesis Data Streams to stream data from the devices to Amazon S3. The company's operations team wants to get insights from the IoT data to monitor data quality at ingestion. The insights need to be derived in near-real time, and the output must be logged to Amazon DynamoDB for further analysis.
Which solution meets these requirements?

• A. Connect Amazon Kinesis Data Analytics to analyze the stream dat
• B. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.
• C. Connect Amazon Kinesis Data Analytics to analyze the stream dat
• D. Save the output to DynamoDB by using an AWS Lambda function.
• E. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function.Save the output to DynamoDB by using the default output from Kinesis Data Firehose.
• F. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function.Save the data to Amazon S3. Then run an AWS Glue job on schedule to ingest the data into DynamoDB.

Answer: C

NEW QUESTION 10
Three teams of data analysts use Apache Hive on an Amazon EMR cluster with the EMR File System (EMRFS) to query data stored within each teams Amazon S3 bucket. The EMR cluster has Kerberos enabled and is configured to authenticate users from the corporate Active Directory. The data is highly sensitive, so access must be limited to the members of each team.
Which steps will satisfy the security requirements?

• A. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3.Create three additional IAM roles, each granting access to each team’s specific bucke
• B. Add the additional IAM roles to the cluster’s EMR role for the EC2 trust polic
• C. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• D. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3.Create three additional IAM roles, each granting access to each team's specific bucke
• E. Add the service role for the EMR cluster EC2 instances to the trust policies for the additional IAM role
• F. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• G. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3.Create three additional IAM roles, each granting access to each team’s specific bucke
• H. Add the service role for the EMR cluster EC2 instances to the trust polices for the additional IAM role
• I. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• J. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3.Create three additional IAM roles, each granting access to each team's specific bucke
• K. Add the service role for the EMR cluster EC2 instances to the trust polices for the base IAM role
• L. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.

Answer: C

NEW QUESTION 11
A real estate company has a mission-critical application using Apache HBase in Amazon EMR. Amazon EMR is configured with a single master node. The company has over 5 TB of data stored on an Hadoop Distributed File System (HDFS). The company wants a cost-effective solution to make its HBase data highly available.
Which architectural pattern meets company’s requirements?

• A. Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node.Configurethe EMR cluster with multiple master node
• B. Schedule automated snapshots using AmazonEventBridge.
• C. Store the data on an EMR File System (EMRFS) instead of HDF
• D. Enable EMRFS consistent view.Create an EMR HBase cluster with multiple master node
• E. Point the HBase root directory to an Amazon S3 bucket.
• F. Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.Run two separate EMR clusters in two different Availability Zone
• G. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
• H. Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.Create a primary EMR HBase cluster with multiple master node
• I. Create a secondary EMR HBase read- replica cluster in a separate Availability Zon
• J. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.

Answer: D

NEW QUESTION 12
A healthcare company uses AWS data and analytics tools to collect, ingest, and store electronic health record (EHR) data about its patients. The raw EHR data is stored in Amazon S3 in JSON format partitioned by hour, day, and year and is updated every hour. The company wants to maintain the data catalog and metadata in an AWS Glue Data Catalog to be able to access the data using Amazon Athena or Amazon Redshift Spectrum for analytics.
When defining tables in the Data Catalog, the company has the following requirements:
Choose the catalog table name and do not rely on the catalog table naming algorithm. Keep the table updated with new partitions loaded in the respective S3 bucket prefixes.
Which solution meets these requirements with minimal effort?

• A. Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.
• B. Use the AWS Glue console to manually create a table in the Data Catalog and schedule an AWS Lambda function to update the table partitions hourly.
• C. Use the AWS Glue API CreateTable operation to create a table in the Data Catalo
• D. Create an AWS Glue crawler and specify the table as the source.
• E. Create an Apache Hive catalog in Amazon EMR with the table schema definition in Amazon S3, and update the table partition with a scheduled jo
• F. Migrate the Hive catalog to the Data Catalog.

Answer: C

Explanation:
Updating Manually Created Data Catalog Tables Using Crawlers: To do this, when you define a crawler, instead of specifying one or more data stores as the source of a crawl, you specify one or more existing Data Catalog tables. The crawler then crawls the data stores specified by the catalog tables. In this case, no new tables are created; instead, your manually created tables are updated.

NEW QUESTION 13
An IoT company wants to release a new device that will collect data to track sleep overnight on an intelligent mattress. Sensors will send data that will be uploaded to an Amazon S3 bucket. About 2 MB of data is generated each night for each bed. Data must be processed and summarized for each user, and the results need to be available as soon as possible. Part of the process consists of time windowing and other functions. Based on tests with a Python script, every run will require about 1 GB of memory and will complete within a couple of minutes.
Which solution will run the script in the MOST cost-effective way?

• A. AWS Lambda with a Python script
• B. AWS Glue with a Scala job
• C. Amazon EMR with an Apache Spark script
• D. AWS Glue with a PySpark job

Answer: A

NEW QUESTION 14
A company wants to collect and process events data from different departments in near-real time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB.
How should a data analytics specialist design the solution for data ingestion?

• A. Use Amazon Kinesis Data Stream
• B. Configure a stream for the raw dat
• C. Use a Kinesis Agent to write data to the strea
• D. Create an Amazon Kinesis Data Analytics application that reads data from the raw stream, cleanses it, and stores the output to Amazon S3.
• E. Use Amazon Kinesis Data Firehos
• F. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansin
• G. Use a Kinesis Agent to write data to the delivery strea
• H. Configure Kinesis Data Firehose to deliver the data to Amazon S3.
• I. Use Amazon Managed Streaming for Apache Kafk
• J. Configure a topic for the raw dat
• K. Use a Kafka producer to write data to the topi
• L. Create an application on Amazon EC2 that reads data from the topic by using the Apache Kafka consumer API, cleanses the data, and writes to Amazon S3.
• M. Use Amazon Simple Queue Service (Amazon SQS). Configure an AWS Lambda function to read events from the SQS queue and upload the events to Amazon S3.

Answer: B

NEW QUESTION 15
An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: “Command Failed with Exit Code 1.”
Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90–95% soon after. The average memory usage across all executors continues to be less than 4%.
The data engineer also notices the following error while examining the related Amazon CloudWatch Logs. What should the data engineer do to solve the failure in the MOST cost-effective way?

• A. Change the worker type from Standard to G.2X.
• B. Modify the AWS Glue ETL code to use the ‘groupFiles’: ‘inPartition’ feature.
• C. Increase the fetch size setting by using AWS Glue dynamics frame.
• D. Modify maximum capacity to increase the total maximum data processing units (DPUs) used.

Answer: B

Explanation:
https://docs.aws.amazon.com/glue/latest/dg/monitor-profile-debug-oom-abnormalities.html#monitor-debug-oom

NEW QUESTION 16
An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.
Which solution meets these requirements?

• A. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.Perform the join with AWS Glue ETL scripts.
• B. Export the call center data from Amazon Redshift using a Python shell in AWS Glu
• C. Perform the join with AWS Glue ETL scripts.
• D. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
• E. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoo
• F. Perform the join with Apache Hive.

Answer: C

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/c-spectrum-external-tables.html

NEW QUESTION 17
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?

• A. Create instance fleet configurations for core and task node
• B. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metri
• C. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.
• D. Create instance fleet configurations for core and task node
• E. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metri
• F. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric.
• G. Create instance group configurations for core and task node
• H. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metri
• I. Create anautomatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.
• J. Create instance group configurations for core and task node
• K. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metri
• L. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric.

Answer: D

Explanation:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-instances-guidelines.html

NEW QUESTION 18
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?

• A. Upload the data from the on-premises PostgreSQL database to Amazon S3 by using a customized batch upload proces
• B. 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 forma
• C. Use Amazon Athena to query the data.
• D. Create an Amazon RDS for PostgreSQL database and use AWS Database Migration Service (AWS DMS) to migrate the data into Amazon RD
• E. Use AWS Data Pipeline to copy and enrich the data from the Amazon RDS for PostgreSQL table and move the data to Amazon S3. Use Amazon Athena to querythe data.
• F. Configure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises databas
• G. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet forma
• H. Create an Amazon Redshift cluster and use Amazon Redshift Spectrum to query the data.
• I. Configure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises databas
• J. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet forma
• K. Use Amazon Athena to query the data.

Answer: B

NEW QUESTION 19
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 20
A company stores its sales and marketing data that includes personally identifiable information (PII) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run analytics reports with the data. To meet compliance requirements, the company must ensure the data is not publicly accessible throughout this process. A data engineer has secured Amazon S3 but must ensure the individual EMR clusters created by the analysts are not exposed to the public internet.
Which solution should the data engineer to meet this compliance requirement with LEAST amount of effort?

• A. Create an EMR security configuration and ensure the security configuration is associated with the EMR clusters when they are created.
• B. Check the security group of the EMR clusters regularly to ensure it does not allow inbound traffic from IPv4 0.0.0.0/0 or IPv6 ::/0.
• C. Enable the block public access setting for Amazon EMR at the account level before any EMR cluster is created.
• D. Use AWS WAF to block public internet access to the EMR clusters across the board.

Answer: C

Explanation:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-block-public-access.html

NEW QUESTION 21
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