NEW QUESTION 1
An online retail company is migrating its reporting system to AWS. The company’s legacy system runs data processing on online transactions using a complex series of nested Apache Hive queries. Transactional data is exported from the online system to the reporting system several times a day. Schemas in the files are stable between updates.
A data analyst wants to quickly migrate the data processing to AWS, so any code changes should be minimized. To keep storage costs low, the data analyst decides to store the data in Amazon S3. It is vital that the data from the reports and associated analytics is completely up to date based on the data in Amazon S3.
Which solution meets these requirements?

• A. Create an AWS Glue Data Catalog to manage the Hive metadat
• B. Create an AWS Glue crawler over Amazon S3 that runs when data is refreshed to ensure that data changes are update
• C. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
• D. Create an AWS Glue Data Catalog to manage the Hive metadat
• E. Create an Amazon EMR cluster with consistent view enable
• F. Run emrfs sync before each analytics step to ensure data changes are update
• G. Create an EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
• H. Create an Amazon Athena table with CREATE TABLE AS SELECT (CTAS) to ensure data is refreshed from underlying queries against the raw datase
• I. Create an AWS Glue Data Catalog to manage the Hive metadata over the CTAS tabl
• J. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
• K. Use an S3 Select query to ensure that the data is properly update
• L. Create an AWS Glue Data Catalog to manage the Hive metadata over the S3 Select tabl
• M. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

Answer: A

NEW QUESTION 2
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 3
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 4
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 5
Once a month, a company receives a 100 MB .csv file compressed with gzip. The file contains 50,000 property listing records and is stored in Amazon S3 Glacier. The company needs its data analyst to query a subset of the data for a specific vendor.
What is the most cost-effective solution?

• A. Load the data into Amazon S3 and query it with Amazon S3 Select.
• B. Query the data from Amazon S3 Glacier directly with Amazon Glacier Select.
• C. Load the data to Amazon S3 and query it with Amazon Athena.
• D. Load the data to Amazon S3 and query it with Amazon Redshift Spectrum.

Answer: A

NEW QUESTION 6
A data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection. Users will join data stored in Amazon S3 in Apache ORC format with data stored in Amazon Elasticsearch Service (Amazon ES) and Amazon Aurora MySQL.
Which solution will provide the MOST up-to-date results?

• A. Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon Athena.
• B. Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshif
• C. Query the data with Amazon Redshift.
• D. Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.
• E. Query all the datasets in place with Apache Presto running on Amazon EMR.

Answer: C

NEW QUESTION 7
A company launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service.
The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture.
Which actions should the data analyst take to resolve this issue? (Choose two.)

• A. Increase the Kinesis Data Streams retention period to reduce throttling.
• B. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.
• C. Increase the number of shards in the stream using the UpdateShardCount API.
• D. Choose partition keys in a way that results in a uniform record distribution across shards.
• E. Customize the application code to include retry logic to improve performance.

Answer: CD

Explanation:
https://aws.amazon.com/blogs/big-data/under-the-hood-scaling-your-kinesis-data-streams/

NEW QUESTION 8
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?

• A. Enable Amazon Redshift Enhanced VPC Routin
• B. Enable VPC Flow Logs to monitor traffic.
• C. Allow access to the Amazon Redshift database using AWS IAM onl
• D. Log access using AWS CloudTrail.
• E. Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.
• F. Enable and download audit reports from AWS Artifact.

Answer: C

NEW QUESTION 9
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 10
A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the data analyst notices that, sometimes, the messages arrive out of order for account_id. Upon further investigation, the data analyst discovers the messages that are out of order seem to be arriving from different shards for the same account_id and are seen when a stream resize runs.
What is an explanation for this behavior and what is the solution?

• A. There are multiple shards in a stream and order needs to be maintained in the shar
• B. The data analyst needs to make sure there is only a single shard in the stream and no stream resize runs.
• C. The hash key generation process for the records is not working correctl
• D. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.
• E. The records are not being received by Kinesis Data Streams in orde
• F. The producer should use the PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering parameter.
• G. The consumer is not processing the parent shard completely before processing the child shards after a stream resiz
• H. The data analyst should process the parent shard completely first before processing the child shards.

Answer: D

Explanation:
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-after-resharding.html the parent shards that remain after the reshard could still contain data that you haven't read yet that was added to the stream before the reshard. If you read data from the child shards before having read all data from the parent shards, you could read data for a particular hash key out of the order given by the data records' sequence numbers. Therefore, assuming that the order of the data is important, you should, after a reshard, always continue to read data from the parent shards until it is exhausted. Only then should you begin reading data from the child shards.

NEW QUESTION 11
An education provider’s learning management system (LMS) is hosted in a 100 TB data lake that is built on Amazon S3. The provider’s LMS supports hundreds of schools. The provider wants to build an advanced analytics reporting platform using Amazon Redshift to handle complex queries with optimal performance. System users will query the most recent 4 months of data 95% of the time while 5% of the queries will leverage data from the previous 12 months.
Which solution meets these requirements in the MOST cost-effective way?

• A. Store the most recent 4 months of data in the Amazon Redshift cluste
• B. Use Amazon Redshift Spectrum to query data in the data lak
• C. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacier storage.
• D. Leverage DS2 nodes for the Amazon Redshift cluste
• E. Migrate all data from Amazon S3 to Amazon Redshif
• F. Decommission the data lake.
• G. Store the most recent 4 months of data in the Amazon Redshift cluste
• H. Use Amazon Redshift Spectrum to query data in the data lak
• I. Ensure the S3 Standard storage class is in use with objects in the data lake.
• J. Store the most recent 4 months of data in the Amazon Redshift cluste
• K. Use Amazon Redshift federated queries to join cluster data with the data lake to reduce cost
• L. Ensure the S3 Standard storage class is in use with objects in the data lake.

Answer: C

NEW QUESTION 12
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 13
A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units.
Which steps should a data analyst take to accomplish this task efficiently and securely?

• A. Create an AWS Lambda function to process the DynamoDB strea
• B. Decrypt the sensitive data using the same KMS ke
• C. Save the output to a restricted S3 bucket for the finance tea
• D. Create a finance table in Amazon Redshift that is accessible to the finance team onl
• E. Use the COPY command to load the data from Amazon S3 to the finance table.
• F. Create an AWS Lambda function to process the DynamoDB strea
• G. Save the output to a restricted S3 bucket for the finance tea
• H. Create a finance table in Amazon Redshift that is accessible to the finance team onl
• I. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.
• J. Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key.Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshif
• K. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.
• L. Create an Amazon EMR cluste
• M. Create Apache Hive tables that reference the data stored inDynamoD
• N. Insert the output to the restricted Amazon S3 bucket for the finance tea
• O. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.

Answer: B

NEW QUESTION 14
A company is building a service to monitor fleets of vehicles. The company collects IoT data from a device in each vehicle and loads the data into Amazon Redshift in near-real time. Fleet owners upload .csv files containing vehicle reference data into Amazon S3 at different times throughout the day. A nightly process loads the vehicle reference data from Amazon S3 into Amazon Redshift. The company joins the IoT data from the device and the vehicle reference data to power reporting and dashboards. Fleet owners are frustrated by waiting a day for the dashboards to update.
Which solution would provide the SHORTEST delay between uploading reference data to Amazon S3 and the change showing up in the owners’ dashboards?

• A. Use S3 event notifications to trigger an AWS Lambda function to copy the vehicle reference data into Amazon Redshift immediately when the reference data is uploaded to Amazon S3.
• B. Create and schedule an AWS Glue Spark job to run every 5 minute
• C. The job inserts reference data into Amazon Redshift.
• D. Send reference data to Amazon Kinesis Data Stream
• E. Configure the Kinesis data stream to directly load the reference data into Amazon Redshift in real time.
• F. Send the reference data to an Amazon Kinesis Data Firehose delivery strea
• G. Configure Kinesis with a buffer interval of 60 seconds and to directly load the data into Amazon Redshift.

Answer: A

NEW QUESTION 15
A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex real-world scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values.
The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the LEAST amount of management overhead.
Which solution will meet these requirements?

• A. Use an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.
• B. Use Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business metrics.
• C. Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data.
• D. Use calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.

Answer: A

NEW QUESTION 16
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 17
A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental data. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only.
Which solution achieves these required access patterns to minimize costs and administrative tasks?

• A. Consolidate all AWS accounts into one accoun
• B. Create different S3 buckets for each department and move all the data from every account to the central data lake accoun
• C. Migrate the individual data catalogs into a central data catalog and apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.
• D. Keep the account structure and the individual AWS Glue catalogs on each accoun
• E. Add a central data lake account and use AWS Glue to catalog data from various account
• F. Configure cross-account access for AWS Glue crawlers to scan the data in each departmental S3 bucket to identify the schema and populate the catalo
• G. Add the senior data analysts into the central account and apply highly detailed access controls in the Data Catalog and Amazon S3.
• H. Set up an individual AWS account for the central data lak
• I. Use AWS Lake Formation to catalog the cross- account location
• J. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked rol
• K. Use Lake Formation permissions to addfine-grained access controls to allow senior analysts to view specific tables and columns.
• L. Set up an individual AWS account for the central data lake and configure a central S3 bucke
• M. Use an AWS Lake Formation blueprint to move the data from the various buckets into the central S3 bucke
• N. On each individual bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked rol
• O. Use Lake Formation permissions to add fine-grained access controls for both associate and senior analysts to view specific tables and columns.

Answer: C

Explanation:
Lake Formation provides secure and granular access to data through a new grant/revoke permissions model that augments AWS Identity and Access Management (IAM) policies. Analysts and data scientists can use the full portfolio of AWS analytics and machine learning services, such as Amazon Athena, to access the data. The configured Lake Formation security policies help ensure that users can access only the data that they are authorized to access. Source : https://docs.aws.amazon.com/lake-formation/latest/dg/how-it-works.html

NEW QUESTION 18
A company is planning to create a data lake in Amazon S3. The company wants to create tiered storage based on access patterns and cost objectives. The solution must include support for JDBC connections from legacy clients, metadata management that allows federation for access control, and batch-based ETL using PySpark and Scala. Operational management should be limited.
Which combination of components can meet these requirements? (Choose three.)

• A. AWS Glue Data Catalog for metadata management
• B. Amazon EMR with Apache Spark for ETL
• C. AWS Glue for Scala-based ETL
• D. Amazon EMR with Apache Hive for JDBC clients
• E. Amazon Athena for querying data in Amazon S3 using JDBC drivers
• F. Amazon EMR with Apache Hive, using an Amazon RDS with MySQL-compatible backed metastore

Answer: BEF

NEW QUESTION 19
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 20
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