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100%無料DAS-C01試験問題集リアルAWS Certified Data Analytics問題集159解答を掴み取れ！ [Q63-Q88]

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100%無料DAS-C01試験問題集リアルAWS Certified Data Analytics問題集159解答を掴み取れ！

あなたを余裕でDAS-C01試験合格させます！100%試験高合格率保証 [2023]

質問 # 63
A company is migrating from an on-premises Apache Hadoop cluster to an Amazon EMR cluster. The cluster runs only during business hours. Due to a company requirement to avoid intraday cluster failures, the EMR cluster must be highly available. When the cluster is terminated at the end of each business day, the data must persist.
Which configurations would enable the EMR cluster to meet these requirements? (Choose three.)

  • A. AWS Glue Data Catalog as the metastore for Apache Hive
  • B. Hadoop Distributed File System (HDFS) for storage
  • C. Multiple master nodes in multiple Availability Zones
  • D. MySQL database on the master node as the metastore for Apache Hive
  • E. Multiple master nodes in a single Availability Zone
  • F. EMR File System (EMRFS) for storage

正解：A、E、F

解説：
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-ha.html "Note : The cluster can reside only in one Availability Zone or subnet."


質問 # 64
A company is building a data lake and needs to ingest data from a relational database that has time-series data.
The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3.
What is the MOST cost-effective approach to meet these requirements?

  • A. Use AWS Glue to connect to the data source using JDBC Drivers. Ingest incremental records only using job bookmarks.
  • B. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the entire dataset. Use appropriate Apache Spark libraries to compare the dataset, and find the delta.
  • C. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the full data. Use AWS DataSync to ensure the delta only is written into Amazon S3.
  • D. Use AWS Glue to connect to the data source using JDBC Drivers. Store the last updated key in an Amazon DynamoDB table and ingest the data using the updated key as a filter.

正解：A

解説：
Explanation
https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html


質問 # 65
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 to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.
  • C. Export the call center data from Amazon Redshift using a Python shell in AWS Glue. 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.

正解：D


質問 # 66
An online retailer is rebuilding its inventory management system and inventory reordering system to automatically reorder products by using Amazon Kinesis Data Streams. The inventory management system uses the Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Kinesis Client Library (KCL) to consume data from the stream. The stream has been configured to scale as needed. Just before production deployment, the retailer discovers that the inventory reordering system is receiving duplicated data.
Which factors could be causing the duplicated data? (Choose two.)

  • A. There was a change in the number of shards, record processors, or both.
  • B. The max_records configuration property was set to a number that is too high.
  • C. The producer has a network-related timeout.
  • D. The stream's value for the IteratorAgeMilliseconds metric is too high.
  • E. The AggregationEnabled configuration property was set to true.

正解：D、E


質問 # 67
A company developed a new elections 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 low-cost option to perform this infrequent data analysis with visualizations of logs in a way that requires minimal development effort.
Which solution meets these requirements?

  • A. Create a second Kinesis Data Firehose delivery stream to deliver the log files to Amazon Elasticsearch Service (Amazon ES). Use Amazon ES to perform text-based searches of the logs for ad-hoc analyses and use Kibana for data visualizations.
  • B. Create an AWS Lambda function to convert the logs into .csv format. Then add the function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.
  • C. Create an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.
  • D. Use an AWS Glue crawler to create and update a table in the Glue data catalog from the logs. Use Athena to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

正解：D

解説：
Explanation
https://aws.amazon.com/blogs/big-data/analyzing-aws-waf-logs-with-amazon-es-amazon-athena-and-amazon-qu


質問 # 68
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 Jav a. 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. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. 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.
  • B. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. 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.
  • C. 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 script. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON file. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.
  • D. 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 bucket. Load the S3 data into an Amazon Redshift cluster.

正解：B

解説：
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.


質問 # 69
A media content company has a streaming playback application. The company wants to collect and analyze the data to provide near-real-time feedback on playback issues. The company needs to consume this data and return results within 30 seconds according to the service-level agreement (SLA). The company needs the consumer to identify playback issues, such as quality during a specified timeframe. The data will be emitted as JSON and may change schemas over time.
Which solution will allow the company to collect data for processing while meeting these requirements?

  • A. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to trigger an event for AWS Lambda to process. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.
  • B. Send the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.
  • C. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event trigger an AWS Lambda function to process the data. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon S3.
  • D. Send the data to Amazon Kinesis Data Streams and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon S3.

正解：D

解説：
https://aws.amazon.com/blogs/aws/new-amazon-kinesis-data-analytics-for-java/


質問 # 70
A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient's protected health information (PHI) from the streaming data and store the data in durable storage.
Which solution meets these requirements with the least operational overhead?

  • A. Ingest the data using Amazon Kinesis Data Streams to write the data to Amazon S3. Have the data stream launch an AWS Lambda function that parses the sensor data and removes all PHI in Amazon S3.
  • B. Ingest the data using Amazon Kinesis Data Streams, which invokes an AWS Lambda function using Kinesis Client Library (KCL) to remove all PHI. Write the data in Amazon S3.
  • C. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Have Amazon S3 trigger an AWS Lambda function that parses the sensor data to remove all PHI in Amazon S3.
  • D. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Implement a transformation AWS Lambda function that parses the sensor data to remove all PHI.

正解：A


質問 # 71
A US-based sneaker retail company launched its global website. All the transaction data is stored in Amazon RDS and curated historic transaction data is stored in Amazon Redshift in the us-east-1 Region. The business intelligence (BI) team wants to enhance the user experience by providing a dashboard for sneaker trends.
The BI team decides to use Amazon QuickSight to render the website dashboards. During development, a team in Japan provisioned Amazon QuickSight in ap-northeast-1. The team is having difficulty connecting Amazon QuickSight from ap-northeast-1 to Amazon Redshift in us-east-1.
Which solution will solve this issue and meet the requirements?

  • A. Create a VPC endpoint from the Amazon QuickSight VPC to the Amazon Redshift VPC so Amazon QuickSight can access data from Amazon Redshift.
  • B. In the Amazon Redshift console, choose to configure cross-Region snapshots and set the destination Region as ap-northeast-1. Restore the Amazon Redshift Cluster from the snapshot and connect to Amazon QuickSight launched in ap-northeast-1.
  • C. Create an Amazon Redshift endpoint connection string with Region information in the string and use this connection string in Amazon QuickSight to connect to Amazon Redshift.
  • D. Create a new security group for Amazon Redshift in us-east-1 with an inbound rule authorizing access from the appropriate IP address range for the Amazon QuickSight servers in ap-northeast-1.

正解：A


質問 # 72
A company is building a data lake and needs to ingest data from a relational database that has time-series dat a. The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3.
What is the MOST cost-effective approach to meet these requirements?

  • A. Use AWS Glue to connect to the data source using JDBC Drivers. Ingest incremental records only using job bookmarks.
  • B. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the entire dataset. Use appropriate Apache Spark libraries to compare the dataset, and find the delta.
  • C. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the full data. Use AWS DataSync to ensure the delta only is written into Amazon S3.
  • D. Use AWS Glue to connect to the data source using JDBC Drivers. Store the last updated key in an Amazon DynamoDB table and ingest the data using the updated key as a filter.

正解：A

解説：
https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html


質問 # 73
A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.
Which solution meets these requirements?

  • A. Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet files. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.
  • B. Use AWS Glue to convert the files from .csv to a single large Apache ORC file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
  • C. Use AWS Glue to convert all the files from .csv to a single large Apache Parquet file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
  • D. Use Amazon EMR to convert each .csv file to Apache Avro. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.

正解：A


質問 # 74
A streaming application is reading data from Amazon Kinesis Data Streams and immediately writing the data to an Amazon S3 bucket every 10 seconds. The application is reading data from hundreds of shards. The batch interval cannot be changed due to a separate requirement. The data is being accessed by Amazon Athena.
Users are seeing degradation in query performance as time progresses.
Which action can help improve query performance?

  • A. Increase the number of shards in Kinesis Data Streams.
  • B. Merge the files in Amazon S3 to form larger files.
  • C. Add more memory and CPU capacity to the streaming application.
  • D. Write the files to multiple S3 buckets.

正解：C


質問 # 75
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. Store the metadata and the Amazon S3 location of the image file in an Amazon Redshift table. Allow the data analysts to run ad-hoc queries on the table.
  • B. 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 Catalog. Allow data analysts to use Amazon Athena to submit custom queries.
  • C. For each image, use object tags to add the metadata. Use Amazon S3 Select to retrieve the files based on the applicant name and application date.
  • D. 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.

正解：C


質問 # 76
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 to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.
  • C. Export the call center data from Amazon Redshift using a Python shell in AWS Glue. 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.

正解：D

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


質問 # 77
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. Choose partition keys in a way that results in a uniform record distribution across shards.
  • 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. Increase the Kinesis Data Streams retention period to reduce throttling.
  • E. Customize the application code to include retry logic to improve performance.

正解：A、C

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


質問 # 78
A company receives data from its vendor in JSON format with a timestamp in the file name. The vendor uploads the data to an Amazon S3 bucket, and the data is registered into the company's data lake for analysis and reporting. The company has configured an S3 Lifecycle policy to archive all files to S3 Glacier after 5 days.
The company wants to ensure that its AWS Glue crawler catalogs data only from S3 Standard storage and ignores the archived files. A data analytics specialist must implement a solution to achieve this goal without changing the current S3 bucket configuration.
Which solution meets these requirements?

  • A. Use the exclude patterns feature of AWS Glue to identify the S3 Glacier files for the crawler to exclude.
  • B. Schedule an automation job that uses AWS Lambda to move files from the original S3 bucket to a new S3 bucket for S3 Glacier storage.
  • C. Use the include patterns feature of AWS Glue to identify the S3 Standard files for the crawler to include.
  • D. Use the excludeStorageClasses property in the AWS Glue Data Catalog table to exclude files on S3 Glacier storage

正解：A


質問 # 79
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 compressed nested JSON partitioned by source IP and sorted by date
  • B. In compressed .csv partitioned by date and sorted by source IP
  • C. In Apache ORC partitioned by date and sorted by source IP
  • D. In Apache Parquet partitioned by source IP and sorted by date

正解：A


質問 # 80
A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide.
The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company.
The data engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step Functions for process orchestration, and Amazon CloudWatch for job scheduling.
More testing facilities were recently added, and the time to process files is increasing.
What will MOST efficiently decrease the data processing time?

  • A. Use AWS Lambda to group the small files into larger files. Write the files back to Amazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables.
  • B. Use the Amazon Redshift COPY command to move the files from Amazon S3 into Amazon Redshift tables directly. Process the files in Amazon Redshift.
  • C. Use Amazon EMR instead of AWS Glue to group the small input files. Process the files in Amazon EMR and load them into Amazon Redshift tables.
  • D. Use the AWS Glue dynamic frame file grouping option while ingesting the raw input files. Process the files and load them into Amazon Redshift tables.

正解：A


質問 # 81
A company is building an analytical solution that includes Amazon S3 as data lake storage and Amazon Redshift for data warehousing. The company wants to use Amazon Redshift Spectrum to query the data that is stored in Amazon S3.
Which steps should the company take to improve performance when the company uses Amazon Redshift Spectrum to query the S3 data files? (Select THREE ) Use gzip compression with individual file sizes of 1-5 GB

  • A. Use file formats that are not splittable
  • B. Keep all files about the same size.
  • C. Split the data into KB-sized files.
  • D. Use a columnar storage file format
  • E. Partition the data based on the most common query predicates

正解：B、C、E


質問 # 82
An ecommerce company is migrating its business intelligence environment from on premises to the AWS Cloud. The company will use Amazon Redshift in a public subnet and Amazon QuickSight. The tables already are loaded into Amazon Redshift and can be accessed by a SQL tool.
The company starts QuickSight for the first time. During the creation of the data source, a data analytics specialist enters all the information and tries to validate the connection. An error with the following message occurs: "Creating a connection to your data source timed out." How should the data analytics specialist resolve this error?

  • A. Grant the SELECT permission on Amazon Redshift tables.
  • B. Use a QuickSight admin user for creating the dataset.
  • C. Add the QuickSight IP address range into the Amazon Redshift security group.
  • D. Create an IAM role for QuickSight to access Amazon Redshift.

正解：A

解説：
Explanation
Connection to the database times out
Your client connection to the database appears to hang or time out when running long queries, such as a COPY command. In this case, you might observe that the Amazon Redshift console displays that the query has completed, but the client tool itself still appears to be running the query. The results of the query might be missing or incomplete depending on when the connection stopped.


質問 # 83
A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users.
The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB.
Which configuration will provide the MOST cost-effective solution that meets these requirements?

  • A. Use QuickSight Enterprise edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source and import the data into SPICE. Automatically refresh every 24 hours.
  • B. Use QuickSight Standard edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source with a direct query option.
  • C. Load the data into an Amazon Redshift cluster by using the COPY command. Configure 50 author users and 1,000 reader users. Use QuickSight Enterprise edition. Configure an Amazon Redshift data source with a direct query option.
  • D. Use QuickSight Enterprise edition. Configure 1 administrator and 1,000 reader users. Configure an S3 data source and import the data into SPICE. Automatically refresh every 24 hours.

正解：A


質問 # 84
A company has a marketing department and a finance department. The departments are storing data in Amazon S3 in their own AWS accounts in AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their data. The departments have some databases and tables that share common names.
The marketing department needs to securely access some tables from the finance department.
Which two steps are required for this process? (Choose two.)

  • A. The finance department creates cross-account IAM permissions to the table for the marketing department role.
  • B. The marketing department creates an IAM role that has permissions to the Lake Formation tables.
  • C. The finance department grants Lake Formation permissions for the tables to the external account for the marketing department.

正解：A、C

解説：
Explanation
Granting Lake Formation Permissions
Creating an IAM role (AWS CLI)


質問 # 85
A retail company stores order invoices in an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster Indices on the cluster are created monthly Once a new month begins, no new writes are made to any of the indices from the previous months The company has been expanding the storage on the Amazon OpenSearch Service {Amazon Elasticsearch Service) cluster to avoid running out of space, but the company wants to reduce costs Most searches on the cluster are on the most recent 3 months of data while the audit team requires infrequent access to older data to generate periodic reports The most recent 3 months of data must be quickly available for queries, but the audit team can tolerate slower queries if the solution saves on cluster costs Which of the following is the MOST operationally efficient solution to meet these requirements?

  • A. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to store the indices in Amazon S3 Glacier When the audit team requires the archived data restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster
  • B. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage When the audit team requires the older data: migrate the indices in UltraWarm storage back to hot storage
  • C. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage
  • D. Archive indices that are older than 3 months by taking manual snapshots and storing the snapshots in Amazon S3 When the audit team requires the archived data, restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster

正解：B


質問 # 86
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. Choose partition keys in a way that results in a uniform record distribution across shards.
  • 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. Increase the Kinesis Data Streams retention period to reduce throttling.
  • E. Customize the application code to include retry logic to improve performance.

正解：A、C

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


質問 # 87
A company has collected more than 100 TB of log files in the last 24 months. The files are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log file was initially created. A table was created in Amazon Athena that points to the S3 bucket. One-time queries are run against a subset of columns in the table several times an hour.
A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.
Which combination of steps should the data analyst take to meet these requirements? (Choose three.)

  • A. Add a key prefix of the form date=year-month-day/ to the S3 objects to partition the data.
  • B. Convert the log files to Apache Parquet format.
  • C. Add a key prefix of the form year-month-day/ to the S3 objects to partition the data.
  • D. Drop and recreate the table with the PARTITIONED BY clause. Run the ALTER TABLE ADD PARTITION statement.
  • E. Drop and recreate the table with the PARTITIONED BY clause. Run the MSCK REPAIR TABLE statement.
  • F. Convert the log files to Apace Avro format.

正解：A、B、E


質問 # 88
......

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