100%合格、売れ筋最上位DAS-C01試験材料は2023年最新のAmazon練習試験合格させます
AWS Certified Data Analytics問題集でDAS-C01試験完全版問題、試験学習ガイド
AWS認定データ分析 - 専門認定は、データエンジニア、データアナリスト、データ科学者、およびビッグデータを扱い、データ分析にAWSサービスを使用する知識とスキルを検証したいと考えている他のIT専門家に最適です。この認定は、候補者がAWSにデータ分析ソリューションを設計および実装する専門知識を持ち、キャリアを進め、収益の可能性を高めるのに役立つことを示しています。
質問 # 59
An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadat a. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync Which solution will simplify permissions management with minimal development effort?
- A. Use Amazon Cognito user pools.
- B. Manage AWS Glue and S3 permissions by using bucket policies
- C. Use AWS Lake Formation permissions
- D. Set AWS Identity and Access Management (1AM) permissions tor AWS Glue
正解：C
質問 # 60
A data analyst runs a large number of data manipulation language (DML) queries by using Amazon Athena with the JDBC driver. Recently, a query failed after It ran for 30 minutes. The query returned the following message Java.sql.SGLException: Query timeout The data analyst does not immediately need the query results However, the data analyst needs a long-term solution for this problem Which solution will meet these requirements?
- A. In the Service Quotas console, request an increase for the DML query timeout
- B. In the settings for Athena, adjust the DML query timeout limit
- C. Save the tables as compressed .csv files
- D. Split the query into smaller queries to search smaller subsets of data.
正解：D
質問 # 61
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 migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage
- B. 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
- 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 When the audit team requires the older data: migrate the indices in UltraWarm storage back to hot storage
- D. 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
正解：C
質問 # 62
A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster using a separate COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.
Which solution meets these requirements?
- A. Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.
- B. Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.
- C. Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
- D. Use an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
正解：C
質問 # 63
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
正解：B
質問 # 64
A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-real-time data.
Which visualization solution will meet these requirements?
- A. Select Amazon Elasticsearch Service (Amazon ES) as the endpoint for Kinesis Data Firehose. Set up a Kibana dashboard using the data in Amazon ES with the desired analyses and visualizations.
- B. Select Amazon Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.
- C. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Use AWS Glue to catalog the data and Amazon Athena to query it. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.
- D. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.
正解：A
質問 # 65
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. Decrease the number of Amazon ES shards for the index.
- B. Decrease the number of Amazon ES data nodes.
- C. Increase the number of Amazon ES shards for the index.
- D. Increase the memory of the Amazon ES master nodes.
正解：A
解説：
Explanation
https://aws.amazon.com/premiumsupport/knowledge-center/high-jvm-memory-pressure-elasticsearch/
質問 # 66
An online retailer needs to deploy a product sales reporting solution. The source data is exported from an external online transaction processing (OLTP) system for reporting. Roll-up data is calculated each day for the previous day's activities. The reporting system has the following requirements:
Have the daily roll-up data readily available for 1 year.
After 1 year, archive the daily roll-up data for occasional but immediate access.
The source data exports stored in the reporting system must be retained for 5 years. Query access will be needed only for re-evaluation, which may occur within the first 90 days.
Which combination of actions will meet these requirements while keeping storage costs to a minimum?
(Choose two.)
- A. Store the source data initially in the Amazon S3 Glacier storage class. Apply a lifecycle configuration that changes the storage class from Amazon S3 Glacier to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.
- B. Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 1 year after data creation.
- C. Store the daily roll-up data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier 1 year after data creation.
- D. Store the source data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.
- E. Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Standard-Infrequent Access (S3 Standard-IA)
1 year after
data creation.
正解：D、E
質問 # 67
A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?
- A. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
- B. Use a snapshot, restore, and resize operation. Switch to the new target cluster.
- C. Enable concurrency scaling in the workload management (WLM) queue.
- D. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
正解：C
解説：
https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.html
質問 # 68
A retail company leverages Amazon Athena for ad-hoc queries against an AWS Glue Data Catalog. The data analytics team manages the data catalog and data access for the company. The data analytics team wants to separate queries and manage the cost of running those queries by different workloads and teams. Ideally, the data analysts want to group the queries run by different users within a team, store the query results in individual Amazon S3 buckets specific to each team, and enforce cost constraints on the queries run against the Data Catalog.
Which solution meets these requirements?
- A. Create Athena query groups for each team within the company and assign users to the groups.
- B. Create Athena workgroups for each team within the company. Set up IAM workgroup policies that control user access and actions on the workgroup resources.
- C. Create Athena resource groups for each team within the company and assign users to these groups. Add S3 bucket names and other query configurations to the properties list for the resource groups.
- D. Create IAM groups and resource tags for each team within the company. Set up IAM policies that control user access and actions on the Data Catalog resources.
正解：B
解説：
https://aws.amazon.com/about-aws/whats-new/2019/02/athena_workgroups/
質問 # 69
A company is reading data from various customer databases that run on Amazon RDS. The databases contain many inconsistent fields For example, a customer record field that is place_id in one database is location_id in another database. The company wants to link customer records across different databases, even when many customer record fields do not match exactly Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an Amazon EMR cluster to process and analyze data in the databases. Connect to the Apache Zeppelin notebook, and use Apache Spark ML to find duplicate records in the data. Evaluate and tune the model by evaluating performance and results of finding duplicates
- B. Create an AWS Glue crawler to crawl the data in the databases Use Amazon SageMaker to construct Apache Spark ML pipelines to find duplicate records in the data
- C. Create an AWS Glue crawler to crawl the databases. Use the FindMatches transform to find duplicate records in the data Evaluate and tune the transform by evaluating performance and results of finding matches
- D. Create an Amazon EMR cluster to process and analyze data in the databases Connect to the Apache Zeppelin notebook, and use the FindMatches transform to find duplicate records in the data.
正解：C
質問 # 70
An Amazon Redshift database contains sensitive user dat
a. 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 audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.
- B. Enable Amazon Redshift Enhanced VPC Routing. Enable VPC Flow Logs to monitor traffic.
- C. Allow access to the Amazon Redshift database using AWS IAM only. Log access using AWS CloudTrail.
- D. Enable and download audit reports from AWS Artifact.
正解：A
質問 # 71
A data analyst is designing an Amazon QuickSight dashboard using centralized sales data that resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney, Australia, can see only the Australia view and that a salesperson in New York can see only United States (US) data.
What should the data analyst do to ensure the appropriate data security is in place?
- A. Deploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.
- B. Place the data sources for Australia and the US into separate SPICE capacity pools.
- C. Deploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.
- D. Set up an Amazon Redshift VPC security group for Australia and the US.
正解：A
質問 # 72
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 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.
- B. 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.
- C. 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.
- D. 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.
正解：C
解説：
Explanation
https://aws.amazon.com/blogs/aws/new-amazon-kinesis-data-analytics-for-java/
質問 # 73
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. Create an Apache Hive catalog in Amazon EMR with the table schema definition in Amazon S3, and update the table partition with a scheduled job. Migrate the Hive catalog to 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. Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.
- D. Use the AWS Glue API CreateTable operation to create a table in the Data Catalog. Create an AWS Glue crawler and specify the table as the source.
正解：D
解説：
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.
質問 # 74
An online retailer needs to deploy a product sales reporting solution. The source data is exported from an external online transaction processing (OLTP) system for reporting. Roll-up data is calculated each day for the previous day's activities. The reporting system has the following requirements:
Have the daily roll-up data readily available for 1 year.
After 1 year, archive the daily roll-up data for occasional but immediate access.
The source data exports stored in the reporting system must be retained for 5 years. Query access will be needed only for re-evaluation, which may occur within the first 90 days.
Which combination of actions will meet these requirements while keeping storage costs to a minimum? (Choose two.)
- A. Store the source data initially in the Amazon S3 Glacier storage class. Apply a lifecycle configuration that changes the storage class from Amazon S3 Glacier to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.
- B. Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 1 year after data creation.
- C. Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) 1 year after data creation.
- D. Store the daily roll-up data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier 1 year after data creation.
- E. Store the source data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.
正解：C、E
質問 # 75
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. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
- B. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.
Perform the join with AWS Glue ETL scripts. - C. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.
- D. Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.
正解：A
質問 # 76
A gaming company is collecting cllckstream data into multiple Amazon Kinesis data streams. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3 Data scientists use Amazon Athena to query the most recent data and derive business insights. The company wants to reduce its Athena costs without having to recreate the data pipeline. The company prefers a solution that will require less management effort Which set of actions can the data scientists take immediately to reduce costs?
- A. Change the Kinesis Data Firehose output format to Apache Parquet Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size For the existing data, run an AWS Glue ETL job to combine and convert small JSON files to large Parquet files and add the YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.
- B. Integrate an AWS Lambda function with Kinesis Data Firehose to convert source records to Apache Parquet and write them to Amazon S3 In parallel, run an AWS Glue ETL job to combine and convert existing JSON files to large Parquet files Create a custom S3 object YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.
- C. Create an Apache Spark Job that combines and converts JSON files to Apache Parquet files Launch an Amazon EMR ephemeral cluster daily to run the Spark job to create new Parquet files in a different S3 location Use ALTER TABLE SET LOCATION to reflect the new S3 location on the existing Athena table.
- D. Create a Kinesis data stream as a delivery target for Kinesis Data Firehose Run Apache Flink on Amazon Kinesis Data Analytics on the stream to read the streaming data, aggregate ikand save it to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table
正解：B
質問 # 77
An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities. Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an application running on Amazon EC2 processes the data and makes search options and reports available for visualization by editors and marketers. The company wants to make website clicks and aggregated data available to editors and marketers in minutes to enable them to connect with users more effectively.
Which options will help meet these requirements in the MOST efficient way? (Choose two.)
- A. Use Amazon Elasticsearch Service deployed on Amazon EC2 to aggregate, filter, and process the data.
Refresh content performance dashboards in near-real time. - B. Use Kibana to aggregate, filter, and visualize the data stored in Amazon Elasticsearch Service. Refresh content performance dashboards in near-real time.
- C. Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon Elasticsearch Service.
- D. Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon Elasticsearch Service.
- E. Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon Elasticsearch Service from Amazon S3.
正解：B、C
質問 # 78
A team of data scientists plans to analyze market trend data for their company's new investment strategy. The trend data comes from five different data sources in large volumes. The team wants to utilize Amazon Kinesis to support their use case. The team uses SQL-like queries to analyze trends and wants to send notifications based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to Amazon S3 for archival and historical re-processing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution.
Which solution meets these requirements?
- A. Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
- B. Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the first stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS.
Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket. - C. Publish data to two Kinesis data streams. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
- D. Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS.
Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
正解：D
質問 # 79
A company has a business unit uploading .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.
Which solution will update the Redshift table without duplicates when jobs are rerun?
- A. Load the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.
- B. Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.
- C. Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.
- D. Modify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.
正解：A
質問 # 80
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. The consumer is not processing the parent shard completely before processing the child shards after a stream resize. The data analyst should process the parent shard completely first before processing the child shards.
- B. The hash key generation process for the records is not working correctly. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.
- C. There are multiple shards in a stream and order needs to be maintained in the shard. The data analyst needs to make sure there is only a single shard in the stream and no stream resize runs.
- D. The records are not being received by Kinesis Data Streams in order. The producer should use the PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering parameter.
正解：A
解説：
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.
質問 # 81
A company with a video streaming website wants to analyze user behavior to make recommendations to users in real time Clickstream data is being sent to Amazon Kinesis Data Streams and reference data is stored in Amazon S3 The company wants a solution that can use standard SQL quenes The solution must also provide a way to look up pre-calculated reference data while making recommendations Which solution meets these requirements?
- A. Use AWS Glue streaming and Scale to process incoming data from Kinesis Data Streams Use the AWS Glue connector to write data to Amazon Redshift
- B. Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use a data stream to write results to Amazon Redshift
- C. Use an AWS Glue Python shell job to process incoming data from Kinesis Data Streams Use the Boto3 library to write data to Amazon Redshift
- D. Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use an Amazon Kinesis Data Firehose delivery stream to write results to Amazon Redshift
正解：D
質問 # 82
A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined that, at an expected ingestion rate of about 2 TB per day, the cluster will be undersized in less than 4 months. A long-term solution is needed. The data analyst has indicated that most queries only reference the most recent 13 months of data, yet there are also quarterly reports that need to query all the data generated from the past 7 years. The chief technology officer (CTO) is concerned about the costs, administrative effort, and performance of a long-term solution.
Which solution should the data analyst use to meet these requirements?
- A. Execute a CREATE TABLE AS SELECT (CTAS) statement to move records that are older than 13 months to quarterly partitioned data in Amazon Redshift Spectrum backed by Amazon S3.
- B. Unload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tiering. Use AWS Glue to crawl the S3 bucket location to create external tables in an AWS Glue Data Catalog.
Create an Amazon EMR cluster using Auto Scaling for any daily analytics needs, and use Amazon Athena for the quarterly reports, with both using the same AWS Glue Data Catalog. - C. Take a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.
- D. Create a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and delete those records from Amazon Redshift. Create an external table in Amazon Redshift to point to the S3 location. Use Amazon Redshift Spectrum to join to data that is older than 13 months.
正解：D
質問 # 83
......
この試験では、データの収集、ストレージ、処理、データ分析、視覚化、レポートなど、さまざまなトピックについて説明します。候補者は、Amazon S3、Amazon Redshift、AWS Glue、Amazon Kinesis、Amazon QuicksightなどのAWSサービスを深く理解することが期待されています。さらに、候補者は、データセキュリティ、データ品質、およびデータガバナンスにベストプラクティスを適用できるはずです。
正真正銘のベスト試験材料DAS-C01オンライン練習試験：https://www.jpntest.com/shiken/DAS-C01-mondaishu
DAS-C01テストエンジン練習試験：https://drive.google.com/open?id=15SGKhVkIPzgoBEJBWrfpZRt4ugVg6Bxn