DAS-C01のPDF問題集リアル2023最近更新された問題
リリースAmazon DAS-C01更新された問題PDF
AWS Certified Data Analytics - Specialty Examは、データの収集、ストレージ、処理、分析、視覚化など、幅広いトピックをカバーしています。この試験は、Amazon Kinesis、Amazon Redshift、Amazon EMR、Amazon Athena、Amazon QuickSight、AWS Glueなどのデータ分析のためのAWSサービスやツールに関する知識も候補者のテストを行います。この試験に合格することは、候補者がAWSプラットフォーム上のデータ分析に深い理解を持ち、戦略的なビジネスの意思決定にデータを活用する組織に価値ある洞察を提供できることを示します。
AWS Certified Data Analytics - Specialty（DAS-C01）試験は、Amazon Web Services（AWS）が提供する認定試験で、データプロフェッショナルのスキルと知識を評価することに焦点を当てています。試験は、AWSサービスを使用してデータ分析を行う候補者の能力、およびデータ入力と出力を理解して最適化する能力をテストするために設計されています。
質問 # 91
A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.
Which approach would allow the developers to solve the issue with minimal coding effort?
- A. Enable job bookmarks on the AWS Glue jobs.
- B. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.
- C. Create custom logic on the ETL jobs to track the processed S3 objects.
- D. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
正解：B
質問 # 92
A data engineering team within a shared workspace company wants to build a centralized logging system for all weblogs generated by the space reservation system. The company has a fleet of Amazon EC2 instances that process requests for shared space reservations on its website. The data engineering team wants to ingest all weblogs into a service that will provide a near-real-time search engine. The team does not want to manage the maintenance and operation of the logging system.
Which solution allows the data engineering team to efficiently set up the web logging system within AWS?
- A. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatch. Configure Splunk as the end destination of the weblogs.
- B. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Firehose delivery stream to CloudWatch. Configure Amazon DynamoDB as the end destination of the weblogs.
- C. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatch. Choose Amazon Elasticsearch Service as the end destination of the weblogs.
- D. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Data Firehose delivery stream to CloudWatch. Choose Amazon Elasticsearch Service as the end destination of the weblogs.
正解：D
解説：
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CWL_ES_Stream.html
質問 # 93
A company's marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:
* The data size is approximately 32 TB uncompressed.
* There is a low volume of single-row inserts each day.
* There is a high volume of aggregation queries each day.
* Multiple complex joins are performed.
* The queries typically involve a small subset of the columns in a table.
Which storage service will provide the MOST performant solution?
- A. Amazon Redshift
- B. Amazon Elasticsearch
- C. Amazon Aurora MySQL
- D. Amazon Neptune
正解：A
質問 # 94
A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in both Regions. The solution should be as low-cost as possible.
What should the company do to achieve this goal?
- A. Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2.
Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2. - B. Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena queries in us-west-2.
- C. Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog in us-west-2 and run Athena queries.
- D. Run the AWS Glue crawler in us-west-2 to catalog datasets in all Regions. Once the data is crawled, run Athena queries in us-west-2.
正解：D
質問 # 95
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. 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.
- 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. 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.
正解：C
質問 # 96
A company is migrating its existing on-premises ETL jobs to Amazon EMR. The code consists of a series of jobs written in Java. The company needs to reduce overhead for the system administrators without changing the underlying code. Due to the sensitivity of the data, compliance requires that the company use root device volume encryption on all nodes in the cluster. Corporate standards require that environments be provisioned though AWS CloudFormation when possible.
Which solution satisfies these requirements?
- A. Install open-source Hadoop on Amazon EC2 instances with encrypted root device volumes. Configure the cluster in the CloudFormation template.
- B. Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a bootstrap action to encrypt the root device volume of every node.
- C. Create a custom AMI with encrypted root device volumes. Configure Amazon EMR to use the custom AMI using the CustomAmild property in the CloudFormation template.
- D. Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a bootstrap action to enable TLS.
正解：C
質問 # 97
A marketing company is storing its campaign response data in Amazon S3. A consistent set of sources has generated the data for each campaign. The data is saved into Amazon S3 as .csv files. A business analyst will use Amazon Athena to analyze each campaign's data. The company needs the cost of ongoing data analysis with Athena to be minimized.
Which combination of actions should a data analytics specialist take to meet these requirements? (Choose two.)
- A. Partition the data by source.
- B. Compress the .csv files.
- C. Convert the .csv files to Apache Parquet.
- D. Convert the .csv files to Apache Avro.
- E. Partition the data by campaign.
正解：C、E
解説：
Explanation
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/
質問 # 98
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. Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.
- C. 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.
- D. 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.
正解：C
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 # 99
A retail company has 15 stores across 6 cities in the United States. Once a month, the sales team requests a visualization in Amazon QuickSight that provides the ability to easily identify revenue trends across cities and stores. The visualization also helps identify outliers that need to be examined with further analysis.
Which visual type in QuickSight meets the sales team's requirements?
- A. Geospatial chart
- B. Heat map
- C. Line chart
- D. Tree map
正解：A
質問 # 100
A media company has been performing analytics on log data generated by its applications. There has been a recent increase in the number of concurrent analytics jobs running, and the overall performance of existing jobs is decreasing as the number of new jobs is increasing. The partitioned data is stored in Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) and the analytic processing is performed on Amazon EMR clusters using the EMR File System (EMRFS) with consistent view enabled. A data analyst has determined that it is taking longer for the EMR task nodes to list objects in Amazon S3.
Which action would MOST likely increase the performance of accessing log data in Amazon S3?
- A. Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.
- B. Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.
- C. Redeploy the EMR clusters that are running slowly to a different Availability Zone.
- D. Use a hash function to create a random string and add that to the beginning of the object prefixes when storing the log data in Amazon S3.
正解：C
質問 # 101
A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables.
A trips fact table for information on completed rides. A drivers dimension table for driver profiles.
A customers fact table holding customer profile information.
The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes.
What table design provides optimal query performance?
- A. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.
- B. Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
- C. Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.
- D. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
正解：D
解説：
https://www.matillion.com/resources/blog/aws-redshift-performance-choosing-the-right-distribution-styles/#:~:text=The%20distribution%20style%20is%20how,you%20want%20to%20distribute%20it%E2%80%A6 https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-best-dist-key.html
質問 # 102
A marketing company collects clickstream data The company sends the data to Amazon Kinesis Data Firehose and stores the data in Amazon S3 The company wants to build a series of dashboards that will be used by hundreds of users across different departments The company will use Amazon QuickSight to develop these dashboards The company has limited resources and wants a solution that could scale and provide daily updates about clickstream activity Which combination of options will provide the MOST cost-effective solution? (Select TWO )
- A. Use Amazon Redshift to store and query the clickstream data
- B. Use Amazon Athena to query the clickstream data in Amazon S3
- C. Use QuickSight with a direct SQL query
- D. Use S3 analytics to query the clickstream data
- E. Use the QuickSight SPICE engine with a daily refresh
正解：C、D
質問 # 103
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. 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.
- C. 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.
- D. 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.
正解：A
質問 # 104
A company uses Amazon Redshift as its data warehouse. A new table has columns that contain sensitive data.
The data in the table will eventually be referenced by several existing queries that run many times a day.
A data analyst needs to load 100 billion rows of data into the new table. Before doing so, the data analyst must ensure that only members of the auditing group can read the columns containing sensitive data.
How can the data analyst meet these requirements with the lowest maintenance overhead?
- A. Load all the data into the new table and grant all users read-only permissions to non-sensitive columns.
Attach an IAM policy to the auditing group with explicit ALLOW access to the sensitive data columns. - B. Load all the data into the new table and grant the auditing group permission to read from the table. Use the GRANT SQL command to allow read-only access to a subset of columns to the appropriate users.
- C. Load all the data into the new table and grant the auditing group permission to read from the table.
Create a view of the new table that contains all the columns, except for those considered sensitive, and grant the appropriate users read-only permissions to the table. - D. Load all the data into the new table and grant the auditing group permission to read from the table. Load all the data except for the columns containing sensitive data into a second table. Grant the appropriate users read-only permissions to the second table.
正解：B
解説：
Explanation
https://aws.amazon.com/blogs/big-data/achieve-finer-grained-data-security-with-column-level-access-control-in-
質問 # 105
A company is sending historical datasets to Amazon S3 for storage. A data engineer at the company wants to make these datasets available for analysis using Amazon Athen a. The engineer also wants to encrypt the Athena query results in an S3 results location by using AWS solutions for encryption. The requirements for encrypting the query results are as follows:
Use custom keys for encryption of the primary dataset query results.
Use generic encryption for all other query results.
Provide an audit trail for the primary dataset queries that shows when the keys were used and by whom.
Which solution meets these requirements?
- A. Use client-side encryption with AWS Key Management Service (AWS KMS) customer managed keys for the primary dataset. Use S3 client-side encryption with client-side keys for the other datasets.
- B. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the primary dataset. Use SSE-S3 for the other datasets.
- C. Use server-side encryption with AWS KMS managed customer master keys (SSE-KMS CMKs) for the primary dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.
- D. Use server-side encryption with customer-provided encryption keys (SSE-C) for the primary dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.
正解：B
質問 # 106
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 cluster. Use Amazon Redshift federated queries to join cluster data with the data lake to reduce costs. Ensure the S3 Standard storage class is in use with objects in the data lake.
- B. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacier storage.
- C. Leverage DS2 nodes for the Amazon Redshift cluster. Migrate all data from Amazon S3 to Amazon Redshift. Decommission the data lake.
- D. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Ensure the S3 Standard storage class is in use with objects in the data lake.
正解：D
質問 # 107
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. Store the data on an EMR File System (EMRFS) instead of HDFS. Enable EMRFS consistent view.
Create an EMR HBase cluster with multiple master nodes. Point the HBase root directory to an Amazon S3 bucket. - B. 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 nodes. Create a secondary EMR HBase read- replica cluster in a separate Availability Zone. Point both clusters to the same HBase root directory in the same Amazon S3 bucket. - C. Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node.
Configure
the EMR cluster with multiple master nodes. Schedule automated snapshots using Amazon EventBridge. - D. 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 Zones. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
正解：B
質問 # 108
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. Take a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.
- C. 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. - 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 # 109
A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.
* Station A, which has 10 sensors
* Station B, which has five sensors
These weather stations were placed by onsite subject-matter experts.
Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.
Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B.
Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.
How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?
- A. Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.
- B. Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.
- C. Modify the partition key to use the sensor ID instead of the station name.
- D. Reduce the number of sensors in Station A from 10 to 5 sensors.
正解：C
解説：
Explanation
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding.html
"Splitting increases the number of shards in your stream and therefore increases the data capacity of the stream. Because you are charged on a per-shard basis, splitting increases the cost of your stream"
質問 # 110
A retail company's data analytics team recently created multiple product sales analysis dashboards for the average selling price per product using Amazon QuickSight. The dashboards were created from .csv files uploaded to Amazon S3. The team is now planning to share the dashboards with the respective external product owners by creating individual users in Amazon QuickSight. For compliance and governance reasons, restricting access is a key requirement. The product owners should view only their respective product analysis in the dashboard reports.
Which approach should the data analytics team take to allow product owners to view only their products in the dashboard?
- A. Create dataset rules with row-level security.
- B. Separate the data by product and use IAM policies for authorization.
- C. Create a manifest file with row-level security.
- D. Separate the data by product and use S3 bucket policies for authorization.
正解：B
質問 # 111
A bank operates in a regulated environment. The compliance requirements for the country in which the bank operates say that customer data for each state should only be accessible by the bank's employees located in the same state. Bank employees in one state should NOT be able to access data for customers who have provided a home address in a different state.
The bank's marketing team has hired a data analyst to gather insights from customer data for a new campaign being launched in certain states. Currently, data linking each customer account to its home state is stored in a tabular .csv file within a single Amazon S3 folder in a private S3 bucket. The total size of the S3 folder is 2 GB uncompressed. Due to the country's compliance requirements, the marketing team is not able to access this folder.
The data analyst is responsible for ensuring that the marketing team gets one-time access to customer data for their campaign analytics project, while being subject to all the compliance requirements and controls.
Which solution should the data analyst implement to meet the desired requirements with the LEAST amount of setup effort?
- A. Load tabular data from Amazon S3 to Amazon QuickSight Enterprise edition by directly importing it as a data source. Use the built-in row-level security feature in Amazon QuickSight to provide marketing employees with appropriate data access under compliance controls. Delete Amazon QuickSight data sources after the project is complete.
- B. Load tabular data from Amazon S3 to Amazon Redshift with the COPY command. Use the built-in row- level security feature in Amazon Redshift to provide marketing employees with appropriate data access under compliance controls. Delete the Amazon Redshift tables after the project.
- C. Re-arrange data in Amazon S3 to store customer data about each state in a different S3 folder within the same bucket. Set up S3 bucket policies to provide marketing employees with appropriate data access under compliance controls. Delete the bucket policies after the project.
- D. Load tabular data from Amazon S3 to an Amazon EMR cluster using s3DistCp. Implement a custom Hadoop-based row-level security solution on the Hadoop Distributed File System (HDFS) to provide marketing employees with appropriate data access under compliance controls. Terminate the EMR cluster after the project.
正解：B
質問 # 112
A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion events every day.
How should this data be stored for optimal performance?
- A. In Apache Parquet partitioned by source IP and sorted by date
- B. In compressed nested JSON partitioned by source IP and sorted by date
- C. In compressed .csv partitioned by date and sorted by source IP
- D. In Apache ORC partitioned by date and sorted by source IP
正解：B
質問 # 113
A media company is using Amazon QuickSight dashboards to visualize its national sales dat a. The dashboard is using a dataset with these fields: ID, date, time_zone, city, state, country, longitude, latitude, sales_volume, and number_of_items.
To modify ongoing campaigns, the company wants an interactive and intuitive visualization of which states across the country recorded a significantly lower sales volume compared to the national average.
Which addition to the company's QuickSight dashboard will meet this requirement?
- A. A drill-down layer for state-level sales volume data.
- B. A geospatial color-coded chart of sales volume data across the country.
- C. A pivot table of sales volume data summed up at the state level.
- D. A drill through to other dashboards containing state-level sales volume data.
正解：C
質問 # 114
A financial services company needs to aggregate daily stock trade data from the exchanges into a data store. The company requires that data be streamed directly into the data store, but also occasionally allows data to be modified using SQL. The solution should integrate complex, analytic queries running with minimal latency. The solution must provide a business intelligence dashboard that enables viewing of the top contributors to anomalies in stock prices.
Which solution meets the company's requirements?
- A. Use Amazon Kinesis Data Streams to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
- B. Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.
- C. Use Amazon Kinesis Data Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
- D. Use Amazon Kinesis Data Firehose to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.
正解：C
質問 # 115
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DAS-C01問題集と練習テスト（159試験問題)：https://www.jpntest.com/shiken/DAS-C01-mondaishu
ガイド（2023年最新）実際のAmazon DAS-C01試験問題：https://drive.google.com/open?id=1W_4ydKzD9lVwpX0HO7mPozksKa9YnuCk