無料Amazon DAS-C01プレミアム試験エンジンPDFをダウンロード 更新された159問があります
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AWS認定データ分析を取得することの重要な利点の1つ - 専門認定は、AWSサービスを使用して効果的なデータ分析ソリューションを設計および実装するために必要な知識とスキルを雇用主とクライアントに実証することです。この認定は、業界で非常に評価されており、データ分析の分野でキャリアを促進したい専門家に新しいキャリアの機会を開くことができます。
Amazon DAS-C01は、Amazon Web Services（AWS）プラットフォーム上でのデータ分析ソリューションの設計、構築、および維持に必要なスキルと知識を検証するためのプロフェッショナルレベルの認定試験です。この認定試験は、データ分析の概念に強い理解を持ち、データ分析に関連するAWSサービスでの経験がある個人を対象としています。
質問 # 42
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. 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.
- 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. 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.
正解：C
解説：
Explanation
Updating Manually Created Data Catalog Tables Using Crawlers: To do this, when you define a crawler, instead of specifying one or more data stores as the source of a crawl, you specify one or more existing Data Catalog tables. The crawler then crawls the data stores specified by the catalog tables. In this case, no new tables are created; instead, your manually created tables are updated.
質問 # 43
A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company uses PutRecord to send data to Amazon Kinesis, and has observed network outages during certain times of the day. The company wants to obtain exactly once semantics for the entire processing pipeline.
What should the company do to obtain these characteristics?
- A. Rely on the exactly one processing semantics of Apache Flink and Apache Spark Streaming included in Amazon EMR.
- B. Design the data producer so events are not ingested into Kinesis Data Streams multiple times.
- C. Rely on the processing semantics of Amazon Kinesis Data Analytics to avoid duplicate processing of events.
- D. Design the application so it can remove duplicates during processing be embedding a unique ID in each record.
正解：D
質問 # 44
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. Reduce the number of sensors in Station A from 10 to 5 sensors.
- B. Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.
- C. Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.
- D. Modify the partition key to use the sensor ID instead of the station name.
正解：D
解説：
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 # 45
A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental data. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only.
Which solution achieves these required access patterns to minimize costs and administrative tasks?
- A. Keep the account structure and the individual AWS Glue catalogs on each account. Add a central data lake account and use AWS Glue to catalog data from various accounts. Configure cross-account access for AWS Glue crawlers to scan the data in each departmental S3 bucket to identify the schema and populate the catalog. Add the senior data analysts into the central account and apply highly detailed access controls in the Data Catalog and Amazon S3.
- B. Set up an individual AWS account for the central data lake and configure a central S3 bucket. Use an AWS Lake Formation blueprint to move the data from the various buckets into the central S3 bucket.
On each individual bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls for both associate and senior analysts to view specific tables and columns. - C. Consolidate all AWS accounts into one account. Create different S3 buckets for each department and move all the data from every account to the central data lake account. Migrate the individual data catalogs into a central data catalog and apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.
- D. Set up an individual AWS account for the central data lake. Use AWS Lake Formation to catalog the cross- account locations. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls to allow senior analysts to view specific tables and columns.
正解：D
解説：
Explanation
Lake Formation provides secure and granular access to data through a new grant/revoke permissions model that augments AWS Identity and Access Management (IAM) policies. Analysts and data scientists can use the full portfolio of AWS analytics and machine learning services, such as Amazon Athena, to access the data.
The configured Lake Formation security policies help ensure that users can access only the data that they are authorized to access. Source : https://docs.aws.amazon.com/lake-formation/latest/dg/how-it-works.html
質問 # 46
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 and ingest the full data. Use AWS DataSync to ensure the delta only is written into Amazon S3.
- B. Use AWS Glue to connect to the data source using JDBC Drivers. Ingest incremental records only using job bookmarks.
- C. 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.
- D. 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.
正解：B
解説：
Explanation
https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html
質問 # 47
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. 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.
- B. 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.
- C. 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.
- D. 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.
正解：B
質問 # 48
A manufacturing company uses Amazon S3 to store its data. The company wants to use AWS Lake Formation to provide granular-level security on those data assets. The data is in Apache Parquet format. The company has set a deadline for a consultant to build a data lake.
How should the consultant create the MOST cost-effective solution that meets these requirements?
- A. Install Apache Ranger on an Amazon EC2 instance and integrate with Amazon EMR. Using Ranger policies, create role-based access control for the existing data assets in Amazon S3.
- B. Create multiple IAM roles for different users and groups. Assign IAM roles to different data assets in Amazon S3 to create table-based and column-based access controls.
- C. Run Lake Formation blueprints to move the data to Lake Formation. Once Lake Formation has the data, apply permissions on Lake Formation.
- D. To create the data catalog, run an AWS Glue crawler on the existing Parquet data. Register the Amazon S3 path and then apply permissions through Lake Formation to provide granular-level security.
正解：C
解説：
Explanation
https://aws.amazon.com/blogs/big-data/building-securing-and-managing-data-lakes-with-aws-lake-formation/
質問 # 49
An insurance company has raw data in JSON format that is sent without a predefined schedule through an Amazon Kinesis Data Firehose delivery stream to an Amazon S3 bucket. An AWS Glue crawler is scheduled to run every 8 hours to update the schema in the data catalog of the tables stored in the S3 bucket. Data analysts analyze the data using Apache Spark SQL on Amazon EMR set up with AWS Glue Data Catalog as the metastore. Data analysts say that, occasionally, the data they receive is stale. A data engineer needs to provide access to the most up-to-date data.
Which solution meets these requirements?
- A. Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:* event notification on the S3 bucket.
- B. Using the AWS CLI, modify the execution schedule of the AWS Glue crawler from 8 hours to 1 minute.
- C. Create an external schema based on the AWS Glue Data Catalog on the existing Amazon Redshift cluster to query new data in Amazon S3 with Amazon Redshift Spectrum.
- D. Use Amazon CloudWatch Events with the rate (1 hour) expression to execute the AWS Glue crawler every hour.
正解：A
解説：
https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html "you can use a wildcard (for example, s3:ObjectCreated:*) to request notification when an object is created regardless of the API used" "AWS Lambda can run custom code in response to Amazon S3 bucket events. You upload your custom code to AWS Lambda and create what is called a Lambda function. When Amazon S3 detects an event of a specific type (for example, an object created event), it can publish the event to AWS Lambda and invoke your function in Lambda. In response, AWS Lambda runs your function."
質問 # 50
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 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.
- B. 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.
- 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 Glacier Deep Archive 1 year after data creation.
- D. 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.
- 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：A、E
質問 # 51
An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query.
Which steps will create the required logs?
- A. Enable Amazon Redshift Enhanced VPC Routing. Enable VPC Flow Logs to monitor traffic.
- B. Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.
- 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：B
質問 # 52
A hospital is building a research data lake to ingest data from electronic health records (EHR) systems from multiple hospitals and clinics. The EHR systems are independent of each other and do not have a common patient identifier. The data engineering team is not experienced in machine learning (ML) and has been asked to generate a unique patient identifier for the ingested records.
Which solution will accomplish this task?
- A. Amazon Kendra
- B. An AWS Glue ETL job with the ResolveChoice transform
- C. An AWS Glue ETL job with the FindMatches transform
- D. Amazon SageMaker Ground Truth
正解：C
解説：
Explanation
Matching Records with AWS Lake Formation FindMatches
質問 # 53
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 Spectrum to query data in the data lake. 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. 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.
- D. Leverage DS2 nodes for the Amazon Redshift cluster. Migrate all data from Amazon S3 to Amazon Redshift. Decommission the data lake.
正解：A
質問 # 54
A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster.
The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight.
How should the data be secured?
- A. Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.
- B. Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.
- C. Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.
- D. Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.
正解：A
解説：
Explanation
https://docs.aws.amazon.com/quicksight/latest/user/directory-integration.html
質問 # 55
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. Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
- B. 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.
- C. Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.
- D. Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.
正解：A
質問 # 56
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 to implement row-level security (RLS) to the sales table.
- B. Set up an Amazon Redshift VPC security group for Australia and the US.
- C. Place the data sources for Australia and the US into separate SPICE capacity pools.
- D. Deploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.
正解：D
質問 # 57
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. 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.
- B. 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.
- C. Create a VPC endpoint from the Amazon QuickSight VPC to the Amazon Redshift VPC so Amazon QuickSight can access data from 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：C
質問 # 58
A company owns facilities with IoT devices installed across the world. The company is using Amazon Kinesis Data Streams to stream data from the devices to Amazon S3. The company's operations team wants to get insights from the IoT data to monitor data quality at ingestion. The insights need to be derived in near-real time, and the output must be logged to Amazon DynamoDB for further analysis.
Which solution meets these requirements?
- A. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function.
Save the data to Amazon S3. Then run an AWS Glue job on schedule to ingest the data into DynamoDB. - B. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function.
Save the output to DynamoDB by using the default output from Kinesis Data Firehose. - C. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.
- D. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.
正解：B
質問 # 59
An insurance company has raw data in JSON format that is sent without a predefined schedule through an Amazon Kinesis Data Firehose delivery stream to an Amazon S3 bucket. An AWS Glue crawler is scheduled to run every 8 hours to update the schema in the data catalog of the tables stored in the S3 bucket. Data analysts analyze the data using Apache Spark SQL on Amazon EMR set up with AWS Glue Data Catalog as the metastore. Data analysts say that, occasionally, the data they receive is stale. A data engineer needs to provide access to the most up-to-date data.
Which solution meets these requirements?
- A. Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:* event notification on the S3 bucket.
- B. Using the AWS CLI, modify the execution schedule of the AWS Glue crawler from 8 hours to 1 minute.
- C. Create an external schema based on the AWS Glue Data Catalog on the existing Amazon Redshift cluster to query new data in Amazon S3 with Amazon Redshift Spectrum.
- D. Use Amazon CloudWatch Events with the rate (1 hour) expression to execute the AWS Glue crawler every hour.
正解：A
解説：
Explanation
https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html "you can use a wildcard (for example, s3:ObjectCreated:*) to request notification when an object is created regardless of the API used"
"AWS Lambda can run custom code in response to Amazon S3 bucket events. You upload your custom code to AWS Lambda and create what is called a Lambda function. When Amazon S3 detects an event of a specific type (for example, an object created event), it can publish the event to AWS Lambda and invoke your function in Lambda. In response, AWS Lambda runs your function."
質問 # 60
......
Amazon DAS-C01：AWS認定データ分析 - 専門（DAS-C01）試験は、AWSサービスを使用してデータ分析における候補者の知識とスキルを検証するように設計されています。この認定は、AWSプラットフォームで分析ソリューションの設計、構築、保護、維持の専門知識を実証したいデータ専門家に最適です。この試験では、データの収集、処理、ストレージ、分析にAWSサービスを使用する候補者の能力、およびデータから洞察を導き出すためにさまざまな分析ツールと手法を使用する能力を検証します。
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DAS-C01問題集PDF最新 [2023年最新] 究極の学習ガイド：https://drive.google.com/open?id=1xqNkn48hx9B6UktfxbnoArMTVYlqSkhp