[2023年05月06日]AWS-Certified-Data-Analytics-Specialtyサンプルには正確で更新された問題
AWS-Certified-Data-Analytics-Specialty試験情報と無料練習テスト
質問 # 37
A power utility company is deploying thousands of smart meters to obtain real-time updates about power consumption. The company is using Amazon Kinesis Data Streams to collect the data streams from smart meters. The consumer application uses the Kinesis Client Library (KCL) to retrieve the stream data. The company has only one consumer application.
The company observes an average of 1 second of latency from the moment that a record is written to the stream until the record is read by a consumer application. The company must reduce this latency to 500 milliseconds.
Which solution meets these requirements?
- A. Use enhanced fan-out in Kinesis Data Streams.
- B. Reduce the propagation delay by overriding the KCL default settings.
- C. Increase the number of shards for the Kinesis data stream.
- D. Develop consumers by using Amazon Kinesis Data Firehose.
正解：B
解説：
Explanation
The KCL defaults are set to follow the best practice of polling every 1 second. This default results in average propagation delays that are typically below 1 second.
質問 # 38
Three teams of data analysts use Apache Hive on an Amazon EMR cluster with the EMR File System (EMRFS) to query data stored within each teams Amazon S3 bucket. The EMR cluster has Kerberos enabled and is configured to authenticate users from the corporate Active Directory. The data is highly sensitive, so access must be limited to the members of each team.
Which steps will satisfy the security requirements?
- A. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3.
Create three additional IAM roles, each granting access to each team's specific bucket. Add the additional IAM roles to the cluster's EMR role for the EC2 trust policy. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team. - B. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3.
Create three additional IAM roles, each granting access to each team's specific bucket. Add the service role for the EMR cluster EC2 instances to the trust policies for the additional IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team. - C. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3.
Create three additional IAM roles, each granting access to each team's specific bucket. Add the service role for the EMR cluster EC2 instances to the trust polices for the base IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team. - D. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3.
Create three additional IAM roles, each granting access to each team's specific bucket. Add the service role for the EMR cluster EC2 instances to the trust polices for the additional IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
正解：D
質問 # 39
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 a snapshot, restore, and resize operation. Switch to the new target cluster.
- B. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
- C. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
- D. Enable concurrency scaling in the workload management (WLM) queue.
正解：D
解説：
https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.html
質問 # 40
A company has an application that uses the Amazon Kinesis Client Library (KCL) to read records from a Kinesis data stream.
After a successful marketing campaign, the application experienced a significant increase in usage. As a result, a data analyst had to split some shards in the data stream. When the shards were split, the application started throwing an ExpiredIteratorExceptions error sporadically.
What should the data analyst do to resolve this?
- A. Increase the provisioned write capacity units assigned to the stream's Amazon DynamoDB table.
- B. Increase the number of threads that process the stream records.
- C. Decrease the provisioned write capacity units assigned to the stream's Amazon DynamoDB table.
- D. Increase the provisioned read capacity units assigned to the stream's Amazon DynamoDB table.
正解：A
質問 # 41
A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, or applications running in the same AWS account to comply with internal security policies.
Which solution meets these requirements?
- A. Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.
- B. Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. and apply the S3 bucket policy to the S3 bucket.
- C. Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.
- D. Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.
正解：A
解説：
https://docs.aws.amazon.com/athena/latest/ug/user-created-workgroups.html Amazon Athena Workgroups - A new resource type that can be used to separate query execution and query history between Users, Teams, or Applications running under the same AWS account https://aws.amazon.com/about-aws/whats-new/2019/02/athena_workgroups/
質問 # 42
A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company's analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall dat a. The amount of data that is ingested into Amazon S3 has increased substantially over time, and the query latency also has increased.
Which solutions could the company implement to improve query performance? (Choose two.)
- A. Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.
- B. Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.
- C. Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.
- D. Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connector. Run the query from MySQL Workbench instead of Athena directly.
- E. Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.
正解：C、E
解説：
Reference:
https://aws.amazon.com/blogs/big-data/work-with-partitioned-data-in-aws-glue/
質問 # 43
A company has 1 million scanned documents stored as image files in Amazon S3. The documents contain typewritten application forms with information including the applicant first name, applicant last name, application date, application type, and application text. The company has developed a machine learning algorithm to extract the metadata values from the scanned documents. The company wants to allow internal data analysts to analyze and find applications using the applicant name, application date, or application text.
The original images should also be downloadable. Cost control is secondary to query performance.
Which solution organizes the images and metadata to drive insights while meeting the requirements?
- A. For each image, use object tags to add the metadata. Use Amazon S3 Select to retrieve the files based on the applicant name and application date.
- B. 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. - C. 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.
- D. 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.
正解：A
質問 # 44
An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.
Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)
- A. Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.
- B. Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.
- C. Use an S3 bucket in the same Region as Athena.
- D. Use an S3 bucket in the same account as Athena.
- E. Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.
- F. Compress the objects to reduce the data transfer I/O.
正解：A、C、F
解説：
Explanation
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/
質問 # 45
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. 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.
- B. Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:* event notification on the S3 bucket.
- C. Use Amazon CloudWatch Events with the rate (1 hour) expression to execute the AWS Glue crawler every hour.
- D. Using the AWS CLI, modify the execution schedule of the AWS Glue crawler from 8 hours to 1 minute.
正解：B
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 # 46
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 table. Use DISTSTYLE EVEN for the customers table.
- 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 KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.
- D. Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.
正解：A
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 # 47
A company recently created a test AWS account to use for a development environment The company also created a production AWS account in another AWS Region As part of its security testing the company wants to send log data from Amazon CloudWatch Logs in its production account to an Amazon Kinesis data stream in its test account Which solution will allow the company to accomplish this goal?
- A. Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account Create a subscription filter in the production accounts CloudWatch Logs to target the Kinesis data stream in the test account as its destination
- B. Create a subscription filter in the production accounts CloudWatch Logs to target the Kinesis data stream in the test account as its destination In the test account create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account
- C. In the test account create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account
- D. In the test account, create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account
正解：A
質問 # 48
A company uses an Amazon EMR cluster with 50 nodes to process operational data and make the data available for data analysts These jobs run nightly use Apache Hive with the Apache Jez framework as a processing model and write results to Hadoop Distributed File System (HDFS) In the last few weeks, jobs are failing and are producing the following error message
"File could only be replicated to 0 nodes instead of 1"
A data analytics specialist checks the DataNode logs the NameNode logs and network connectivity for potential issues that could have prevented HDFS from replicating data The data analytics specialist rules out these factors as causes for the issue Which solution will prevent the jobs from failing'?
- A. Monitor the HDFSUtilization metri.c If the value crosses a user-defined threshold add core nodes to the EMR cluster
- B. Monitor the HDFSUtilization metric. If the value crosses a user-defined threshold add task nodes to the EMR cluster
- C. Monitor the MemoryAllocatedMB metric. If the value crosses a user-defined threshold, add core nodes to the EMR cluster.
- D. Monitor the MemoryAllocatedMB metric. If the value crosses a user-defined threshold, add task nodes to the EMR cluster
正解：D
質問 # 49
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 Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.
- B. 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.
- C. 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.
- D. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.
正解：C
質問 # 50
Three teams of data analysts use Apache Hive on an Amazon EMR cluster with the EMR File System (EMRFS) to query data stored within each teams Amazon S3 bucket. The EMR cluster has Kerberos enabled and is configured to authenticate users from the corporate Active Directory. The data is highly sensitive, so access must be limited to the members of each team.
Which steps will satisfy the security requirements?
- A. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3. Create three additional IAM roles, each granting access to each team's specific bucket. Add the service role for the EMR cluster EC2 instances to the trust polices for the additional IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
- B. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3. Create three additional IAM roles, each granting access to each team's specific bucket. Add the service role for the EMR cluster EC2 instances to the trust policies for the additional IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
- C. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3. Create three additional IAM roles, each granting access to each team's specific bucket. Add the service role for the EMR cluster EC2 instances to the trust polices for the base IAM roles. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
- D. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3. Create three additional IAM roles, each granting access to each team's specific bucket. Add the additional IAM roles to the cluster's EMR role for the EC2 trust policy. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
正解：A
質問 # 51
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. 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.
- 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：
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 # 52
A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running ad-hoc queries, and a data engineering group to run stored procedures and ETL processes.
The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes.
Which Amazon Redshift feature meets the requirements for this task?
- A. Workload management (WLM)
- B. Short query acceleration (SQA)
- C. Concurrency scaling
- D. Materialized views
正解：D
解説：
Explanation
Materialized views:
質問 # 53
A company launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service.
The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture.
Which actions should the data analyst take to resolve this issue? (Choose two.)
- A. Increase the number of shards in the stream using the UpdateShardCount API.
- B. Choose partition keys in a way that results in a uniform record distribution across shards.
- C. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.
- 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、B
解説：
https://aws.amazon.com/blogs/big-data/under-the-hood-scaling-your-kinesis-data-streams/
質問 # 54
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