DAS-C01 Online Practice Questions and Answers

A company is planning to do a proof of concept for a machine learning (ML) project using Amazon SageMaker with a subset of existing on-premises data hosted in the company's 3 TB data warehouse. For part of the project, AWS Direct Connect is established and tested. To prepare the data for ML, data analysts are performing data curation. The data analysts want to perform multiple step, including mapping, dropping null fields, resolving choice, and splitting fields. The company needs the fastest solution to curate the data for this project.

Which solution meets these requirements?

A. Ingest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon EMR cluster. Store the curated data in Amazon S3 for ML processing.

B. Create custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML processing.

C. Ingest data into Amazon S3 using AWS DMS. Use AWS Glue to perform data curation and store the data in Amazon S3 for ML processing.

D. Take a full backup of the data store and ship the backup files using AWS Snowball. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.

A company is building a data lake and needs to ingest data from a relational database that has time-series data. The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3.

What is the MOST cost-effective approach to meet these requirements?

A. Use AWS Glue to connect to the data source using JDBC Drivers. Ingest incremental records only using job bookmarks.

B. Use AWS Glue to connect to the data source using JDBC Drivers. Store the last updated key in an Amazon DynamoDB table and ingest the data using the updated key as a filter.

C. 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.

D. 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.

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. 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.

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. 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.

D. 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.

A university intends to use Amazon Kinesis Data Firehose to collect JSON-formatted batches of water quality readings in Amazon S3. The readings are from 50 sensors scattered across a local lake. Students will query the stored data using Amazon Athena to observe changes in a captured metric over time, such as water temperature or acidity. Interest has grown in the study, prompting the university to reconsider how data will be stored.

Which data format and partitioning choices will MOST significantly reduce costs? (Choose two.)

A. Store the data in Apache Avro format using Snappy compression.

B. Partition the data by year, month, and day.

C. Store the data in Apache ORC format using no compression.

D. Store the data in Apache Parquet format using Snappy compression.

E. Partition the data by sensor, year, month, and day.

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. Choose Amazon OpenSearch Service (Amazon Elasticsearch Service) 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 Data Firehose delivery stream to CloudWatch. Choose Amazon OpenSearch Service (Amazon Elasticsearch Service) 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. Configure Splunk 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 Firehose delivery stream to CloudWatch. Configure Amazon DynamoDB as the end destination of the weblogs.

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. Convert the .csv files to Apache Parquet.

B. Convert the .csv files to Apache Avro.

C. Partition the data by campaign.

D. Partition the data by source.

E. Compress the .csv files.

A company uses Amazon Redshift for its data warehousing needs. ETL jobs run every night to load data, apply business rules, and create aggregate tables for reporting. The company's data analysis, data science, and business intelligence teams use the data warehouse during regular business hours. The workload management is set to auto, and separate queues exist for each team with the priority set to NORMAL.

Recently, a sudden spike of read queries from the data analysis team has occurred at least twice daily, and queries wait in line for cluster resources. The company needs a solution that enables the data analysis team to avoid query queuing without impacting latency and the query times of other teams.

Which solution meets these requirements?

A. Increase the query priority to HIGHEST for the data analysis queue.

B. Configure the data analysis queue to enable concurrency scaling.

C. Create a query monitoring rule to add more cluster capacity for the data analysis queue when queries are waiting for resources.

D. Use workload management query queue hopping to route the query to the next matching queue.

An analytics software as a service (SaaS) provider wants to offer its customers business intelligence (BI) reporting capabilities that are self-service. The provider is using Amazon QuickSight to build these reports. The data for the reports resides in a multi-tenant database, but each customer should only be able to access their own data.

The provider wants to give customers two user role options:

Read-only users for individuals who only need to view dashboards.

Power users for individuals who are allowed to create and share new dashboards with other users.

Which QuickSught feature allows the provider to meet these requirements?

A. Embedded dashboards

B. Table calculations

C. Isolated namespaces

D. SPICE

A company hosts an Apache Flink application on premises. The application processes data from several Apache Kafka clusters. The data originates from a variety of sources, such as web applications, mobile apps, and operational databases. The company has migrated some of these sources to AWS and now wants to migrate the Flink application. The company must ensure that data that resides in databases within the VPC does not traverse the internet. The application must be able to process all the data that comes from the company's AWS solution, on-premises resources, and the public internet.

Which solution will meet these requirements with the LEAST operational overhead?

A. Implement Flink on Amazon EC2 within the company's VPC. Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the VPC to collect data that comes from applications and databases within the VPC. Use Amazon Kinesis Data Streams to collect data that comes from the public internet. Configure Flink to have sources from Kinesis Data Streams Amazon MSK, and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.

B. Implement Flink on Amazon EC2 within the company's VPC. Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC and the public internet. Configure Flink to have sources from Kinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.

C. Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink .jar file. Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC and the public internet. Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.

D. Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink .jar file. Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the company's VPC to collect data that comes from applications and databases within the VPC. Use Amazon Kinesis Data Streams to collect data that comes from the public internet. Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams, Amazon MSK, and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.

A financial institution is building an Amazon QuickSight business intelligence (BI) dashboard to show financial performance and analyze trends. The development team is using an Amazon Redshift database in the development environment and is having difficulty with validating the accuracy of the metrics calculation algorithm due to the lack of quality data. The Redshift production environment database is 500 TB and is in a different AWS account in the same AWS Region as the development environment account. The company needs to use up-to-date production environment data for development purposes.

Which solution MOST cost-effectively meets these requirements?

A. Setup data streaming with Amazon Kinesis Data Streams from the production environment Redshift database to replicate the data to the development environment Redshift database.

B. Create a Redshift datashare to share the production environment data with the development team.

C. Upload the data from Amazon Redshift to Amazon S3. Then load the data directly from Amazon S3 to the development environment Redshift cluster using the COPY command.

D. Create Redshift views that are configured to share all the data between the production and development clusters.