DAS-C01 : AWS Certified Data Analytics – Specialty : Part 06

DAS-C01 : AWS Certified Data Analytics – Specialty : Part 06

1. A company wants to collect and process events data from different departments in near-real time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB.

   How should a data analytics specialist design the solution for data ingestion?

   • Use Amazon Kinesis Data Streams. Configure a stream for the raw data. Use a Kinesis Agent to write data to the stream. Create an Amazon Kinesis Data Analytics application that reads data from the raw stream, cleanses it, and stores the output to Amazon S3.
   • Use Amazon Kinesis Data Firehose. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansing. Use a Kinesis Agent to write data to the delivery stream. Configure Kinesis Data Firehose to deliver the data to Amazon S3.
   • Use Amazon Managed Streaming for Apache Kafka. Configure a topic for the raw data. Use a Kafka producer to write data to the topic. Create an application on Amazon EC2 that reads data from the topic by using the Apache Kafka consumer API, cleanses the data, and writes to Amazon S3.
   • Use Amazon Simple Queue Service (Amazon SQS). Configure an AWS Lambda function to read events from the SQS queue and upload the events to Amazon S3.

2. An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: “Command Failed with Exit Code 1.”

   Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90–95% soon after. The average memory usage across all executors continues to be less than 4%.

   The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.

   

   What should the data engineer do to solve the failure in the MOST cost-effective way?

   • Change the worker type from Standard to G.2X.
   • Modify the AWS Glue ETL code to use the ‘groupFiles’: ‘inPartition’ feature.
   • Increase the fetch size setting by using AWS Glue dynamics frame.
   • Modify maximum capacity to increase the total maximum data processing units (DPUs) used.

3. 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?

   • Kinesis Agent
   • Kinesis Producer Library (KPL)
   • Kinesis Data Firehose
   • Kinesis SDK

4. 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?

   • Geospatial chart
   • Line chart
   • Heat map
   • Tree map

5. A marketing company has data in Salesforce, MySQL, and Amazon S3. The company wants to use data from these three locations and create mobile dashboards for its users. The company is unsure how it should create the dashboards and needs a solution with the least possible customization and coding.

   Which solution meets these requirements?

   • Use Amazon Athena federated queries to join the data sources. Use Amazon QuickSight to generate the mobile dashboards.
   • Use AWS Lake Formation to migrate the data sources into Amazon S3. Use Amazon QuickSight to generate the mobile dashboards.
   • Use Amazon Redshift federated queries to join the data sources. Use Amazon QuickSight to generate the mobile dashboards.
   • Use Amazon QuickSight to connect to the data sources and generate the mobile dashboards.

6. 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?

   • Increase the query priority to HIGHEST for the data analysis queue.
   • Configure the data analysis queue to enable concurrency scaling.
   • Create a query monitoring rule to add more cluster capacity for the data analysis queue when queries are waiting for resources.
   • Use workload management query queue hopping to route the query to the next matching queue.

7. 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?

   • Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.
   • Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.
   • 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.
   • 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.

8. A company has a data lake on AWS that ingests sources of data from multiple business units and uses Amazon Athena for queries. The storage layer is Amazon S3 using the AWS Glue Data Catalog. The company wants to make the data available to its data scientists and business analysts. However, the company first needs to manage data access for Athena based on user roles and responsibilities.

   What should the company do to apply these access controls with the LEAST operational overhead?

   • Define security policy-based rules for the users and applications by role in AWS Lake Formation.
   • Define security policy-based rules for the users and applications by role in AWS Identity and Access Management (IAM).
   • Define security policy-based rules for the tables and columns by role in AWS Glue.
   • Define security policy-based rules for the tables and columns by role in AWS Identity and Access Management (IAM).

9. A company has an encrypted Amazon Redshift cluster. The company recently enabled Amazon Redshift audit logs and needs to ensure that the audit logs are also encrypted at rest. The logs are retained for 1 year. The auditor queries the logs once a month.

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

   • Encrypt the Amazon S3 bucket where the logs are stored by using AWS Key Management Service (AWS KMS). Copy the data into the Amazon Redshift cluster from Amazon S3 on a daily basis. Query the data as required.
   • Disable encryption on the Amazon Redshift cluster, configure audit logging, and encrypt the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query the data as required.
   • Enable default encryption on the Amazon S3 bucket where the logs are stored by using AES-256 encryption. Copy the data into the Amazon Redshift cluster from Amazon S3 on a daily basis. Query the data as required.
   • Enable default encryption on the Amazon S3 bucket where the logs are stored by using AES-256 encryption. Use Amazon Redshift Spectrum to query the data as required.

10. A data analytics specialist is setting up workload management in manual mode for an Amazon Redshift environment. The data analytics specialist is defining query monitoring rules to manage system performance and user experience of an Amazon Redshift cluster.

    Which elements must each query monitoring rule include?

    • A unique rule name, a query runtime condition, and an AWS Lambda function to resubmit any failed queries in off hours
    • A queue name, a unique rule name, and a predicate-based stop condition
    • A unique rule name, one to three predicates, and an action
    • A workload name, a unique rule name, and a query runtime-based condition

11. A market data company aggregates external data sources to create a detailed view of product consumption in different countries. The company wants to sell this data to external parties through a subscription. To achieve this goal, the company needs to make its data securely available to external parties who are also AWS users.

    What should the company do to meet these requirements with the LEAST operational overhead?

    • Store the data in Amazon S3. Share the data by using presigned URLs for security.
    • Store the data in Amazon S3. Share the data by using S3 bucket ACLs.
    • Upload the data to AWS Data Exchange for storage. Share the data by using presigned URLs for security.
    • Upload the data to AWS Data Exchange for storage. Share the data by using the AWS Data Exchange sharing wizard.