AWS Certified Data Analytics - Specialty practice test

1. Question 1 · 1

   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?

   Choose one answer.

   • 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.
   • 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.
   • 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.
   • 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.

2. Question 2 · 1

   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?

   Choose one answer.

   • 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.
   • Use Amazon CloudWatch Events with the rate (1 hour) expression to execute the AWS Glue crawler every hour.
   • Using the AWS CLI, modify the execution schedule of the AWS Glue crawler from 8 hours to 1 minute.
   • Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:* event notification on the S3 bucket.

3. Question 3 · 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?

   Choose one answer.

   • 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.

4. Question 4 · 1

   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?

   Choose one answer.

   

   • 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.

5. Question 5 · 1

   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?

   Choose one answer.

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

6. Question 6 · 1

   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?

   Choose one answer.

   • 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.

7. Question 7 · 1

   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?

   Choose one answer.

   • 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).

8. Question 8 · 1

   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?

   Choose one answer.

   • 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.

9. Question 9 · 1

   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?

   Choose one answer.

   • In Apache ORC partitioned by date and sorted by source IP
   • In compressed .csv partitioned by date and sorted by source IP
   • In Apache Parquet partitioned by source IP and sorted by date
   • In compressed nested JSON partitioned by source IP and sorted by date

10. Question 10 · 1

    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?

    Choose one answer.

    • 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.

11. Question 11 · 1

    A company has a marketing department and a finance department. The departments are storing data in Amazon S3 in their own AWS accounts in AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their data. The departments have some databases and tables that share common names. The marketing department needs to securely access some tables from the finance department. Which two steps are required for this process? (Choose two.)

    Choose all answers that apply.

    • The finance department grants Lake Formation permissions for the tables to the external account for the marketing department.
    • The finance department creates cross-account IAM permissions to the table for the marketing department role.
    • The marketing department creates an IAM role that has permissions to the Lake Formation tables.

12. Question 12 · 1

    A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company's data. The Amazon Redshift cluster contains a product table and a transactions table, and both tables have a product_sku column. The tables are over 100 GB in size. The majority of queries run on both tables. Which distribution style should the company use for the two tables to achieve optimal query performance?

    Choose one answer.

    • An EVEN distribution style for both tables
    • A KEY distribution style for both tables
    • An ALL distribution style for the product table and an EVEN distribution style for the transactions table
    • An EVEN distribution style for the product table and an KEY distribution style for the transactions table

13. Question 13 · 1

    A company receives data from its vendor in JSON format with a timestamp in the file name. The vendor uploads the data to an Amazon S3 bucket, and the data is registered into the company's data lake for analysis and reporting. The company has configured an S3 Lifecycle policy to archive all files to S3 Glacier after 5 days. The company wants to ensure that its AWS Glue crawler catalogs data only from S3 Standard storage and ignores the archived files. A data analytics specialist must implement a solution to achieve this goal without changing the current S3 bucket configuration. Which solution meets these requirements?

    Choose one answer.

    • Use the exclude patterns feature of AWS Glue to identify the S3 Glacier files for the crawler to exclude.
    • Schedule an automation job that uses AWS Lambda to move files from the original S3 bucket to a new S3 bucket for S3 Glacier storage.
    • Use the excludeStorageClasses property in the AWS Glue Data Catalog table to exclude files on S3 Glacier storage.
    • Use the include patterns feature of AWS Glue to identify the S3 Standard files for the crawler to include.

14. Question 14 · 1

    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 data. 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.)

    Choose all answers that apply.

    • 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.
    • Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.
    • 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.
    • Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.
    • Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.

15. Question 15 · 1

    A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the objects stored in the data stores. The data stores are composed of structured sources like Amazon RDS and Amazon Redshift, and semistructured sources like JSON and XML files stored in Amazon S3. The catalog must be updated on a regular basis, be able to detect the changes to object metadata, and require the least possible administration. Which solution meets these requirements?

    Choose one answer.

    • Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the data catalog in Aurora. Schedule the Lambda functions periodically.
    • Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and update the Data Catalog with metadata changes. Schedule the crawlers periodically to update the metadata catalog.
    • Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the DynamoDB catalog. Schedule the Lambda functions periodically.
    • Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for RDS and Amazon Redshift sources and build the Data Catalog. Use AWS crawlers for data stored in Amazon S3 to infer the schema and automatically update the Data Catalog.

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