An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?
A. Transition the data to Amazon S3 Glacier Flexible Retrieval.
B. Transition the data from Amazon S3 to an Amazon Aurora cluster.
C. Configure AWS Glue ETL to transform the incoming data to Apache Parquet format.
D. Configure AWS Glue ETL to use Amazon EMR to process incoming data in parallel.
Show Answer
Correct Answer: C
Explanation: Storing analytical data in Apache Parquet significantly reduces storage and query costs compared to CSV. Parquet is a columnar, compressed format that lowers S3 storage usage and minimizes data scanned during analytics queries, which is ideal for daily analysis workloads. Other options either increase cost or are unsuitable for frequently accessed analytical data.
Question 26
A data engineer is launching an Amazon EMR cluster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit.
The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.
Which solution will meet these requirements?
A. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Create a second security configuration. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach both security configurations to the cluster.
B. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for local disk encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
C. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
D. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach the security configuration to the cluster.
Show Answer
Correct Answer: C
Explanation: Amazon EMR uses a single security configuration to define both at-rest and in-transit encryption. To protect data read from and written to Amazon S3 that is encrypted with AWS KMS, the security configuration must specify S3 at-rest encryption with the appropriate KMS key. For in-transit encryption within the cluster, the configuration must also specify the S3 path to the PEM certificate. This security configuration is then selected and used during EMR cluster creation. You cannot attach multiple security configurations to one cluster, and local disk encryption is not the requirement here.
Question 27
A company runs multiple applications on AWS. The company configured each application to output logs. The company wants to query and visualize the application logs in near real time.
Which solution will meet these requirements?
A. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an Amazon S3 bucket. Create an AWS Lambda function that runs on a schedule to export the required log groups to the S3 bucket. Use Amazon Athena to query the log data in the S3 bucket.
B. Create an Amazon OpenSearch Service domain. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an OpenSearch Service subscription filter for each log group to stream the data to OpenSearch. Create the required queries and dashboards in OpenSearch Service to analyze and visualize the data.
C. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Use CloudWatch log anomaly detection to query and visualize the log data.
D. Update the application code to send the log data to Amazon QuickSight by using Super-fast, Parallel, In-memory Calculation Engine (SPICE). Create the required analyses and dashboards in QuickSight.
Show Answer
Correct Answer: B
Explanation: The requirement is to query and visualize application logs in near real time. Streaming CloudWatch Logs to Amazon OpenSearch Service via subscription filters provides near-real-time ingestion, powerful search, and built-in visualization dashboards. Other options either introduce delays (scheduled exports to S3 and Athena), lack log visualization capabilities, or are not designed for direct log ingestion.
Question 27
A company uses Amazon Redshift as its data warehouse service. A data engineer needs to design a physical data model.
The data engineer encounters a de-normalized table that is growing in size. The table does not have a suitable column to use as the distribution key.
Which distribution style should the data engineer use to meet these requirements with the LEAST maintenance overhead?
A. ALL distribution
B. EVEN distribution
C. AUTO distribution
D. KEY distribution
Show Answer
Correct Answer: C
Explanation: The table is large and growing and has no suitable column for a distribution key, which rules out KEY distribution. ALL distribution would create high storage and maintenance overhead as the table grows. EVEN distribution could work, but it requires the engineer to manually choose and potentially revisit the design. AUTO distribution lets Amazon Redshift automatically select and adjust the optimal distribution style over time (ALL, KEY, or EVEN) in the background, resulting in the least maintenance overhead.
Question 28
A company wants to combine data from multiple software as a service (SaaS) applications for analysis.
A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.
Which solution will meet these requirements in the MOST operationally efficient way?
A. Create AWS Lambda functions that call the required APIs to extract the data from the applications. Store the data in an Amazon S3 bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.
B. Use AWS Lambda functions as Amazon Athena data source connectors to run federated queries against the SaaS applications. Create an Athena data source and a dataset in QuickSight.
C. Use Amazon AppFlow to create a flow for each SaaS application. Set an Amazon S3 bucket as the destination. Schedule the flows to extract the data to the bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.
D. Export data the from the SaaS applications as Microsoft Excel files. Create a data source and a dataset in QuickSight by uploading the Excel files.
Show Answer
Correct Answer: C
Explanation: Amazon AppFlow is purpose-built to ingest data from multiple SaaS applications into AWS services like Amazon S3 with minimal operational overhead. It is fully managed, supports scheduling and incremental loads, and requires no custom API code. Once data is landed in S3 and cataloged with AWS Glue, Amazon QuickSight can easily query it. Other options require custom development, manual exports, or less suitable federated querying, making them less operationally efficient.
Question 28
A data engineer is using an AWS Glue crawler to catalog data that is in an Amazon S3 bucket. The S3 bucket contains both .csv and json files. The data engineer configured the crawler to exclude the .json files from the catalog.
When the data engineer runs queries in Amazon Athena, the queries also process the excluded .json files. The data engineer wants to resolve this issue. The data engineer needs a solution that will not affect access requirements for the .csv files in the source S3 bucket.
Which solution will meet this requirement with the SHORTEST query times?
A. Adjust the AWS Glue crawler settings to ensure that the AWS Glue crawler also excludes .json files.
B. Use the Athena console to ensure the Athena queries also exclude the .json files.
C. Relocate the .json files to a different path within the S3 bucket.
D. Use S3 bucket policies to block access to the .json files.
Show Answer
Correct Answer: C
Explanation: Athena reads all objects under the S3 location defined for a table, regardless of AWS Glue crawler exclude patterns. Excluding .json files in the crawler only affects the Data Catalog schema, not what Athena scans, so queries still read the .json files and incur extra scan time. Moving the .json files to a different S3 prefix that is not referenced by the Athena table prevents Athena from scanning them, yielding the shortest query times without changing access to the .csv files.
Question 29
A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files.
The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline.
The company needs to improve the performance of the second pipeline.
Which solution will meet this requirement MOST cost-effectively?
A. Use a larger worker type.
B. Increase the number of workers in the AWS Glue ETL jobs.
C. Use the AWS Glue DynamicFrame grouping option.
D. Enable AWS Glue auto scaling.
Show Answer
Correct Answer: C
Explanation: The performance issue is caused by processing tens of thousands of very small files (200–300 KB), which creates excessive I/O and Spark task overhead. AWS Glue DynamicFrame grouping combines many small files into larger in-memory partitions before processing, significantly improving performance without increasing worker size or count. This avoids additional compute cost, making it the most cost-effective solution.
Question 29
An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party tool in the on-premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers. The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?
A. AWS Lambda
B. Amazon Managed Workflows for Apache Airflow (Amazon MVVAA)
C. AWS Step Functions
D. AWS Glue
Show Answer
Correct Answer: B
Explanation: Amazon Managed Workflows for Apache Airflow (MWAA) is a fully managed, serverless orchestration service that runs Apache Airflow on AWS-managed infrastructure (Fargate). It can directly orchestrate existing Python and Bash scripts using Airflow operators without requiring code refactoring. This closely matches the company’s current third‑party orchestration model and minimizes operational overhead. Lambda and Step Functions would require refactoring workflows, and AWS Glue is focused on ETL jobs rather than general script orchestration.
Question 30
A company uses an Amazon Redshift cluster as a data warehouse that is shared across two departments. To comply with a security policy, each department must have unique access permissions.
Department A must have access to tables and views for Department A. Department B must have access to tables and views for Department B.
The company often runs SQL queries that use objects from both departments in one query.
Which solution will meet these requirements with the LEAST operational overhead?
A. Group tables and views for each department into dedicated schemas. Manage permissions at the schema level.
B. Group tables and views for each department into dedicated databases. Manage permissions at the database level.
C. Update the names of the tables and views to follow a naming convention that contains the department names. Manage permissions based on the new naming convention.
D. Create an IAM user group for each department. Use identity-based IAM policies to grant table and view permissions based on the IAM user group.
Show Answer
Correct Answer: A
Explanation: Using separate schemas for each department allows granular access control with GRANT/REVOKE at the schema and object level while keeping everything in the same Redshift database. This supports cross-department queries easily via cross-schema references and avoids the higher operational overhead of managing multiple databases or IAM-based object permissions, which Redshift does not support at the table/view level.
Question 30
A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data. The company uses an Amazon Redshift cluster for a data warehouse.
An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster.
A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.
Which combination of steps will meet these requirements? (Choose two.)
A. Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database.
B. Configure Amazon Redshift Spectrum to query live transactional data that is in the PostgreSQL database.
C. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.
D. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Redshift Spectrum to access historical data from S3 Glacier Flexible Retrieval.
E. Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.
Show Answer
Correct Answer: A, C
Explanation: To query live transactional data directly from Aurora PostgreSQL without additional ETL, Amazon Redshift Federated Query is required (A). To reduce Redshift storage costs while still enabling analytics on older data, data older than 15 months should be unloaded to Amazon S3 and queried using Amazon Redshift Spectrum (C). Redshift Spectrum cannot query Glacier Flexible Retrieval, and it is designed specifically to query data stored in S3.
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