A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.
Which solution will meet these requirements?
A. Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.
B. Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.
C. Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster. Write the logs to a table in the Aurora MySQL database.
D. Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.
Show Answer
Correct Answer: A
Explanation: Amazon Redshift natively supports audit logging for user activity and connection activity, and these logs are automatically delivered to an Amazon S3 bucket that you specify. This is the supported, durable, and compliant way to store Redshift logs. Redshift cannot write logs directly to EFS, Aurora, or EBS, making the other options invalid.
Question 61
A company wants to migrate a data warehouse from Teradata to Amazon Redshift.
Which solution will meet this requirement with the LEAST operational effort?
A. Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.
B. Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.
C. Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.
D. Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.
Show Answer
Correct Answer: B
Explanation: Migrating from Teradata to Amazon Redshift requires both schema conversion and data migration. AWS Database Migration Service (DMS) is optimized for data movement but has limited schema conversion capabilities. The AWS Schema Conversion Tool (SCT) is specifically designed to convert complex source schemas, stored procedures, and SQL dialects from Teradata to Redshift with minimal manual work. Using SCT for schema migration and DMS for data migration is the AWS-recommended approach and results in the least operational effort compared to manual schema handling or relying on DMS alone.
Question 62
A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.
The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon Redshift Serverless to load all the data into Amazon Redshift managed storage (RMS).
B. Use Amazon Athena to load all the data into Amazon S3 in Apache Parquet format.
C. Use Amazon Redshift provisioned clusters to load all the data into Amazon Redshift managed storage (RMS).
D. Use Amazon Aurora PostgreSQL to load all the data into Aurora.
Show Answer
Correct Answer: A
Explanation: Amazon Redshift Serverless provides a fully managed, auto-scaling data warehouse optimized for fast analytics with minimal operational overhead. It handles unpredictable query spikes, integrates natively with Amazon QuickSight in direct query mode, and avoids manual capacity provisioning required by provisioned clusters. Athena can have higher latency for interactive BI, and Aurora is not optimized as a central analytics warehouse.
Question 63
A company has AWS resources in multiple AWS Regions. The company has an Amazon EFS file system in each Region where the company operates. The company’s data science team operates within only a single Region. The data that the data science team works with must remain within the team's Region.
A data engineer needs to create a single dataset by processing files that are in each of the company's Regional EFS file systems. The data engineer wants to use an AWS Step Functions state machine to orchestrate AWS Lambda functions to process the data.
Which solution will meet these requirements with the LEAST effort?
A. Peer the VPCs that host the EFS file systems in each Region with the VPC that is in the data science team’s Region. Enable EFS file locking. Configure the Lambda functions in the data science team's Region to mount each of the Region specific file systems. Use the Lambda functions to process the data.
B. Configure each of the Regional EFS file systems to replicate data to the data science team's Region. In the data science team’s Region, configure the Lambda functions to mount the replica file systems. Use the Lambda functions to process the data.
C. Deploy the Lambda functions to each Region. Mount the Regional EFS file systems to the Lambda functions. Use the Lambda functions to process the data. Store the output in an Amazon S3 bucket in the data science team’s Region.
D. Use AWS DataSync to transfer files from each of the Regional EFS files systems to the file system that is in the data science team's Region. Configure the Lambda functions in the data science team's Region to mount the file system that is in the same Region. Use the Lambda functions to process the data.
Show Answer
Correct Answer: B
Explanation: Amazon EFS replication is a managed, native feature that automatically keeps file systems in sync across Regions. Replicating each Regional EFS file system into the data science team’s Region allows the Lambda functions and Step Functions state machine to process all data locally, satisfying the data locality requirement. This approach requires less setup and ongoing management than configuring AWS DataSync jobs or deploying Lambda functions across multiple Regions, resulting in the least overall engineering effort.
Question 64
A data engineer is using Amazon QuickSight to build a dashboard to report a company’s revenue in multiple AWS Regions. The data engineer wants the dashboard to display the total revenue for a Region, regardless of the drill-down levels shown in the visual.
Which solution will meet these requirements?
A. Create a table calculation.
B. Create a simple calculated field.
C. Create a level-aware calculation - aggregate (LAC-A) function.
D. Create a level-aware calculation - window (LAC-W) function.
Show Answer
Correct Answer: C
Explanation: To show total revenue for a Region regardless of the drill-down level in an Amazon QuickSight visual, the calculation must ignore the visual’s current level of detail. Level-aware calculations using aggregate functions (LAC-A) explicitly define the aggregation level (for example, by Region), ensuring the value remains constant even when users drill down. Table calculations and simple calculated fields are affected by the visual’s grain, and LAC-W functions operate over windows within the current visual context, not fixed aggregation levels.
Question 65
The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first 30 days after the records are created. The company accesses records that are older than 30 days infrequently.
The company needs to cost-optimize its Amazon S3 storage.
Which solution will meet these requirements MOST cost-effectively?
A. Apply a lifecycle policy to transition records to S3 Standard Infrequent-Access (S3 Standard-IA) storage after 30 days.
B. Use S3 Intelligent-Tiering storage.
C. Transition records to S3 Glacier Deep Archive storage after 30 days.
D. Use S3 Standard-Infrequent Access (S3 Standard-IA) storage for all customer records.
Show Answer
Correct Answer: A
Explanation: Records must be accessed immediately for the first 30 days, which fits S3 Standard. After 30 days, access is infrequent, making S3 Standard-IA a lower-cost option while still providing immediate access when needed. Glacier options do not allow immediate access, Intelligent-Tiering adds monitoring cost without clear benefit here, and using Standard-IA for all data would be more expensive for frequently accessed new records.
Question 66
A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?
A. Use AWS Glue Python jobs to read and transform the CSV files.
B. Use an AWS Glue custom crawler to read and transform the CSV files.
C. Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.
D. Use AWS Glue DataBrew recipes to read and transform the CSV files.
Show Answer
Correct Answer: D
Explanation: AWS Glue DataBrew is designed for low-code/no-code data preparation. It natively supports column renaming, column removal, row filtering (including ignoring specific rows), creating new columns based on existing values, and filtering by numeric conditions through recipes, with minimal development effort. Glue Python jobs or workflows require custom code and more setup, and crawlers are for schema discovery, not transformation.
Question 67
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Choose two.)
A. Use Amazon Redshift ML to generate inventory recommendations.
B. Use SQL to invoke a remote SageMaker endpoint for prediction.
C. Use Amazon Redshift ML to schedule regular data exports for offline model training.
D. Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.
E. Use Amazon Redshift as a file storage system to archive old inventory management reports.
Show Answer
Correct Answer: A, B
Explanation: Amazon Redshift ML allows Redshift to directly integrate with SageMaker models to generate predictions such as inventory recommendations using SQL, satisfying the real-time recommendation requirement. Additionally, Redshift can invoke a remote SageMaker real-time endpoint via SQL to perform predictions about future inventory needs. The other options do not address real-time prediction or are incorrect uses of the services.
Question 69
A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details.
Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the pre-processing phase. The company needs a low-maintenance solution to mask and secure the PII data throughout the entire engineering pipeline.
Which combination of solutions will meet these requirements? (Choose two.)
A. Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.
B. Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.
C. Configure an Amazon Macie discovery job for the S3 bucket.
D. Use AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data.
E. Write custom scripts in an application to mask the PII data and to control access.
Show Answer
Correct Answer: A, D
Explanation: AWS Glue DataBrew provides a low-maintenance, managed way to detect and mask PII as part of the data preparation/ETL pipeline before analysis. AWS IAM controls fine-grained access to S3 objects and KMS keys, allowing only authorized users to access unmasked PII during pre-processing while securing the data throughout the pipeline.
Question 70
A company hosts its applications on Amazon EC2 instances. The company must use SSL/TLS connections that encrypt data in transit to communicate securely with AWS infrastructure that is managed by a customer.
A data engineer needs to implement a solution to simplify the generation, distribution, and rotation of digital certificates. The solution must automatically renew and deploy SSL/TLS certificates.
Which solution will meet these requirements with the LEAST operational overhead?
A. Store self-managed certificates on the EC2 instances.
B. Use AWS Certificate Manager (ACM).
C. Implement custom automation scripts in AWS Secrets Manager.
D. Use Amazon Elastic Container Service (Amazon ECS) Service Connect.
Show Answer
Correct Answer: B
Explanation: AWS Certificate Manager (ACM) is a fully managed service that handles the creation, storage, automatic renewal, and deployment of SSL/TLS certificates. It integrates directly with supported AWS services and eliminates the need for manual certificate management or custom automation, resulting in the least operational overhead.
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