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: S3 Standard provides immediate access and is appropriate for the first 30 days when objects are accessed frequently. After 30 days, transitioning to S3 Standard-IA reduces storage costs while still providing immediate retrieval for infrequently accessed objects. Intelligent-Tiering incurs monitoring charges and is most beneficial when access patterns are unknown. Glacier Deep Archive does not provide immediate access, and using Standard-IA for all objects would incur retrieval charges and a minimum storage duration even during the frequently accessed initial period.
Question 123
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 provides no-code/low-code data preparation with built-in recipe steps for common transformations such as renaming columns, removing columns, skipping rows, creating derived columns, and filtering rows. These requirements are standard DataBrew recipe operations, making it the solution with the least development effort. Glue Python jobs would require custom code, crawlers do not perform transformations, and workflows orchestrate jobs rather than replacing the transformation logic.
Question 124
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 can generate predictions and recommendations by integrating ML models with SQL, supporting inventory recommendation use cases. For real-time inference against an already deployed SageMaker model, Amazon Redshift can invoke a remote SageMaker endpoint directly from SQL. The other options misstate Redshift ML, SageMaker Autopilot, or Redshift capabilities.
Question 125
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 that can define both at-rest and in-transit encryption. For S3 data encrypted with AWS KMS, configure EMR at-rest encryption to use the appropriate KMS key for Amazon S3, and configure in-transit encryption with the PEM certificate stored at the specified S3 path. The security configuration is selected during cluster creation; it is not attached after creation, and only one security configuration can be associated with a cluster.
Question 126
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 built-in transformations to detect and mask PII during data preparation, reducing operational overhead for ETL. IAM is the standard service to manage fine-grained permissions so only authorized users can access unmasked PII during preprocessing. GuardDuty monitors threats rather than masking data, Macie discovers and classifies PII but does not perform masking, and custom scripts increase maintenance.
Question 127
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) provisions, manages, automatically renews, and can automatically deploy public and private SSL/TLS certificates for supported AWS-integrated services, minimizing operational overhead. Self-managed certificates require manual lifecycle management, custom Secrets Manager automation increases complexity, and Amazon ECS Service Connect is unrelated to certificate lifecycle management for EC2-hosted applications.
Question 128
A car sales company maintains data about cars that are listed for sale in an area. The company receives data about new car listings from vendors who upload the data daily as compressed files into Amazon S3. The compressed files are up to 5 KB in size. The company wants to see the most up-to-date listings as soon as the data is uploaded to Amazon S3.
A data engineer must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The data engineer must also provide the ability to perform one-time queries and analytical reporting. The query solution must be scalable.
Which solution will meet these requirements MOST cost-effectively?
A. Use an Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Apache Hive for one-time queries and analytical reporting. Use Amazon OpenSearch Service to bulk ingest the data into compute optimized instances. Use OpenSearch Dashboards in OpenSearch Service for the dashboard.
B. Use a provisioned Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.
C. Use AWS Glue to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Redshift Spectrum for one-time queries and analytical reporting. Use OpenSearch Dashboards in Amazon OpenSearch Service for the dashboard.
D. Use AWS Glue to process incoming data. Use AWS Lambda and S3 Event Notifications to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.
Show Answer
Correct Answer: D
Explanation: The uploads are very small (up to 5 KB) and arrive in Amazon S3, so an event-driven, serverless architecture is the most cost-effective. Amazon S3 Event Notifications can invoke AWS Lambda immediately when new objects arrive, providing near real-time processing without maintaining clusters. AWS Glue can perform the ETL/transformation. Amazon Athena is ideal for scalable, one-time ad hoc queries directly on data in S3 without provisioning infrastructure, and Amazon QuickSight can visualize the processed data for dashboards. EMR and Redshift Spectrum introduce unnecessary infrastructure or cost for this workload, and Step Functions are not required for such a simple event-driven pipeline.
Question 129
A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.
Which solution will meet these requirements with the LEAST ongoing maintenance?
A. Use the DynamoDB TTL feature to automatically expire data based on timestamps.
B. Configure a scheduled Amazon EventBridge rule to invoke an AWS Lambda function to check for data that is older than 1 month. Configure the Lambda function to delete old data.
C. Configure a stream on the DynamoDB table to invoke an AWS Lambda function. Configure the Lambda function to delete data in the table that is older than 1 month.
D. Use an AWS Lambda function to periodically scan the DynamoDB table for data that is older than 1 month. Configure the Lambda function to delete old data.
Show Answer
Correct Answer: A
Explanation: DynamoDB Time to Live (TTL) is designed to automatically expire and delete items based on a timestamp attribute. It requires minimal ongoing maintenance, helps control table size and storage costs, and performs deletions asynchronously without disrupting normal read or write operations.
Question 130
A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AWS Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Daily.csv in a second S3 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day’s CSV file.
A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?
A. Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.
B. Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required fields and empty files.
C. Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.
D. Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.
Show Answer
Correct Answer: B
Explanation: AWS Glue Data Quality can validate datasets within Glue ETL jobs using DQDL rules, including checks for completeness, nulls, and empty datasets. By configuring the ETL pipeline to validate the input before producing or overwriting the output, the previous day's CSV remains unchanged when validation fails. This is the least-effort managed solution compared with custom Lambda code, modifying transformation logic to impute values, or post-processing with Athena.
Question 131
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable formation (PII) about customers within the data. The company's privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules.
The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake.
Which solution will meet these requirements with the LEAST operational overhead?
A. Manually review the data for custom PII categories.
B. Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.
C. Develop custom Python scripts to detect the custom PII categories. Call the scripts from DataBrew.
D. Implement regex patterns to extract PII information from fields during extract transform, and load (ETL) operations into the data lake.
Show Answer
Correct Answer: B
Explanation: AWS Glue DataBrew supports custom data quality rules that can be reused across datasets. When an organization has custom definitions of PII that are not covered by the built-in rules, creating custom DataBrew data quality rules provides a managed, scalable solution with minimal operational overhead. Manual review does not scale, custom Python scripts require ongoing maintenance, and regex during ETL is less flexible and couples detection to ingestion rather than leveraging DataBrew's data quality framework.
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