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 requirements favor a fully serverless, event-driven, and low-cost architecture that reacts immediately to small files arriving in Amazon S3. AWS Glue is appropriate for scalable ETL without managing clusters. S3 Event Notifications with AWS Lambda provide near-real-time triggering and lightweight orchestration without the overhead and cost of Step Functions or EMR. Amazon Athena is ideal for scalable, pay-per-query, one-time and ad hoc analytical queries directly on S3 data, avoiding the cost and setup of Redshift or EMR. Amazon QuickSight integrates natively with Athena for dashboards. Overall, option D meets the real-time, scalability, and cost-effectiveness requirements best.
Question 72
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 a fully managed feature that automatically deletes items after a specified timestamp without impacting ongoing read or write operations. It requires minimal configuration, no custom code, scales automatically, and directly addresses cost control and table size management with the least ongoing maintenance compared to Lambda-based scanning or stream processing solutions.
Question 73
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 rules (DQDL) are purpose-built to validate datasets for conditions such as empty files and missing required fields within an existing Glue ETL job. This requires minimal additional code and effort, and allows the pipeline to conditionally proceed or fail so the previous day’s CSV is only overwritten when the new data is valid. The other options require custom logic or additional services, increasing effort.
Question 74
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 creating and enforcing custom data quality rules, which can include organization-specific definitions of PII. These rules can be reused and applied consistently across multiple datasets in the data lake with minimal operational overhead. The other options either require manual effort, custom scripting, or embedding logic into ETL jobs, all of which increase maintenance and operational complexity compared to native DataBrew capabilities.
Question 75
A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.
Which solution will meet these requirements with the LEAST effort?
A. Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.
B. Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.
C. Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.
D. Use scripts to scan data elements and to assign data classifications based on the format of the data.
Show Answer
Correct Answer: B
Explanation: AWS Glue crawlers can automatically scan Amazon S3 and Amazon RDS data sources to populate the AWS Glue Data Catalog. By using built-in and custom crawler classifiers, Glue can detect and store storage format metadata (such as CSV, JSON, Parquet) directly in the catalog. This provides an automated, low-effort solution compared to manual inspection, scripting, or using services like Macie that are not designed to build data catalogs.
Question 76
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?
A. Expand the storage of the MSK broker. Configure the MSK cluster storage to expand automatically.
B. Expand the storage of the Apache ZooKeeper nodes.
C. Update the MSK broker instance to a larger instance type. Restart the MSK cluster.
D. Specify the Target Volume-in-GiB parameter for the existing topic.
Show Answer
Correct Answer: A
Explanation: The RootDiskUsed metric indicates that the brokers’ root/EBS storage is running out of space due to the additional topic and data. The correct remediation is to increase broker storage. Amazon MSK supports expanding broker storage and configuring it for automatic expansion, which directly addresses disk utilization without changing instance types or ZooKeeper. ZooKeeper storage is unrelated, instance resizing is unnecessary, and topic-level volume parameters do not control broker disk capacity.
Question 77
A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day. The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.
The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.
Which solution will meet this requirement with the LEAST operational overhead?
A. Create data quality checks for the source datasets that the daily reports use. Create a new AWS managed Apache Airflow cluster. Run the data quality checks by using Airflow tasks that run data quality queries on the columns data type and the presence of null values. Configure Airflow Directed Acyclic Graphs (DAGs) to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
B. Create data quality checks on the source datasets that the daily reports use. Create a new Amazon EMR cluster. Use Apache Spark SQL to create Apache Spark jobs in the EMR cluster that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow. Configure the workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
C. Create data quality checks on the source datasets that the daily reports use. Create data quality actions by using AWS Glue workflows to confirm the completeness and consistency of the datasets. Configure the data quality actions to create an event in Amazon EventBridge if a dataset is incomplete. Configure EventBridge to send the event that informs the data engineer about the incomplete datasets to the Amazon SNS topic.
D. Create AWS Lambda functions that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow that runs the Lambda functions. Configure the Step Functions workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
Show Answer
Correct Answer: C
Explanation: AWS Glue Data Quality integrates natively with the AWS Glue Data Catalog and Glue workflows, allowing managed data quality rules (such as completeness and consistency) with minimal infrastructure to manage. Glue can emit events to Amazon EventBridge when data quality rules fail, and EventBridge can directly route notifications to an existing Amazon SNS topic. This serverless, managed approach avoids operating clusters (EMR), orchestration engines (Airflow, Step Functions), or custom Lambda code, resulting in the least operational overhead.
Question 78
A data engineer needs to onboard a new data producer into AWS. The data producer needs to migrate data products to AWS.
The data producer maintains many data pipelines that support a business application. Each pipeline must have service accounts and their corresponding credentials. The data engineer must establish a secure connection from the data producer's on-premises data center to AWS. The data engineer must not use the public internet to transfer data from an on-premises data center to AWS.
Which solution will meet these requirements?
A. Instruct the new data producer to create Amazon Machine Images (AMIs) on Amazon Elastic Container Service (Amazon ECS) to store the code base of the application. Create security groups in a public subnet that allow connections only to the on-premises data center.
B. Create an AWS Direct Connect connection to the on-premises data center. Store the service account credentials in AWS Secrets manager.
C. Create a security group in a public subnet. Configure the security group to allow only connections from the CIDR blocks that correspond to the data producer. Create Amazon S3 buckets than contain presigned URLS that have one-day expiration dates.
D. Create an AWS Direct Connect connection to the on-premises data center. Store the application keys in AWS Secrets Manager. Create Amazon S3 buckets that contain presigned URLS that have one-day expiration dates.
Show Answer
Correct Answer: B
Explanation: AWS Direct Connect provides a private, dedicated network connection from the on-premises data center to AWS without using the public internet. AWS Secrets Manager securely stores and manages service account credentials required by the data pipelines. Other options either rely on public internet access, misuse services, or add unnecessary components that do not address the core requirements.
Question 79
A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.
A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places. The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).
Which solution will meet these requirements?
A. Define and create the calculated field in the dataset.
B. Define and create the calculated field in the analysis.
C. Define and create the calculated field in the visual.
D. Define and create the calculated field in the dashboard.
Show Answer
Correct Answer: A
Explanation: Calculated fields created at the dataset level in Amazon QuickSight are computed during data ingestion and materialized in SPICE. This ensures the exchange rate calculations are precomputed, stored in the in-memory engine, and can maintain the required four-decimal precision. Calculated fields created in analyses, visuals, or dashboards are evaluated at query or render time and are not precomputed or materialized in SPICE.
Question 80
An ecommerce company operates a complex order fulfilment process that spans several operational systems hosted in AWS. Each of the operational systems has a Java Database
Connectivity (JDBC)-compliant relational database where the latest processing state is captured.
The company needs to give an operations team the ability to track orders on an hourly basis across the entire fulfillment process.
Which solution will meet these requirements with the LEAST development overhead?
A. Use AWS Glue to build ingestion pipelines from the operational systems into Amazon Redshift Build dashboards in Amazon QuickSight that track the orders.
B. Use AWS Glue to build ingestion pipelines from the operational systems into Amazon DynamoDBuild dashboards in Amazon QuickSight that track the orders.
C. Use AWS Database Migration Service (AWS DMS) to capture changed records in the operational systems. Publish the changes to an Amazon DynamoDB table in a different AWS region from the source database. Build Grafana dashboards that track the orders.
D. Use AWS Database Migration Service (AWS DMS) to capture changed records in the operational systems. Publish the changes to an Amazon DynamoDB table in a different AWS region from the source database. Build Amazon QuickSight dashboards that track the orders.
Show Answer
Correct Answer: A
Explanation: The requirement is hourly tracking across multiple JDBC-compliant relational databases with the least development overhead. Amazon Redshift is purpose-built for aggregating and querying relational data for analytics, and Amazon QuickSight integrates natively with Redshift for dashboards. AWS Glue provides managed connectors and ETL jobs to ingest data from JDBC sources on a schedule with minimal custom code.
The DynamoDB-based options add unnecessary complexity: DynamoDB is not a natural fit for relational, cross-system order tracking, and QuickSight does not query DynamoDB directly without additional integration steps. The DMS-based approaches also introduce extra setup and operational complexity without reducing dashboard development effort. Therefore, using Glue to load data into Redshift and visualizing with QuickSight is the lowest-overhead solution.
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