A data engineer is building a serverless. multi-step extract, transform, and load (ETL) pipeline. The pipeline extracts data from an Amazon S3 data lake and transforms the data by using AWS Glue ETL jobs. The pipeline then loads the results into an Amazon Redshift database. The data engineer needs to orchestrate the serverless ETL workflow.
Which solutions will meet these requirements? (Choose two.)
A. Implement the workflow by using AWS Step Functions. Configure Step Functions to coordinate the AWS Glue ETL jobs and handle error conditions with automatic retries.
B. Use AWS Glue workflows to create a graph of the ETL tasks that visually represents the dependencies between jobs and the job triggers.
C. Provision an always on Amazon EC2 instance. Create a cron job that invokes the AWS Glue ETL jobs in sequence based on a predefined scheduleю
D. Use Amazon EventBridge rules to invoke the AWS Glue ETL jobs based on S3 object creation events. Configure the rules to chain the AWS Glue ETL jobs in sequence and handle complex job dependencies.
E. Build an orchestration solution by using AWS CodePipeline to coordinate the ETL pipeline and infrastructure changes based on the dependencies.
Show Answer
Correct Answer: A, B
Explanation: AWS Step Functions is a serverless workflow orchestration service that can coordinate AWS Glue jobs, manage branching, retries, and error handling. AWS Glue Workflows is purpose-built to orchestrate Glue crawlers, jobs, and triggers with dependency graphs. An always-on EC2 instance is not serverless, EventBridge alone is not designed for complex workflow orchestration and dependency management, and CodePipeline is intended for CI/CD rather than ETL workflow orchestration.
Question 22
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time.
Which solution will meet these requirements?
A. Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.
B. Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.
C. Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.
D. Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Show Answer
Correct Answer: D
Explanation: Athena Federated Query can query and join data across supported external data sources such as Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3 without first centralizing or moving the data. For a one-time performance report, this minimizes unnecessary data movement while enabling cross-source analysis.
Question 23
A company processes a CSV file that contains millions of transaction records every day. The file is stored in Amazon S3. Each transaction must be validated before updating a database. The company needs a solution that will process the data in parallel. The solution must use error handling that stops the entire process if more than 15% of the records fail validation.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create an AWS Batch job that processes chunks of the file in parallel with a custom error tracking mechanism.
B. Use AWS Step Functions Distributed Map state with the ToleratedFailurePercentage field set to 15%.
C. Deploy an Amazon EMR cluster with Spark to process the file Configure a custom failure threshold to 15%.
D. Use AWS Lambda with S3 Batch Operations to process the file and track validation failures to be less than 15%.
Show Answer
Correct Answer: B
Explanation: AWS Step Functions Distributed Map is designed to process very large datasets from Amazon S3 in parallel with managed orchestration. The ToleratedFailurePercentage field provides built-in failure threshold handling, allowing the workflow to stop when more than 15% of items fail. This satisfies the parallel processing and error handling requirements with the least operational overhead compared to managing AWS Batch, EMR, or custom tracking with Lambda and S3 Batch Operations.
Question 24
A company needs to collect logs for an Amazon RDS for MySQL database and make the logs available for audits. The logs must track each user that modifies data in the database or makes changes to the database instance.
Which solution will meet these requirements?
A. Enable Amazon CloudWatch Logs. Create metric filters to monitor database changes and instance-level changes. Configure automated notification systems to send near real-time alerts for suspicious database operations.
B. Configure an Amazon EventBridge rule to monitor database activity. Create an AWS Lambda function to process EventBridge events and store them in Amazon OpenSearch Service.
C. Configure AWS CloudTrail to log API calls. Use Amazon CloudWatch Logs for basic monitoring. Use IAM policies to control access to the logs. Set up scheduled reporting for log audits.
D. Enable and configure native Amazon RDS database audit logging. Enable Amazon CloudWatch Logs. Configure metric filters and alarms. Configure AWS CloudTrail audit logging.
Show Answer
Correct Answer: D
Explanation: Tracking users who modify data inside an Amazon RDS for MySQL database requires native database audit logging, because AWS CloudTrail records AWS API (management plane) activity rather than SQL operations. Tracking changes to the RDS instance itself requires CloudTrail. Publishing database logs to CloudWatch Logs makes them available for monitoring and retention, with metric filters and alarms as needed. Combining native RDS audit logging with CloudWatch Logs and CloudTrail satisfies both database-user auditing and instance-change auditing.
Question 25
A company is building data processing pipelines by using AWS Glue. The pipelines access data stored in Amazon S3. The company has organized the data into folders with prefixes that represent different classification levels. The company needs to restrict AWS Glue jobs to access only specific prefixes based on the data classification. The company must also restrict access to business hours (9 AM to 5 PM).
Which elements must the company include in a custom IAM policy to meet these requirements?
A. A Resource element with S3 object Amazon Resource Name (ARN) patterns that use wildcards for each prefix and a Condition element that uses the $util.time variable with TimeGreaterThan and TimeLessThan operators
B. A Resource element with S3 object Amazon Resource Name (ARN) patterns that use wildcards for each prefix and a Condition element that uses the aws:CurrentTime condition key with DateGreaterThan and DateLessThan operators
C. A Condition element that uses the s3:prefix condition key to restrict folder access and aws:CurrentTime with DateGreaterThanEquals and DateLessThanEquals to restrict hours of operation
D. A Condition element that uses the s3:ResourceAccount condition key to restrict bucket access and a Deny statement that applies outside of business hours
Show Answer
Correct Answer: B
Explanation: To restrict access to specific S3 prefixes in an IAM policy, specify S3 object ARN patterns with wildcarded prefixes in the Resource element. Time-based access in IAM uses the global condition key aws:CurrentTime with DateGreaterThan and DateLessThan operators. $util.time is not an IAM policy variable, s3:prefix is primarily for ListBucket requests rather than object access, and s3:ResourceAccount does not restrict prefixes.
Question 26
A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%.
Which component of an AWS Glue Data Quality rule will meet these requirements?
A. Uniqueness "Email" matches "[%@%.%]" with a threshold set to > 0.9
B. ColumnValues "Email” matches "[%@%.%]" with a threshold set to > 0.1
C. ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.9
D. UniqueValueRatio "Email" matches "[%@%.%]" with a threshold set to > 0.1
Show Answer
Correct Answer: C
Explanation: The AWS Glue Data Quality rule type should validate the values in the Email column against a pattern, not uniqueness. `ColumnValues` with a `matches` regex evaluates the proportion of rows that match the email pattern. Setting the threshold to >0.9 enforces that at least 90% of the column values are valid email addresses; nulls and non-matching values count against the threshold.
Question 27
A company stores sensitive transaction data in an Amazon S3 bucket. A data engineer must implement controls to prevent accidental deletions.
Which solution will meet this requirement?
A. Enable versioning on the S3 bucket and configure MFA delete.
B. Configure an S3 bucket policy rule that denies the creation of S3 delete markers.
C. Create an S3 Lifecycle rule that moves deleted files to S3 Glacier Deep Archive.
D. Set up AWS Config remediation actions to prevent users from deleting S3 objects.
Show Answer
Correct Answer: A
Explanation: Enabling S3 Versioning protects against accidental deletions by retaining previous object versions, and MFA Delete adds an extra layer of protection by requiring multi-factor authentication for version deletions and versioning state changes. A bucket policy cannot reliably prevent delete markers in all required scenarios, Lifecycle rules do not archive deleted objects, and AWS Config remediation is not a preventive control for object deletions.
Question 28
A company’s application needs to search and analyze data in near real time. The application must handle up to 1,000 requests each second with low query latency. The company wants a solution that individual data teams can own and configure to meet each team's cost and performance optimization requirements.
Which solution will meet these requirements?
A. Use Amazon S3 buckets to store the data. Use Amazon Athena to query and analyze the data. Assign each data team a separate S3 bucket prefix to optimize queries.
B. Use streams in Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink to query and analyze the data. Assign each data team a separate stream to manage and consume.
C. Use Amazon OpenSearch Service clusters with indexing to query the data. Assign each data team a separate cluster to configure for storage and queries.
D. Use Amazon Aurora clusters that run on Aurora I/O-Optimized instances. Assign each data team a separate Aurora cluster to configure for storage and queries.
Show Answer
Correct Answer: C
Explanation: Amazon OpenSearch Service is designed for near real-time search and analytics with low-latency queries at high request rates. Separate OpenSearch clusters allow each data team to independently manage storage, indexing, performance, and cost. Amazon Athena is not intended for low-latency near real-time querying, Kinesis Data Streams with Flink is for stream processing rather than search, and Aurora is a relational database rather than a search analytics engine.
Question 29
A global finance company needs to implement near real-time cross-Region synchronization of trading data between trading centers in the us-east-1 Region, the eu-west-2 Region, and the ap-northeast-1 Region. The company must ensure that data is encrypted in transit. The solution must ensure data ordering and consistency and must support cross-Region disaster recovery. The solution must provide data latency of less than 500 milliseconds.
Which solution will meet these requirements with the LEAST operational effort?
A. Deploy Apache Kafka Connect in each AWS Region. Use custom-developed connectors to set up cross-Region data replication. Configure the SSL security protocol.
B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) Replicator to establish fully interconnected replication relationships between MSK clusters in the three AWS Regions. Enable TLS encryption and IAM authentication. Set up cross-Region backup configurations.
C. Deploy Apache Kafka Mirror Maker 2.0 in each AWS Region. Set up custom replication policies to handle cross-Region data synchronization. Configure the SSL security protocol.
D. Use Amazon Kinesis Data Streams to receive trading data from each AWS Region. Use Amazon Data Firehose to replicate data between Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in each Region. Configure AWS Key Management Service (AWS KMS) encryption and IAM roles to manage access.
Show Answer
Correct Answer: B
Explanation: Amazon MSK Replicator is the fully managed AWS service for cross-Region replication between MSK clusters. It provides near real-time replication with minimal operational overhead compared to self-managed MirrorMaker 2 or Kafka Connect, supports encrypted communication using TLS, preserves Kafka partition ordering, and is designed for cross-Region disaster recovery scenarios.
Question 30
A data engineer at a large company needs to create centralized datasets that are optimized for Amazon Redshift performance. The company has multiple downstream teams that use their own AWS accounts and dedicated Amazon Redshift clusters with RA3 nodes. All downstream teams need access to the centralized datasets.
Which solution will provide immediate access to the datasets and maintain the current Amazon Redshift performance?
A. Copy the datasets to an Amazon S3 bucket by using the UNLOAD command. Register the table definitions in a dedicated AWS Glue Data Catalog schema. Share the schema with the other AWS accounts by using AWS Lake Formation. Use Amazon Redshift Spectrum to access the data.
B. Create a daily extract, transform, and load (ETL) job to unload the data to an Amazon S3 staging area. Instruct the teams to copy the data into their Amazon Redshift clusters.
C. Set up Amazon Redshift data sharing between the Amazon Redshift producer clusters and the consumer clusters to provide access to the centralized datasets.
D. Set up an AWS DataSync job that automatically syncs the data between the Amazon Redshift producer clusters and the consumer clusters.
Show Answer
Correct Answer: C
Explanation: Amazon Redshift data sharing allows producer clusters to share live data with consumer Redshift RA3 clusters, including across AWS accounts, without copying or unloading data. Consumers get immediate access to the latest centralized datasets while queries continue to run on Redshift storage optimized for Redshift performance. The other options involve data copies, delays, or unsupported synchronization approaches.
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