A company stores customer data in an Amazon S3 bucket. Multiple teams in the company want to use the customer data for downstream analysis. The company needs to ensure that the teams do not have access to personally identifiable information (PII) about the customers.
Which solution will meet this requirement with LEAST operational overhead?
A. Use Amazon Macie to create and run a sensitive data discovery job to detect and remove PII.
B. Use S3 Object Lambda to access the data, and use Amazon Comprehend to detect and remove PII.
C. Use Amazon Data Firehose and Amazon Comprehend to detect and remove PII.
D. Use an AWS Glue DataBrew job to store the PII data in a second S3 bucket. Perform analysis on the data that remains in the original S3 bucket.
Show Answer
Correct Answer: B
Explanation: Amazon Macie discovers and classifies PII in S3 but does not remove or redact it. S3 Object Lambda can transform objects as they are retrieved, and with Amazon Comprehend can detect and redact PII on-the-fly, allowing downstream teams to access sanitized data without maintaining a separate redacted copy. Data Firehose is for streaming ingestion, not existing S3 objects. AWS Glue DataBrew can clean data but requires creating and maintaining transformed datasets, resulting in more operational overhead.
Question 153
A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?
A. Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.
B. Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.
C. Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.
D. Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.
Show Answer
Correct Answer: C
Explanation: AWS CloudTrail data events for Amazon S3 can record object-level API activity, including the IAM identity/user that made the request. These events can be sent to Amazon CloudWatch Logs, where metric filters and alarms can notify on unauthorized access attempts or policy violations while including the username from the CloudTrail event. AWS Config evaluates resource configuration compliance rather than object access, CloudWatch alone does not provide object-level identity information, and S3 server access logs are less suitable for real-time policy violation alerting and identity-based notifications.
Question 154
A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse. The company wants to use the combined dataset to identify potential new customers.
A data engineer notices that one of the fields in the source data includes values that are in JSON format.
How should the data engineer load the JSON data into the data warehouse with the LEAST effort?
A. Use the SUPER data type to store the data in the Amazon Redshift table.
B. Use AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.
C. Use Amazon S3 to store the JSON data. Use Amazon Athena to query the data.
D. Use an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.
Show Answer
Correct Answer: A
Explanation: Amazon Redshift's SUPER data type is designed for semi-structured data such as JSON, allowing JSON values to be loaded and queried directly without flattening or ETL transformations. This provides the least-effort solution while keeping all data in Redshift for combined analytics.
Question 155
A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs.
Recently, customers reported that a query on one of the Athena tables did not return any data. A data engineer must resolve the issue.
Which combination of troubleshooting steps should the data engineer take? (Choose two.)
A. Confirm that Athena is pointing to the correct Amazon S3 location.
B. Increase the query timeout duration.
C. Use the MSCK REPAIR TABLE command.
D. Restart Athena.
E. Delete and recreate the problematic Athena table.
Show Answer
Correct Answer: A, C
Explanation: If Athena returns no data, first verify that the table points to the correct S3 location. Because the data is partitioned, missing partition metadata is a common cause of empty query results; running MSCK REPAIR TABLE discovers and adds partitions that exist in S3 but are not registered in the Glue/Athena metastore. Increasing timeouts, restarting Athena, or recreating the table do not address the underlying metadata or location issues.
Question 156
A data engineer configured an AWS Glue Data Catalog for data that is stored in Amazon S3 buckets. The data engineer needs to configure the Data Catalog to receive incremental updates.
The data engineer sets up event notifications for the S3 bucket and creates an Amazon Simple Queue Service (Amazon SQS) queue to receive the S3 events.
Which combination of steps should the data engineer take to meet these requirements with LEAST operational overhead? (Choose two.)
A. Create an S3 event-based AWS Glue crawler to consume events from the SQS queue.
B. Define a time-based schedule to run the AWS Glue crawler, and perform incremental updates to the Data Catalog.
C. Use an AWS Lambda function to directly update the Data Catalog based on S3 events that the SQS queue receives.
D. Manually initiate the AWS Glue crawler to perform updates to the Data Catalog when there is a change in the S3 bucket.
E. Use AWS Step Functions to orchestrate the process of updating the Data Catalog based on S3 events that the SQS queue receives.
Show Answer
Correct Answer: A, B
Explanation: AWS Glue supports S3 event-based crawlers that consume S3 event notifications from an Amazon SQS queue to perform incremental crawls. These event-based crawlers are still invoked on a schedule; when they run, they check the SQS queue and crawl only changed folders if events exist, otherwise they stop quickly. This uses the managed Glue capability with the least operational overhead. Using Lambda or Step Functions adds custom code and orchestration that are unnecessary for this requirement.
Question 157
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Choose two.)
A. The producer experienced network-related timeouts.
B. The stream’s value for the IteratorAgeMilliseconds metric was too high.
C. There was a change in the number of shards, record processors, or both.
D. The AggregationEnabled configuration property was set to true.
E. The max_records configuration property was set to a number that was too high.
Show Answer
Correct Answer: A, C
Explanation: Kinesis provides at-least-once delivery semantics, so duplicate records can occur under certain conditions. Network-related timeouts on the producer can cause the Kinesis Producer Library (KPL) to retry a PutRecords/PutRecord request when it is uncertain whether the original request succeeded, resulting in duplicate records in the stream. In addition, changes to shard topology or record processor assignments (such as shard splits/merges during scaling) can cause the Kinesis Client Library (KCL) to reprocess records around checkpoint boundaries, leading to duplicate consumption. High IteratorAgeMilliseconds indicates consumer lag rather than duplication, record aggregation does not itself create duplicates, and increasing max_records affects batch size, not delivery semantics.
Question 158
A data engineer needs to create an empty copy of an existing table in Amazon Athena to perform data processing tasks. The existing table in Athena contains 1,000 rows.
Which query will meet this requirement?
A. CREATE TABLE new_table - LIKE old_table;
B. CREATE TABLE new_table - AS SELECT * FROM old_table - WITH NO DATA;
C. CREATE TABLE new_table - AS SELECT * FROM old_table;
D. CREATE TABLE new_table - as SELECT * FROM old_cable - WHERE 1=1;
Show Answer
Correct Answer: B
Explanation: In Amazon Athena, CREATE TABLE AS SELECT (CTAS) supports the WITH NO DATA clause to create a new table with the same schema as the SELECT result but without copying any rows. Option C copies all data, option D with WHERE 1=1 also copies all rows, and option A is not valid Athena syntax for duplicating a table schema.
Question 159
A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations,
The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The company's QuickSight instance is in a separate account named BI-Account.
The company updates the S3 bucket policy to grant access to the QuickSight service role. The company wants to enable cross-account access to allow QuickSight to interact with the S3 bucket.
Which combination of steps will meet this requirement? (Choose two.)
A. Use the existing AWS KMS key to encrypt connections from QuickSight to the S3 bucket.
B. Add the S3 bucket as a resource that the QuickSight service role can access.
C. Use AWS Resource Access Manager (AWS RAM) to share the S3 bucket with the BI-Account account.
D. Add an IAM policy to the QuickSight service role to give QuickSight access to the KMS key that encrypts the S3 bucket.
E. Add the KMS key as a resource that the QuickSight service role can access.
Show Answer
Correct Answer: B, D
Explanation: The S3 bucket policy has already been updated. To complete cross-account access for Amazon QuickSight, you must configure QuickSight to allow its service role to access the cross-account S3 bucket (add the bucket as an allowed resource), and the QuickSight service role must have IAM permissions to use the customer-managed KMS key (such as kms:Decrypt and kms:DescribeKey). The KMS key policy in the owning account must also trust that role in practice, but among the listed options, adding the IAM policy to the service role is the correct selectable step. AWS RAM is not used for sharing S3 buckets, and KMS does not encrypt connections.
Sources:
https://www.dumpspedia.com/aws-certified-data-engineer-associate-dea-c01-dumps.html
https://www.p2pexams.com/free-questions/real-amazon-amazon-dea-c01-study-questions-by-bird.pdf
Question 160
A marketing company uses Amazon S3 to store marketing data. The company uses versioning in some buckets. The company runs several jobs to read and load data into the buckets.
To help cost-optimize its storage, the company wants to gather information about incomplete multipart uploads and outdated versions that are present in the S3 buckets.
Which solution will meet these requirements with the LEAST operational effort?
A. Use AWS CLI to gather the information.
B. Use Amazon S3 Inventory configurations reports to gather the information.
C. Use the Amazon S3 Storage Lens dashboard to gather the information.
D. Use AWS usage reports for Amazon S3 to gather the information.
Show Answer
Correct Answer: C
Explanation: Amazon S3 Storage Lens provides built-in metrics and dashboards for storage usage optimization, including visibility into incomplete multipart uploads and noncurrent (outdated) object versions across buckets, with minimal operational effort. S3 Inventory generates object reports but is intended for object-level inventory rather than aggregated storage optimization insights. AWS CLI and usage reports require more manual work.
Question 161
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
Which solution will meet these requirements with the LEAST operational effort?
A. Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.
B. Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.
C. Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.
D. Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.
Show Answer
Correct Answer: B
Explanation: Amazon Athena Federated Query can query Amazon Redshift, Teradata, and Google BigQuery through connectors, allowing a single SQL statement to read and join data across sources. Predicate and query pushdown lets source engines perform as much processing as possible. Athena supports Apache Iceberg tables in Amazon S3, including MERGE INTO operations, providing a serverless solution with the least operational overhead compared to managing Glue or EMR ETL jobs.
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