Amazon-DEA-C01 Exam Questions & Answers

Option B is the most operationally efficient because it checks the business requirement directly: whether the target table contains the most recent data, not merely whether a job failed. Monitoring only failures (Options C and D) can produce false positives (a job failure might not impact freshness) and false negatives (a job can succeed but still load stale or incomplete data). The study material emphasizes implementing data quality validation as part of the ETL process so data can be verified before or as it is stored, rather than relying only on pipeline execution status.

Using a data quality rule focused on freshness (for example, validating that a ''max event timestamp'' or ''latest partition date'' meets today's expected value) lets the pipeline detect stale loads even when the workflow runs. Then, an EventBridge rule can route failures of that data quality check to SNS for immediate notification, keeping operations serverless and centralized. Macie (Option A) is designed for sensitive-data discovery/classification, not operational ''freshness'' checks on Redshift tables, so it adds unnecessary services and effort compared to a Glue-native data quality validation approach.

Option A is the correct design for single-digit millisecond latency with unpredictable spikes and variable attributes. The study material describes Amazon DynamoDB as a NoSQL database ''designed for highly dynamic datasets with frequent read and write operations,'' providing low-latency performance at any scale---which directly matches the latency and traffic-spike requirements.

DynamoDB's key-value and document model fits ''product data that has variable attributes'' because items can contain different attributes without needing schema migrations typical of relational databases. The requirement to retrieve items by Product ID maps naturally to DynamoDB's primary key access pattern. The requirement for flexible queries on Category and Brand is met by creating global secondary indexes (GSIs) on those attributes so queries can be served efficiently without scanning the whole table.

Option B (Aurora) can scale reads, but it is not typically the best fit for sustained single-digit millisecond performance during sudden spikes without careful capacity planning. Option C is optimized for search and text/query relevance rather than primary-key transactional access patterns. Option D uses Athena (interactive SQL over S3) which is not designed for millisecond-latency, high-QPS query workloads.

To make the S3 data accessible daily in the AWS Glue Data Catalog, the data engineer needs to create a crawler that can crawl the S3 data and write the metadata to the Data Catalog. The crawler also needs to run on a daily schedule to keep the Data Catalog updated with the latest data. Therefore, the solution must include the following steps:

Create an IAM role that has the necessary permissions to access the S3 data and the Data Catalog.The AWSGlueServiceRole policy is a managed policy that grants these permissions1.

Associate the role with the crawler.

Specify the S3 bucket path of the source data as the crawler's data store.The crawler will scan the data and infer the schema and format2.

Create a daily schedule to run the crawler.The crawler will run at the specified time every day and update the Data Catalog with any changes in the data3.

Specify a database name for the output. The crawler will create or update a table in the Data Catalog under the specified database. The table will contain the metadata about the data in the S3 bucket, such as the location, schema, and classification.

Option B is the only solution that includes all these steps. Therefore, option B is the correct answer.

Option A is incorrect because it configures the output destination to a new path in the existing S3 bucket. This is unnecessary and may cause confusion, as the crawler does not write any data to the S3 bucket, only metadata to the Data Catalog.

Option C is incorrect because it allocates data processing units (DPUs) to run the crawler every day. This is also unnecessary, as DPUs are only used for AWS Glue ETL jobs, not crawlers.

Option D is incorrect because it combines the errors of option A and C. It configures the output destination to a new path in the existing S3 bucket and allocates DPUs to run the crawler every day, both of which are irrelevant for the crawler.

:

1: AWS managed (predefined) policies for AWS Glue - AWS Glue

2: Data Catalog and crawlers in AWS Glue - AWS Glue

3: Scheduling an AWS Glue crawler - AWS Glue

[4]: Parameters set on Data Catalog tables by crawler - AWS Glue

[5]: AWS Glue pricing - Amazon Web Services (AWS)

AWS Glue Workflows can coordinate multiple ETL jobs and triggers. They support parallel execution and sequential dependencies, which is ideal for concurrent data processing followed by correlation steps, all with minimal operational overhead.

''Use AWS Glue Workflows to orchestrate multiple ETL jobs in sequence or in parallel, supporting conditional triggers and dependency management.''

-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Amazon Redshift supports Role-Based Access Control (RBAC) to manage access to database objects. RBAC allows administrators to create roles for job functions and assign privileges at the schema, table, or column level based on data sensitivity and user roles.

''RBAC in Amazon Redshift helps manage permissions more efficiently at scale by assigning users to roles that reflect their job function. It simplifies user management and secures access based on job role and data sensitivity.''

-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

RBAC is preferred over RLS or CLS alone because it offers a more comprehensive and scalable solution across multiple users and permissions.