Amazon-DEA-C01 Exam Dumps

This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures.Reference:

Amazon Managed Service for Apache Flink

Apache Flink

Window Aggregations in Flink

The AWS Transfer Family server's security policy can be updated to enforce TLS 1.2 or higher, ensuring compliance with company policy for encrypted data transfers.

AWS Transfer Family Security Policy:

AWS Transfer Family supports setting a minimum TLS version through its security policy configuration. This ensures that only connections using TLS 1.2 or above are allowed.

Alternatives Considered:

A (Generate new SSH keys): SSH keys are unrelated to TLS and do not enforce encryption protocols like TLS 1.2.

B (Update security group rules): Security groups control IP-level access, not TLS versions.

D (Install SSL certificate): SSL certificates ensure secure connections, but the TLS version is controlled via the security policy.

AWS Transfer Family Documentation

The problem likely arises from Athena not being able to read from the correct S3 location or missing partitions. The two most relevant troubleshooting steps involve checking the S3 location and repairing the table metadata.

A . Confirm that Athena is pointing to the correct Amazon S3 location:

One of the most common issues with missing data in Athena queries is that the query is pointed to an incorrect or outdated S3 location. Checking the S3 path ensures Athena is querying the correct data.

C . Use the MSCK REPAIR TABLE command:

When new partitions are added to the S3 bucket without being reflected in the Glue Data Catalog, Athena queries will not return data from those partitions. The MSCK REPAIR TABLE command updates the Glue Data Catalog with the latest partitions.

Alternatives Considered:

B (Increase query timeout): Timeout issues are unrelated to missing data.

D (Restart Athena): Athena does not require restarting.

E (Delete and recreate table): This introduces unnecessary overhead when the issue can be resolved by repairing the table and confirming the S3 location.

Athena Query Fails to Return Data

The query editor v2 in Amazon Redshift is a web-based tool that allows users to run SQL queries and scripts on Amazon Redshift clusters. The query editor v2 supports creating and managing materialized views, which are precomputed results of a query that can improve the performance of subsequent queries. The query editor v2 also supports scheduling queries to run at specified intervals, which can be used to refresh materialized views automatically. This solution requires the least effort, as it does not involve any additional services, coding, or configuration. The other solutions are more complex and require more operational overhead. Apache Airflow is an open-source platform for orchestrating workflows, which can be used to refresh materialized views, but it requires setting up and managing an Airflow environment, creating DAGs (directed acyclic graphs) to define the workflows, and integrating with Amazon Redshift. AWS Lambda is a serverless compute service that can run code in response to events, which can be used to refresh materialized views, but it requires creating and deploying Lambda functions, defining UDFs within Amazon Redshift, and triggering the functions using events or schedules. AWS Glue is a fully managed ETL service that can run jobs to transform and load data, which can be used to refresh materialized views, but it requires creating and configuring Glue jobs, defining Glue workflows to orchestrate the jobs, and scheduling the workflows using triggers.Reference:

Query editor V2

Working with materialized views

Scheduling queries

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

The FLEX execution class allows you to run AWS Glue jobs on spare compute capacity instead of dedicated hardware. This can reduce the cost of running non-urgent or non-time sensitive data integration workloads, such as testing and one-time data loads. The FLEX execution class is available for AWS Glue 3.0 Spark jobs. The other options are not as cost-effective as FLEX, because they either use dedicated resources (STANDARD) or do not affect the cost at all (Spot Instance type and GlueVersion).Reference:

Introducing AWS Glue Flex jobs: Cost savings on ETL workloads

Serverless Data Integration -- AWS Glue Pricing

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 125)