Amazon DEA-C01 Practice Test - Questions Answers, Page 10

The company's Apache Spark ETL job on Amazon EMR uses high CPU but low memory, meaning that compute-optimized EC2 instances would be the most cost-effective choice. These instances are designed for high-performance compute applications, where CPU usage is high, but memory needs are minimal, which is exactly the case here.

Compute Optimized Instances:

Compute-optimized instances, such as the C5 series, provide a higher ratio of CPU to memory, which is more suitable for jobs with high CPU usage and relatively low memory consumption.

Switching from general-purpose EC2 instances to compute-optimized instances can reduce costs while improving performance, as these instances are optimized for workloads like Spark jobs that perform a lot of computation.

Managed Scaling: The EMR cluster's scaling is currently managed between 1 and 5 nodes, so changing the instance type will leverage the current scaling strategy but optimize it for the workload.

Alternatives Considered:

A (Increase task nodes to 10): Increasing the number of task nodes would increase costs without necessarily improving performance. Since memory usage is low, the bottleneck is more likely the CPU, which compute-optimized instances can handle better.

B (Memory optimized instances): Memory-optimized instances are not suitable since the current job is CPU-bound, and memory usage remains low (under 30%).

D (Reduce scaling cooldown): This could marginally improve scaling speed but does not address the need for cost optimization and improved CPU performance.

Amazon EMR Cluster Optimization

Compute Optimized EC2 Instances

In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to use IAM roles for service accounts (IRSA), which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.

IAM Roles for Service Accounts (IRSA):

With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.

This ensures least privilege access, improving security by preventing credentials from being embedded in the containers.

Alternatives Considered:

A (Storing AWS credentials in S3): Storing AWS credentials in S3 and retrieving them introduces security risks and violates the principle of not embedding credentials.

C (IAM user access keys in environment variables): This also embeds credentials, which is not recommended.

D (Kubernetes secrets): Storing user access keys as secrets is an option, but it still involves handling long-term credentials manually, which is less secure than using IRSA.

IAM Best Practices for Amazon EKS

Secure Access to DynamoDB from EKS

The most efficient and low-operational-overhead solution for ingesting data into Amazon Redshift from Amazon Kinesis Data Streams is to use Amazon Redshift streaming ingestion. This feature allows Redshift to directly ingest streaming data from Kinesis Data Streams and process it in real-time.

Amazon Redshift Streaming Ingestion:

Redshift supports native streaming ingestion from Kinesis Data Streams, allowing real-time data to be queried using materialized views.

This solution reduces operational complexity because you don't need intermediary services like Amazon Kinesis Data Firehose or S3 for batch loading.

Alternatives Considered:

A (Data Firehose to Redshift): This option is more suitable for batch processing but incurs additional operational overhead with the Firehose setup.

B (Firehose to S3): This involves an intermediate step, which adds complexity and delays the real-time requirement.

C (Managed Service for Apache Flink): This would work but introduces unnecessary complexity compared to Redshift's native streaming ingestion.

Amazon Redshift Streaming Ingestion from Kinesis

Materialized Views in Redshift

Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers the least operational effort by avoiding complex data movement and transformation processes.

Amazon Athena Federated Queries:

Athena's federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena's support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.

The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.

Alternatives Considered:

A (AWS Glue pipeline): This would work but requires more operational effort to manage and transform the data in AWS Glue.

C (Amazon EMR): Using EMR and writing PySpark code introduces more operational overhead and complexity compared to a SQL-based solution in Athena.

D (Amazon AppFlow): AppFlow is more suitable for transferring data between services but is not as efficient for transformations and joins as Athena federated queries.

Amazon Athena Documentation

Federated Queries in Amazon Athena

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 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 requirement is to update the AWS Glue Data Catalog incrementally based on S3 events. Using an S3 event-based approach is the most automated and operationally efficient solution.

A . Create an S3 event-based AWS Glue crawler:

An event-based Glue crawler can automatically update the Data Catalog when new data arrives in the S3 bucket. This ensures incremental updates with minimal operational overhead.

C . Use an AWS Lambda function to directly update the Data Catalog:

Lambda can be triggered by S3 events delivered to the SQS queue and can directly update the Glue Data Catalog, ensuring that new data is reflected in near real-time without running a full crawler.

Alternatives Considered:

B (Time-based schedule): Scheduling a crawler to run periodically adds unnecessary latency and operational overhead.

D (Manual crawler initiation): Manually starting the crawler defeats the purpose of automation.

E (AWS Step Functions): Step Functions add complexity that is not needed when Lambda can handle the updates directly.

AWS Glue Event-Driven Crawlers

Using AWS Lambda to Update Glue Catalog

To avoid duplicate records in Amazon Redshift, the most effective solution is to perform the ETL in a way that first loads the data into a staging table and then uses SQL commands like MERGE or UPDATE to insert new records and update existing records without introducing duplicates.

Using Staging Tables in Redshift:

The AWS Glue job can write data to a staging table in Redshift. Once the data is loaded, SQL commands can be executed to compare the staging data with the target table and update or insert records appropriately. This ensures no duplicates are introduced during re-runs of the Glue job.

Alternatives Considered:

B (MySQL upsert): This introduces unnecessary complexity by involving another database (MySQL).

C (Spark dropDuplicates): While Spark can eliminate duplicates, handling duplicates at the Redshift level with a staging table is a more reliable and Redshift-native solution.

D (AWS Glue ResolveChoice): The ResolveChoice transform in Glue helps with column conflicts but does not handle record-level duplicates effectively.

Amazon Redshift MERGE Statements

Staging Tables in Amazon Redshift

The RootDiskUsed metric for the MSK cluster indicates that the storage on the broker is reaching its capacity. The best solution is to expand the storage of the MSK broker and enable automatic storage expansion to prevent future alarms.

Expand MSK Broker Storage:

AWS Managed Streaming for Apache Kafka (MSK) allows you to expand the broker storage to accommodate growing data volumes. Additionally, auto-expansion of storage can be configured to ensure that storage grows automatically as the data increases.

Alternatives Considered:

B (Expand Zookeeper storage): Zookeeper is responsible for managing Kafka metadata and not for storing data, so increasing Zookeeper storage won't resolve the root disk issue.

C (Update instance type): Changing the instance type would increase computational resources but not directly address the storage problem.

D (Target-Volume-in-GiB): This parameter is irrelevant for the existing topic and will not solve the storage issue.

Amazon MSK Storage Auto Scaling