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| Section | Weight | Objectives |
|---|---|---|
| Data Security and Governance | 18% | - Encrypt data at rest and in transit - Protect sensitive data - Enforce compliance and data governance
|
| Data Ingestion and Transformation | 34% | - Ingest data from various sources
|
| Data Operations and Support | 22% | - Automate operational tasks - Ensure reliability and scalability - Backup, restore, and disaster recovery - Monitor and troubleshoot data pipelines
|
| Data Store Management | 26% | - Manage data lifecycle and storage tiers - Optimize storage performance and cost - Design and implement data storage solutions
|
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NEW QUESTION # 14
A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1.
Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, and scale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2.
Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3.
The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages:
It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code.
It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website.
It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function.
Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster.
This solution has the following disadvantages:
It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate.
This increases the operational complexity and cost of running your code.
It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture.
Option C is incorrect because it requires you to deploy a custom Python script that can integrate with API Gateway on Amazon EKS. This solution has the following disadvantages:
It requires you to provision, manage, and scale your own EKS cluster, either using EC2 instances or Fargate.
This increases the operational complexity and cost of running your code.
It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
It requires you to configure your EKS cluster to integrate with API Gateway, either using an Application Load Balancer, a Network Load Balancer, or a service of type LoadBalancer. This adds another layer of complexity to your architecture.
Option D is incorrect because it requires you to create an AWS Lambda function and ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. This solution has the following disadvantages:
It does not guarantee that your Lambda function will always be warm, as Lambda may scale down your function if it does not receive any requests for a long period of time. This may cause cold start delays when your function is invoked by API Gateway.
It incurs unnecessary costs, as you pay for the compute time of your Lambda function every time it is invoked by the EventBridge rule, even if it does not perform any useful work1.
1: AWS Lambda - Features
2: Amazon Elastic Container Service - Features
3: Amazon Elastic Kubernetes Service - Features
[4]: Building API Gateway REST API with Lambda integration - Amazon API Gateway
[5]: Improving latency with Provisioned Concurrency - AWS Lambda
[6]: Integrating Amazon ECS with Amazon API Gateway - Amazon Elastic Container Service
[7]: Integrating Amazon EKS with Amazon API Gateway - Amazon Elastic Kubernetes Service
[8]: Managing concurrency for a Lambda function - AWS Lambda
NEW QUESTION # 15
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?
Answer: D
Explanation:
Apache Parquet is a columnar storage format that is much more space-efficient than row-based formats like CSV, especially for analytics workloads. Transforming data from CSV to Parquet significantly reduces storage costs and improves query performance. According to the study guide:
"Parquet is a columnar storage file format that is optimized for use with analytics workloads, providing efficient storage and fast query performance."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf By switching to Parquet, the company can reduce both storage size and retrieval times, making it the optimal choice for cost-effective data analysis.
NEW QUESTION # 16
A data engineer is designing a log table for an application that requires continuous ingestion. The application must provide dependable API-based access to specific records from other applications. The application must handle more than 4,000 concurrent write operations and 6,500 read operations every second.
Answer: C
Explanation:
For low-latency, high-throughput workloads with API-based access and predictable reads/writes, Amazon DynamoDB is the optimal choice. It scales automatically, supports thousands of read/write operations per second, and offers fully managed API-driven access.
"Amazon DynamoDB provides consistent, single-digit millisecond latency for high-traffic applications and scales seamlessly to handle thousands of concurrent reads and writes."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
NEW QUESTION # 17
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? (Select TWO.)
Answer: B,E
Explanation:
Problem Analysis:
The company needs cross-account access to allow QuickSight in BI-Account to interact with an S3 bucket in Hub-Account.
The bucket is encrypted with an AWS KMS key.
Appropriate permissions must be set for both S3 access and KMS decryption.
Key Considerations:
QuickSight requires IAM permissions to access S3 data and decrypt files using the KMS key.
Both S3 and KMS permissions need to be properly configured across accounts.
Solution Analysis:
Option A: Use Existing KMS Key for Encryption
While the existing KMS key is used for encryption, it must also grant decryption permissions to QuickSight.
Option B: Add S3 Bucket to QuickSight Role
Granting S3 bucket access to the QuickSight service role is necessary for cross-account access.
Option C: AWS RAM for Bucket Sharing
AWS RAM is not required; bucket policies and IAM roles suffice for granting cross-account access.
Option D: IAM Policy for KMS Access
QuickSight's service role in BI-Account needs explicit permissions to use the KMS key for decryption.
Option E: Add KMS Key as Resource for Role
The KMS key must explicitly list the QuickSight role as an entity that can access it.
Implementation Steps:
S3 Bucket Policy in Hub-Account:Add a policy to the S3 bucket granting the QuickSight service role access:
json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam::<BI-Account-ID>:role/service-role/QuickSightRole" },
"Action": "s3:GetObject",
"Resource": "arn:aws:s3:::<Bucket-Name>/*"
}
]
}
KMS Key Policy in Hub-Account:Add permissions for the QuickSight role:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam::<BI-Account-ID>:role/service-role/QuickSightRole" },
"Action": [
"kms:Decrypt",
"kms:DescribeKey"
],
"Resource": "*"
}
]
}
IAM Policy for QuickSight Role in BI-Account:Attach the following policy to the QuickSight service role:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"kms:Decrypt"
],
"Resource": [
"arn:aws:s3:::<Bucket-Name>/*",
"arn:aws:kms:<region>:<Hub-Account-ID>:key/<KMS-Key-ID>"
]
}
]
}
Setting Up Cross-Account S3 Access
AWS KMS Key Policy Examples
Amazon QuickSight Cross-Account Access
NEW QUESTION # 18
A banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.
A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.
Which solution will meet this requirement?
Answer: D
Explanation:
For exactly-once delivery and processing in Amazon Kinesis Data Streams, the best approach is to design the application so that it handlesidempotency. By embedding aunique IDin each record, the application can identify and remove duplicate records during processing.
* Exactly-Once Processing:
* Kinesis Data Streams does not natively support exactly-once processing. Therefore,idempotency should be designed into the application, ensuring that each record has a unique identifier so that the same event is processed only once, even if it is ingested multiple times.
* This pattern is widely used for achieving exactly-once semantics in distributed systems.
Reference:Building Idempotent Applications with Kinesis
Alternatives Considered:
B (Checkpoint configuration): While updating the checkpoint configuration can help with some aspects of duplicate processing, it is not a full solution for exactly-once delivery.
C (Design data source): Ensuring events are not ingested multiple times is ideal, but network outages can make this difficult, and it doesn't guarantee exactly-once delivery.
D (Using EMR): While using EMR with Flink or Spark could work, it introduces unnecessary complexity compared to handling idempotency at the application level.
References:
Amazon Kinesis Best Practices for Exactly-Once Processing
Achieving Idempotency with Amazon Kinesis
NEW QUESTION # 19
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