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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Ensure reliability and scalability - Automate operational tasks - Backup, restore, and disaster recovery - Monitor and troubleshoot data pipelines
|
| Data Store Management | 26% | - Optimize storage performance and cost - Manage data lifecycle and storage tiers - Design and implement data storage solutions
|
| Data Security and Governance | 18% | - Protect sensitive data - Implement access control and authentication
- Enforce compliance and data governance
|
| Data Ingestion and Transformation | 34% | - Implement data quality and validation - Transform and enrich data
|
>> Reliable Data-Engineer-Associate Exam Materials <<
The Amazon sector is an ever-evolving and rapidly growing industry that is crucial in shaping our lives today. With the growing demand for skilled Amazon professionals, obtaining AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) certification exam has become increasingly important for those who are looking to advance their careers and stay competitive in the job market. Individuals who hold AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) certification exam demonstrate to their employers and clients that they have the knowledge and skills necessary to succeed in the Data-Engineer-Associate exam.
NEW QUESTION # 272
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Changing the volume type of the existing gp2 volumes to gp3 is the easiest and fastest way to migrate to the new storage type without any downtime or data loss. You can use the AWS Management Console, the AWS CLI, or the Amazon EC2 API to modify the volume type, size, IOPS, and throughput of your gp2 volumes.
The modification takes effect immediately, and you can monitor the progress of the modification using CloudWatch. The other options are either more complex or require additional steps, such as creating snapshots, transferring data, or attaching new volumes, which can increase the operational overhead and the risk of errors. References:
Migrating Amazon EBS volumes from gp2 to gp3 and save up to 20% on costs (Section: How to migrate from gp2 to gp3) Switching from gp2 Volumes to gp3 Volumes to Lower AWS EBS Costs (Section: How to Switch from GP2 Volumes to GP3 Volumes) Modifying the volume type, IOPS, or size of an EBS volume - Amazon Elastic Compute Cloud (Section:
Modifying the volume type)
NEW QUESTION # 273
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?
Answer: C
Explanation:
This solution will meet the requirements with the lowest latency because it uses Amazon Managed Service for Apache Flink to process the sensor data in real time and write it to Amazon Timestream, a fast, scalable, and serverless time series database. Amazon Timestream is optimized for storing and analyzing time series data, such as sensor data, and can handle trillions of events per day with millisecond latency. By using AmazonTimestream as a source, you can create an Amazon QuickSight dashboard that displays a real-time view of operational efficiency on a large screen in the manufacturing facility. Amazon QuickSight is a fully managed business intelligence service that can connect to various data sources, including Amazon Timestream, and provide interactive visualizations and insights123.
The other options are not optimal for the following reasons:
A: Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is similar to option C, but it uses Grafana instead of Amazon QuickSight to create the dashboard.
Grafana is an open source visualization tool that can also connect to Amazon Timestream, but it requires additional steps to set up and configure, such as deploying a Grafana server on Amazon EC2, installing the Amazon Timestream plugin, and creating an IAM role for Grafana to access Timestream. These steps can increase the latency and complexity of the solution.
B: Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard. This option is not suitable for displaying a real-time view of operational efficiency, as it introduces unnecessary delays and costs in the data pipeline. First, the sensor data is written to an S3 bucket by Amazon Kinesis Data Firehose, which can have a buffering interval of up to 900 seconds. Then, the S3 bucket sends a notification to a Lambda function, which can incur additional invocation and execution time. Finally, the Lambda function publishes the data to Amazon Aurora, a relational database that is not optimized for time series data and can have higher storage and performance costs than Amazon Timestream .
D: Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
This option is also not suitable for displaying a real-time view of operational efficiency, as it uses AWS Glue bookmarks to read sensor data from the S3 bucket. AWS Glue bookmarks are a feature that helps AWS Glue jobs and crawlers keep track of the data that has already been processed, so that they can resume from where they left off. However, AWS Glue jobs and crawlers are not designed for real-time data processing, as they can have a minimum frequency of 5 minutes and a variable start-up time.
Moreover, this option also uses Grafana instead of Amazon QuickSight to create the dashboard, which can increase the latency and complexity of the solution .
References:
1: Amazon Managed Streaming for Apache Flink
2: Amazon Timestream
3: Amazon QuickSight
4: Analyze data in Amazon Timestream using Grafana
5: Amazon Kinesis Data Firehose
6: Amazon Aurora
7: AWS Glue Bookmarks
8: AWS Glue Job and Crawler Scheduling
NEW QUESTION # 274
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,C
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>"
]
}
]
}
Reference:
Setting Up Cross-Account S3 Access
AWS KMS Key Policy Examples
Amazon QuickSight Cross-Account Access
NEW QUESTION # 275
The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first 30 days after the records are created. The company accesses records that are older than 30 days infrequently.
The company needs to cost-optimize its Amazon S3 storage.
Which solution will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here's why:
* Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from the S3 Standard storage class to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but still needs to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.
* Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.
* Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use of S3 Standard for that period ensures compliance with regulatory requirements. After 30 days, transitioning to S3 Standard-IA continues to meet access requirements for infrequent access while reducing storage costs.
* Alternatives Considered:
* Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data to S3 Standard- IA directly would be more cost-effective since the pattern of access is well-known (frequent for
30 days, infrequent thereafter).
* Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.
* Option D (S3 Standard-IA for all records): Using S3 Standard-IA for all records would result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.
References:
* Amazon S3 Lifecycle Policies
* S3 Storage Classes
* Cost Management and Data Optimization Using Lifecycle Policies
* AWS Data Engineering Documentation
NEW QUESTION # 276
A company generates reports from 30 tables in an Amazon Redshift data warehouse. The data source is an operational Amazon Aurora MySQL database that contains 100 tables. Currently, the company refreshes all data from Aurora to Redshift every hour, which causes delays in report generation.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)
Answer: C,D
Explanation:
* Option A (AWS DMS): Lets you replicate only selected tables from Aurora to Redshift. It supports ongoing replication (CDC) and reduces unnecessary data transfers.
* Option C (Zero-ETL with Aurora to Redshift): This new integration allows real-time, serverless, and low-maintenance data replication. It's designed to reduce operational overhead drastically.
Other options introduce manual processing or inefficient query access (Option D). Glue workflows (Option E) add unnecessary complexity and lag for near-real-time needs.
"Zero-ETL integration between Amazon Aurora and Redshift enables real-time analytics without building complex pipelines." Reference: AWS Blog - Introducing Amazon Aurora zero-ETL integration with Redshift
"With AWS DMS, you can replicate only selected tables from your database and use ongoing replication to reduce refresh latency."Reference: AWS DMS Documentation - AWS DMS Use Cases
NEW QUESTION # 277
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