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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
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NEW QUESTION # 206
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data. The data engineer needs to improve the data encoding for the tables that have sub- optimal encoding.
Which solution will meet this requirement?
Answer: C
Explanation:
Option B is correct because ANALYZE COMPRESSION is the Amazon Redshift command specifically used to evaluate existing table data and recommend better compression encodings for columns. AWS states that ANALYZE COMPRESSION performs compression analysis and produces a report with the suggested compression encoding for the tables analyzed. AWS also states that when you already have data in an existing table, you can use ANALYZE COMPRESSION to view the recommended encodings for that table.
Option A is incorrect because the standard ANALYZE command updates optimizer statistics; it does not recommend column compression settings. Options C and D are vacuum operations related to sorting, reclaiming space, or clustering behavior, not choosing better compression encodings. AWS also notes that Redshift supports automatic encoding management with ENCODE AUTO, but when the question asks how to improve encoding for existing suboptimal tables, the direct diagnostic command is ANALYZE COMPRESSION.
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NEW QUESTION # 207
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud.
It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.
The other options are not optimal for the following reasons:
* B. Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can helpwith data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.
* C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .
* D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .
1: AWS Data Exchange User Guide
2: AWS Data Exchange FAQs
3: AWS Glue Developer Guide
4: AWS CodeCommit User Guide
5: Amazon Kinesis Data Streams Developer Guide
6: Amazon Elastic Container Registry User Guide
7: Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source
NEW QUESTION # 208
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand.
Which solution will meet these requirements?
Answer: B
Explanation:
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.
NEW QUESTION # 209
A transportation company wants to track vehicle movements by capturing geolocation records. The records are 10 bytes in size. The company receives up to 10,000 records every second. Data transmission delays of a few minutes are acceptable because of unreliable network conditions.
The transportation company wants to use Amazon Kinesis Data Streams to ingest the geolocation data. The company needs a reliable mechanism to send data to Kinesis Data Streams. The company needs to maximize the throughput efficiency of the Kinesis shards.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: D
Explanation:
* Problem Analysis:
* The company ingests geolocation records (10 bytes each) at 10,000 records per second into Kinesis Data Streams.
* Data transmission delays are acceptable, but the solution must maximize throughput efficiency.
* Key Considerations:
* TheKinesis Producer Library (KPL)batches records and uses aggregation to optimize shard throughput.
* Efficiently handles high-throughput scenarios with minimal operational overhead.
* Solution Analysis:
* Option A: Kinesis Agent
* Designed for file-based ingestion; not optimized for geolocation records.
* Option B: KPL
* Aggregates records into larger payloads, significantly improving shard throughput.
* Suitable for applications generating small, high-frequency records.
* Option C: Kinesis Firehose
* Firehose is for delivery to destinations like S3 or Redshift and is not optimized for direct ingestion to Kinesis Data Streams.
* Option D: Kinesis SDK
* The SDK lacks advanced features like aggregation, resulting in lower throughput efficiency.
* Final Recommendation:
* UseKinesis Producer Library (KPL)for its built-in aggregation and batching capabilities.
:
Kinesis Producer Library (KPL) Overview
Best Practices for Amazon Kinesis
NEW QUESTION # 210
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?
Answer: A
Explanation:
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.
Reference:
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
NEW QUESTION # 211
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