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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
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NEW QUESTION # 127
A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.
Which solution will meet these requirements with the LEAST ongoing maintenance?
Answer: C
Explanation:
The requirement is to manage the size of an Amazon DynamoDB table by automatically deleting data older than 1 month without disrupting ongoing read or write operations. The simplest and most maintenance-free solution is to use DynamoDB Time-to-Live (TTL).
Option A: Use the DynamoDB TTL feature to automatically expire data based on timestamps.
DynamoDB TTL allows you to specify an attribute (e.g., a timestamp) that defines when items in the table should expire. After the expiration time, DynamoDB automatically deletes the items, freeing up storage space and keeping the table size under control without manual intervention or disruptions to ongoing operations.
Other options involve higher maintenance and manual scheduling or scanning operations, which increase complexity unnecessarily compared to the native TTL feature.
Reference:
DynamoDB Time-to-Live (TTL)
NEW QUESTION # 128
A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key. Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Select TWO.)
Answer: D,E
Explanation:
In Amazon Redshift, a compound sort key is designed to optimize the performance of queries that use filtering and join conditions on the columns in the sort key. A compound sort key orders the data based on the first column, followed by the second, and so on. In the scenario given, the compound sort key consists of Region ID, Department ID, and Role ID. Therefore, queries that filter on the leading columns of the sort key are more likely to benefit from this order.
Option B: "Select * from Employee where Region ID='North America' and Department ID=20;" This query will perform well because it uses both the Region ID and Department ID, which are the first two columns of the compound sort key. The order of the columns in the WHERE clause matches the order in the sort key, thus allowing the query to scan fewer rows and improve performance.
Option C: "Select * from Employee where Department ID=20 and Region ID='North America';" This query also benefits from the compound sort key because it includes both Region ID and Department ID, which are the first two columns in the sort key. Although the order in the WHERE clause does not match exactly, Amazon Redshift will still leverage the sort key to reduce the amount of data scanned, improving query speed.
Options A, D, and E are less optimal because they do not utilize the sort key as effectively:
Option A only filters by the Region ID, which may still use the sort key but does not take full advantage of the compound nature.
Option D uses only Role ID, the last column in the compound sort key, which will not benefit much from sorting since it is the third key in the sort order.
Option E filters on Region ID and Role ID but skips the Department ID column, making it less efficient for the compound sort key.
Reference:
Amazon Redshift Documentation - Sorting Data
AWS Certified Data Analytics Study Guide
AWS Certification - Data Engineer Associate Exam Guide
NEW QUESTION # 129
A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.
Which actions will provide the FASTEST queries? (Choose two.)
Answer: A,C
NEW QUESTION # 130
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: A
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 # 131
A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.
Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?
Answer: D
Explanation:
To migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region (Account_A) to an Amazon Redshift cluster in the eu-west-1 Region (Account_B), AWS DMS needs a replication instance located in the target region (in this case, eu-west-1) to facilitate the data transfer between regions.
* Option A: Set up an AWS DMS replication instance in Account_B in eu-west-1.Placing the DMS replication instance in the target account and region (Account_B in eu-west-1) is the most efficient solution. The replication instance can connect to the source RDS PostgreSQL in eu-east-1 and migrate the data to the Redshift cluster in eu-west-1. This setup ensures data is replicated across AWS accounts and regions.
Options B, C, and D place the replication instance in either the wrong account or region, which increases complexity without adding any benefit.
References:
* AWS Database Migration Service (DMS) Documentation
* Cross-Region and Cross-Account Replication
NEW QUESTION # 132
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