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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Backup, restore, and disaster recovery - Ensure reliability and scalability |
| Topic 2: Data Security and Governance | 18% | - Enforce compliance and data governance
- Encrypt data at rest and in transit - Implement access control and authentication
|
| Topic 3: Data Store Management | 26% | - Optimize storage performance and cost - Design and implement data storage solutions
|
| Topic 4: Data Ingestion and Transformation | 34% | - Implement data quality and validation - Transform and enrich data
|
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NEW QUESTION # 30
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 # 31
A company uses an Amazon S3 bucket to integrate multiple data sources into a central data lake. The company needs to perform multiple transformations and data cleaning processes on the data to make the data accessible to business partners.
The company needs a solution that will give multiple business partners the ability to run SQL queries on the central data lake during normal business hours.
Which solution will meet these requirements MOST cost-effectively?
Answer: C
Explanation:
Option B is most cost-effective because it combines a serverless ETL service for overnight processing with a serverless warehouse for partner SQL access during business hours. The material describes AWS Glue as a
"fully managed, serverless, scalable ETL service" that can extract, transform, and load data with minimal operational overhead, which fits the requirement for multiple transformations and data cleaning.
For the SQL access layer, the study material highlights the cost advantage of Amazon Redshift Serverless: it automatically provisions and scales capacity and you pay only for the compute capacity provisioned, with no compute costs when no workloads are running. That directly supports "multiple business partners" querying during business hours while avoiding the always-on cost of a provisioned cluster.
Options C and D use a provisioned Redshift cluster, which is less cost-effective because capacity must be paid for even when partners are not querying. Option A adds EMR cluster management cost and operational overhead compared to using Glue for ETL.
NEW QUESTION # 32
A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.
Which solution will meet these requirements with the LEAST effort?
Answer: C
Explanation:
To build an enterprise data catalog with metadata for storage formats, the easiest and most efficient solution is using an AWS Glue crawler. The Glue crawler can scan Amazon S3 buckets and Amazon RDS databases to automatically create a data catalog that includes metadata such as the schema and storage format (e.g., CSV, Parquet, etc.). By using AWS Glue crawler classifiers, you can configure the crawler to recognize the format of the data and store this information directly in the catalog.
* Option B: Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.This option meets the requirements with the least effort because Glue crawlers automate the discovery and cataloging of data from multiple sources, including S3 and RDS, while recognizing various file formats via classifiers.
Other options (A, C, D) involve additional manual steps, like having data stewards inspect the data, or using services like Amazon Macie that focus more on sensitive data detection rather than format cataloging.
References:
* AWS Glue Crawler Documentation
* AWS Glue Classifiers
NEW QUESTION # 33
A data engineer manages a provisioned Amazon Redshift cluster that stores petabytes of data. The cluster receives continuous data ingestion from multiple sources. As data volume grows, the query performance has degraded. The current table design uses the AUTO distribution style and default sort keys. The data engineer needs to optimize the query performance for recent transactions.
Which solution will meet these requirements?
Answer: C
Explanation:
The correct answer is A because the workload has grown to petabyte scale, but the performance requirement is focused on recent transactions. The best architecture is to keep hot, frequently queried data inside Amazon Redshift and move historical/cold data to Amazon S3. Amazon Redshift Spectrum lets Redshift efficiently query structured and semi-structured files in S3 without loading that data into Redshift tables, and most of the data remains in S3 while Spectrum performs parallel processing. This reduces pressure on the provisioned cluster while preserving query access to historical data. Decreasing nodes would worsen performance. DISTSTYLE ALL for historical tables can increase storage and maintenance overhead. Redshift is already columnar, so switching to row-based storage is not valid.
NEW QUESTION # 34
A company has a data lake in Amazon 53. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?
Answer: C
Explanation:
Problem Analysis:
The company uses AWS Glue for ETL pipelines and must enforce data quality checks during pipeline execution.
The goal is to implement quality checks with minimal implementation effort.
Key Considerations:
AWS Glue provides an Evaluate Data Quality transform that allows for defining quality checks directly in the pipeline.
DQDL (Data Quality Definition Language) simplifies the process by allowing declarative rule definitions.
Solution Analysis:
Option A: SQL Transform
SQL queries can implement rules but require manual effort for each rule and do not integrate natively with Glue.
Option B: Evaluate Data Quality Transform + DQDL
AWS Glue's built-in Evaluate Data Quality transform is designed for this use case.
Allows defining thresholds and rules in DQDL with minimal coding effort.
Option C: Custom Transform with PyDeequ
PyDeequ is a powerful library but adds unnecessary complexity compared to Glue's native features.
Option D: Custom Transform with Great Expectations
Similar to PyDeequ, Great Expectations adds operational complexity and external dependencies.
Final Recommendation:
Use the Evaluate Data Quality transform with DQDL to implement data quality rules in AWS Glue pipelines.
AWS Glue Data Quality
DQDL Syntax and Examples
AWS Glue Studio Documentation
NEW QUESTION # 35
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