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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Store Management | 26% | - Optimize storage performance and cost - Design and implement data storage solutions
|
| Topic 2: Data Security and Governance | 18% | - Encrypt data at rest and in transit - Protect sensitive data - Enforce compliance and data governance
|
| Topic 3: Data Ingestion and Transformation | 34% | - Ingest data from various sources
- Transform and enrich data
|
| Topic 4: Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Automate operational tasks - Backup, restore, and disaster recovery |
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NEW QUESTION # 100
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 # 101
A company hosts its applications on Amazon EC2 instances. The company must use SSL/TLS connections that encrypt data in transit to communicate securely with AWS infrastructure that is managed by a customer.
A data engineer needs to implement a solution to simplify the generation, distribution, and rotation of digital certificates. The solution must automatically renew and deploy SSL/TLS certificates.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
The best solution for managing SSL/TLS certificates on EC2 instances with minimal operational overhead is to use AWS Certificate Manager (ACM). ACM simplifies certificate management by automating the provisioning, renewal, and deployment of certificates.
AWS Certificate Manager (ACM):
ACM manages SSL/TLS certificates for EC2 and other AWS resources, including automatic certificate renewal. This reduces the need for manual management and avoids operational complexity.
ACM also integrates with other AWS services to simplify secure connections between AWS infrastructure and customer-managed environments.
Reference:
Alternatives Considered:
A (Self-managed certificates): Managing certificates manually on EC2 instances increases operational overhead and lacks automatic renewal.
C (Secrets Manager automation): While Secrets Manager can store keys and certificates, it requires custom automation for rotation and does not handle SSL/TLS certificates directly.
D (ECS Service Connect): This is unrelated to SSL/TLS certificate management and would not address the operational need.
AWS Certificate Manager Documentation
NEW QUESTION # 102
A company has a data warehouse in Amazon Redshift. The Amazon Redshift provisioned cluster is created in a VPC. The company is developing a new application in AWS Lambda that needs to access the data from Amazon Redshift. The company security policy states that AWS services can access the Amazon Redshift cluster only from the AWS network. Traffic between Lambda and the Amazon Redshift Data API must remain in the AWS network.
Which solution will meet these requirements?
Answer: A
Explanation:
The correct answer is A because the requirement specifically says that traffic between Lambda and the Amazon Redshift Data API must remain on the AWS network. AWS documentation states that you can call the Redshift Data API with Amazon VPC endpoints and that this keeps traffic between applications in the VPC and the Data API in the AWS network without public IP addresses. The Data API also removes the need to manage database drivers, persistent connections, and network configuration from Lambda. Creating a VPC endpoint for Lambda is not how Lambda privately reaches Redshift Data API. ODBC and JDBC drivers introduce connection management and do not address the stated Data API traffic requirement. Therefore, Data API plus the Redshift Data API VPC endpoint is correct.
NEW QUESTION # 103
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?
Answer: A
Explanation:
To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use anINSERT INTOstatement combined with aSELECTstatement to filter only the records where the country is 'usa'. The correct SQL syntax would be:
* Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa';This statement inserts only the cities and states where the country column has a value of
'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.
Options B, C, and Dare incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).
References:
Amazon Athena SQL Reference
Creating Tables in Athena
NEW QUESTION # 104
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?
Answer: C
Explanation:
AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don't have to manage any infrastructure.
AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines.
AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing.
Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage.
Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References:
AWS Glue Workflows
AWS Step Functions
AWS Glue Studio
Amazon MWAA
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
NEW QUESTION # 105
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