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Amazon Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Data Operations and Support22%- Monitor data pipelines
  • 1. Amazon CloudWatch
  • 2. AWS CloudTrail
  • 3. Logging and metrics
- Automate data pipelines
  • 1. Event-driven triggers
  • 2. Scheduling jobs
  • 3. AWS Lambda triggers
- Manage and troubleshoot data processes
  • 1. Performance tuning
  • 2. Debugging failed jobs
  • 3. Cost optimization
Data Ingestion and Transformation34%- Apply programming concepts
  • 1. SQL, Python, Scala
  • 2. Infrastructure as Code (IaC)
  • 3. Version control
- Transform and process data
  • 1. ETL/ELT patterns
  • 2. Data partitioning and compression
  • 3. Batch and stream processing
  • 4. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda)
- Orchestrate data pipelines
  • 1. Event-driven architectures
  • 2. AWS Glue Workflows
  • 3. AWS Step Functions
  • 4. Amazon Managed Workflows for Apache Airflow (MWAA)
- Perform data ingestion
  • 1. Batch data ingestion (scheduled ingestion, event-driven ingestion)
  • 2. Streaming data ingestion
  • 3. Replayability of data
  • 4. Throughput and latency characteristics for AWS services
  • 5. Data ingestion patterns (frequency and data history)
Data Security and Governance18%- Ensure data encryption
  • 1. AWS KMS
  • 2. Encryption at rest and in transit
- Manage data privacy and compliance
  • 1. Data masking and tokenization
  • 2. AWS Lake Formation permissions
  • 3. PII data handling
- Apply authentication and authorization
  • 1. Service control policies (SCPs)
  • 2. AWS IAM policies and roles
  • 3. Amazon S3 bucket policies
- Implement data quality checks
  • 1. AWS Glue DataBrew
  • 2. Data validation
Data Store Management26%- Design data models
  • 1. Schema design
  • 2. Normalization and denormalization
  • 3. Partitioning and indexing strategies
- Manage data lifecycle
  • 1. Data archiving
  • 2. Data retention policies
  • 3. Amazon S3 storage classes
- Understand data cataloging
  • 1. AWS Glue Data Catalog
  • 2. Schema evolution
  • 3. Data discovery and classification
- Choose a data store
  • 1. Data lakes vs. data warehouses
  • 2. Access and storage patterns
  • 3. Data characteristics (structured, semi-structured, unstructured)
  • 4. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q42-Q47):

NEW QUESTION # 42
A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.
The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Problem Analysis:
The company requires a centralized data warehouse for consolidating data from various sources.
They use Amazon QuickSight in direct query mode, necessitating fast response times for analytical queries.
Users query the data intermittently, with unpredictable spikes during the day.
Operational overhead should be minimal.
Key Considerations:
The solution must support fast, SQL-based analytics.
It must handle unpredictable spikes efficiently.
Must integrate seamlessly with QuickSight for direct querying.
Minimize operational complexity and scaling concerns.
Solution Analysis:
Option A: Amazon Redshift Serverless
Redshift Serverless eliminates the need for provisioning and managing clusters.
Automatically scales compute capacity up or down based on query demand.
Reduces operational overhead by handling performance optimization.
Fully integrates with Amazon QuickSight, ensuring low-latency analytics.
Reduces costs as it charges only for usage, making it ideal for workloads with intermittent spikes.
Option B: Amazon Athena with S3 (Apache Parquet)
Athena supports querying data directly from S3 in Parquet format.
While it's cost-effective, performance depends on the size and complexity of the data.
It is not optimized for high-speed analytics needed by QuickSight in direct query mode.
Option C: Amazon Redshift Provisioned Clusters
Requires manual cluster provisioning, scaling, and maintenance.
Higher operational overhead compared to Redshift Serverless.
Option D: Amazon Aurora PostgreSQL
Aurora is optimized for transactional databases, not data warehousing or analytics.
Does not meet the requirement for fast analytics queries.
Final Recommendation:
Amazon Redshift Serverless is the best choice for this use case because it provides fast analytics, integrates natively with QuickSight, and minimizes operational complexity while efficiently handling unpredictable spikes.
Reference:
Amazon Redshift Serverless Overview
Amazon QuickSight and Redshift Integration


NEW QUESTION # 43
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

Answer: A


NEW QUESTION # 44
A company uses an on-premises Microsoft SQL Server database to store financial transaction dat a. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently.
The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database.
Which AWS service should the company use to meet these requirements?

Answer: D

Explanation:
AWS Database Migration Service (AWS DMS) is a cloud service that makes it possible to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores to AWS quickly, securely, and with minimal downtime and zero data loss1. AWS DMS supports migration between 20-plus database and analytics engines, such as Microsoft SQL Server to Amazon RDS for SQL Server2. AWS DMS takes over many of the difficult or tedious tasks involved in a migration project, such as capacity analysis, hardware and software procurement, installation and administration, testing and debugging, and ongoing replication and monitoring1. AWS DMS is a cost-effective solution, as you only pay for the compute resources and additional log storage used during the migration process2. AWS DMS is the best solution for the company to migrate the financial transaction data from the on-premises Microsoft SQL Server database to AWS, as it meets the requirements of minimal downtime, zero data loss, and low cost.
Option A is not the best solution, as AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers, but it does not provide any built-in features for database migration. You would have to write your own code to extract, transform, and load the data from the source to the target, which would increase the operational overhead and complexity.
Option C is not the best solution, as AWS Direct Connect is a service that establishes a dedicated network connection from your premises to AWS, but it does not provide any built-in features for database migration. You would still need to use another service or tool to perform the actual data transfer, which would increase the cost and complexity.
Option D is not the best solution, as AWS DataSync is a service that makes it easy to transfer data between on-premises storage systems and AWS storage services, such as Amazon S3, Amazon EFS, and Amazon FSx for Windows File Server, but it does not support Amazon RDS for SQL Server as a target. You would have to use another service or tool to migrate the data from Amazon S3 to Amazon RDS for SQL Server, which would increase the latency and complexity. Reference:
Database Migration - AWS Database Migration Service - AWS
What is AWS Database Migration Service?
AWS Database Migration Service Documentation
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 45
A retail company stores customer data in an Amazon S3 bucket. Some of the customer data contains personally identifiable information (PII) about customers. The company must not share PII data with business partners.
A data engineer must determine whether a dataset contains PII before making objects in the dataset available to business partners.
Which solution will meet this requirement with the LEAST manual intervention?

Answer: A

Explanation:
Explanation: Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS, such as PII. By configuring Macie for automated sensitive data discovery, the company can minimize manual intervention while ensuring PII is identified before data is shared.


NEW QUESTION # 46
A company is using an AWS Transfer Family server to migrate data from an on-premises environment to AWS. Company policy mandates the use of TLS 1.2 or above to encrypt the data in transit.
Which solution will meet these requirements?

Answer: A


NEW QUESTION # 47
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