AWS Certified Data Engineer – Associate PRACTICE EXAM

“This follow examination consists of 6 sections, every containing 65 questions, overlaying all of the matters included within the certification examination.”

The AWS Licensed Knowledge Engineer – Affiliate certification is designed to validate the talents required to work with information engineering options utilizing Amazon Internet Companies (AWS). This certification ensures that people have the experience in implementing and managing information pipelines, performing information transformation duties, and making certain information high quality, whereas leveraging AWS information companies. The course prepares candidates for the certification examination by specializing in key areas comparable to information ingestion, storage, transformation, orchestration, and safety.

Course Title: AWS Licensed Knowledge Engineer – Affiliate

Course Overview:

This course gives complete coaching for professionals aiming to attain the AWS Licensed Knowledge Engineer – Affiliate certification. It covers the basic ideas and sensible abilities required to design, construct, and preserve information options on AWS, specializing in the instruments, companies, and structure wanted to course of and handle information at scale. The course is structured across the core AWS information companies, making certain learners achieve hands-on expertise and the data needed for the certification examination.

Key Studying Aims:

1. Introduction to Knowledge Engineering on AWS
   • Perceive the position of a knowledge engineer and the core AWS companies utilized in information engineering, comparable to S3, Redshift, Kinesis, and Glue.
   • Achieve foundational data of knowledge engineering ideas, together with information pipelines, information lakes, and batch vs. real-time processing.
2. Constructing and Managing Knowledge Pipelines
   • Discover ways to ingest, rework, and cargo information utilizing AWS companies like AWS Glue, Kinesis, and Knowledge Pipeline.
   • Implement automated workflows for ETL (Extract, Remodel, Load) processes.
3. Knowledge Storage and Administration
   • Discover AWS storage choices (S3, DynamoDB, Redshift) and the way to decide on the best storage resolution based mostly on particular use instances.
   • Discover ways to construction information in numerous codecs like Parquet, Avro, and JSON for optimized querying and evaluation.
4. Knowledge Safety and Governance
   • Perceive the significance of knowledge safety in information engineering, together with encryption, entry management, and compliance with regulatory requirements.
   • Implement role-based entry management (RBAC) and insurance policies for delicate information utilizing AWS IAM and KMS.
5. Knowledge High quality Assurance
   • Implement methods for making certain information high quality, together with validation checks, error dealing with, and auditing.
   • Study information versioning, monitoring information pipelines, and troubleshooting information points in a manufacturing atmosphere.
6. Optimizing Knowledge Structure for Efficiency and Value
   • Design cost-effective and high-performance information architectures that scale with enterprise wants.
   • Leverage AWS instruments comparable to CloudWatch, Lambda, and Step Capabilities for operational effectivity.

Goal Viewers:

This course is good for information engineers, cloud professionals, and people who want to concentrate on information engineering on AWS. It’s additionally appropriate for professionals with some expertise in cloud computing or data-related roles who wish to validate their abilities and data via the AWS certification examination.

Course Conditions:

• Primary data of AWS companies and cloud computing ideas.
• Familiarity with SQL and information modeling.
• Expertise with information engineering duties comparable to information migration, processing, or working with databases is useful however not required.

Course Modules:

1. Module 1: Knowledge Engineering Foundations
   • Overview of AWS information companies
   • Introduction to Knowledge Lakes, Knowledge Warehouses, and ETL pipelines
2. Module 2: Ingesting Knowledge with AWS
   • Knowledge ingestion utilizing Kinesis, Glue, and S3
   • Working with streaming and batch information processing
3. Module 3: Knowledge Transformation and ETL Workflows
   • Creating and managing ETL workflows utilizing AWS Glue and Lambda
   • Knowledge transformation greatest practices and instruments
4. Module 4: Knowledge Storage and Question Optimization
   • Knowledge storage methods with Redshift, S3, DynamoDB
   • Question optimization methods and efficiency tuning
5. Module 5: Safety and Compliance in Knowledge Engineering
   • Implementing information encryption and safe entry management
   • Managing compliance utilizing AWS companies
6. Module 6: Knowledge Pipeline Administration and Monitoring
   • Orchestrating information pipelines utilizing Step Capabilities, Glue, and Lambda
   • Monitoring and troubleshooting information workflows with CloudWatch and CloudTrail