AWS Certified Data Engineer - Associate Practice Exam
AWS Certified Data Engineer - Associate Practice Exam
AWS Certified Data Engineer - Associate Practice Exam
The AWS Certified Data Engineer - Associate exam validates your expertise in designing, developing, implementing, and maintaining data pipelines and managing data storage and analytics solutions on the Amazon Web Services (AWS) platform. This globally recognized credential demonstrates your ability to:
Who Should Consider This Exam:
The AWS Certified Data Engineer - Associate Exam has been developed for candidates having 2–3 years of experience in data engineering.
Data engineers and data analysts seeking to validate their skills and knowledge on AWS data services.
Cloud architects and solutions architects looking to specialize in designing and implementing data solutions on AWS.
Individuals seeking a career focused on designing and managing data pipelines on AWS.
Key Roles and Responsibilities:
Design and develop data pipelines: Design and develop data pipelines using various AWS services like S3, Glue, Lambda, and Step Functions to automate data ingestion, transformation, and loading processes.
Choose and configure data storage services: Select and configure appropriate data storage services based on specific data requirements, including S3, DynamoDB, and Redshift.
Implement data transformations and analytics: Implement data transformations using services like Glue, Athena, and Spark, and build analytics solutions using services like Amazon QuickSight and Amazon Redshift Spectrum.
Monitor and optimize data pipelines: Monitor data pipelines for performance and errors, identify and troubleshoot issues, and optimize processes for efficiency.
Secure and manage data access: Implement security best practices to secure data access, configure user permissions, and comply with data privacy regulations.
Exam Details:
Format: Multiple-choice questions and case studies
Time Limit: 170 minutes
Languages: English
Passing Score: 720
Course Outline
The AWS Certified Data Engineer - Associate Practice Exam covers the following topics including -
Module 1: Describe Data Ingestion and Transformation (34%)
1.1: Explain Perform data ingestion.
Candidate are required to have -
Knowledge of throughput and latency characteristics for AWS services for ingesting data
Understanding data ingestion patterns (including , frequency and data history)
Ability to stream data ingestion
Skills to perform Batch data ingestion (including, scheduled ingestion, event-driven ingestion)
Overview of Replayability of data ingestion pipelines
Understanding of Stateful and stateless data transactions
Develop Skills
To read data from streaming sources
To read data from batch sources
To implement appropriate configuration options for batch ingestion
To consume data APIs Setting up schedulers using Amazon EventBridge, Apache Airflow, or time-based schedules for jobs and crawlers
To set up event triggers
To call up a Lambda function from Amazon Kinesis
To create allowlists for IP addresses to allow connections to data sources
To implement throttling and overcoming rate limits (for example, DynamoDB, Amazon RDS, Kinesis)
To manage fan-in and fan-out for streaming data distribution
1.2: Explain Transform and process data.
Candidate are required to have -
Knowledge of creating ETL pipelines based on business requirements
Understanding of volume, velocity, and variety of data (including, structured data, unstructured data)
Knowledge of cloud computing and distributed computing
Ability to use Apache Spark to process data
Understanding of intermediate data staging locations
Develop Skills
To optimize container usage for performance needs
To connect to different data sources
To integrate data from multiple sources
To optimize costs while processing data
To implement data transformation services based on requirements
To transform data between formats
To troubleshoot and debug common transformation failures and performance issues
To create data APIs to make data available to other systems by using AWS services
1.3: Explain Orchestrate data pipelines
Candidate are required to have Knowledge of -
Integrating various AWS services to create ETL pipelines
Managing Event-driven architecture
Configuring AWS services for data pipelines based on schedules or dependencies
Managing Serverless workflows
Develop Skills
To use orchestration services to build workflows for data ETL pipelines
To develop data pipelines for performance, availability, scalability, resiliency, and fault tolerance
To implement and maintaining serverless workflows
To use notification services to send alerts
1.4: Explain and apply programming concepts.
Candidates are required to have knowledge to -
Perform continuous integration and continuous delivery (CI/CD)
Manage SQL queries (related to data source queries and data transformations)
Infrastructure as code (IaC) for repeatable deployments
Managing Distributed computing\
Handling Data structures and algorithms
Optimizing SQL query
Develop Skills
To optimize code to reduce runtime for data ingestion and transformation
To configure Lambda functions to meet concurrency and performance needs
To perform SQL queries to transform data (for example, Amazon Redshift stored procedures)
To structure SQL queries to meet data pipeline requirements
To use Git commands to perform actions such as creating, updating, cloning, and branching repositories
To use the AWS Serverless Application Model (AWS SAM) to package and deploy serverless data pipelines
To use and mount storage volumes from within Lambda functions
Module 2: Describe Data Store Management (26%)
2.1: Explain Choose a data store.
Candidate should have -
Knowledge of storage platforms and its features
Knowledge of storage services and configuring specific performance demands
Understanding of data storage formats (including, .csv, .txt, Parquet)
Ability to align data storage with data migration requirements
Skills to determining the appropriate storage solution for specific access patterns
Skills to manage locks to prevent access to data
Develop Skills
To implement the suitable storage services for specific cost and performance requirements
To configure the appropriate storage services for specific access patterns and requirements
To apply storage services to appropriate use cases
integrate migration tools into data processing systems
To implement data migration or remote access methods
2.2: Explain Data Cataloging Systems
Candidates are required to have -
Knowledge to create a data catalog
Skills to classify data based on requirements
Knowledge of components of metadata and data catalogs
Build Skills
To use data catalogs to consume data from the data’s source
To build and reference a data catalog
To identify schemas and using AWS Glue crawlers to populate data catalogs
To synchronize partitions with a data catalog
To create new source or target connections for cataloging
2.3: Explain and manage the lifecycle of data
Candidate should have Knowledge of -
Suggesting suitable storage solutions to address hot and cold data requirements
Optimizing the cost of storage based on the data lifecycle
Deleting data to meet business and legal requirements
Data retention policies and archiving strategies
Protecting data with sutable resiliency and availability
Develop Skills
To perform load and unload operations to move data between Amazon S3 and Amazon Redshift
To manage S3 Lifecycle policies to change the storage tier of S3 data
To expire data when it reaches a specific age by using S3 Lifecycle policies
To manage S3 versioning and DynamoDB TTL
2.4: Explain design data models and schema evolution.
Candidate should have knowledge of -
Concepts of Data modeling
Ensuring accuracy and trustworthiness of data by using data lineage
Best practices and techniques for indexing, partitioning strategies, compression, and other data optimization techniques
Modelling structured, semi-structured, and unstructured data
Techniques of schema evolution
Build Skills in
To design schemas for Amazon Redshift, DynamoDB, and Lake Formation
To address changes to the characteristics of data
To perform schema conversion (for example, by using the AWS Schema
To manage conversion Tool [AWS SCT] and AWS DMS Schema Conversion)
To establish data lineage by using AWS tools
Module 3: Describe Data Operations and Support (22%)
3.1: Explain and automate data processing by using AWS services.
Candidates should have knowledge of -
Maintaining and troubleshooting data processing for repeatable business outcomes
Using API calls for data processing
Identifying services accept scripting
Build Skills
To orchestrate data pipelines
To troubleshoot Amazon managed workflows
To calling SDKs to access Amazon features from code
To use the features of AWS services to process data
To consume and maintaining data APIs
To prepare data transformation
To use Lambda to automate data processing
To manage events and schedulers
3.2: Explain and Analyze data by using AWS services.
Candidates should have Knowledge of -
Providing tradeoffs between provisioned services and serverless services
Running and executing SQL queries
Visualizing data for analysis
Applying cleansing techniques
Data aggregation, rolling average, grouping, and pivoting
Build Skills
To visualize data by using AWS services and tools
To verify and clean data
To use Athena to query data or to create views
To use Athena notebooks that use Apache Spark to explore data
3.3: Explain the process of maintaining and monitoring data pipelines
Candidates should have knowledge of -
Using log application data
Performance tuning using Best practices
Providing log access to AWS services
Amazon Macie, AWS CloudTrail, and Amazon CloudWatch
Build Skills
To Extract logs for audits
To deploy, log and monitor solutions for facilitating auditing and traceability
To use notifications during monitoring to send alerts
To troubleshoot performance issues
To use CloudTrail to track API calls
To troubleshoot and maintain pipelines
To use Amazon CloudWatch Logs for logging into the application data (with a focus on configuration and automation)
To analyze logs with AWS services
3.4: Explain and ensure data quality
Candidates should have knowledge of -
Implementing techniques of Data sampling
techniques to implement data skew mechanisms
Concepts of Data validation (data completeness, consistency, accuracy, and integrity) and Data profiling
Build Skills
To run data quality checks while processing the data
To define data quality rules
To investigate data consistency
Module 4: Describe Data Security and Governance (18%)
4.1: Explain to apply authentication mechanisms.
Candidates should have knowledge of -
Concepts including VPC security networking concepts
Differentiating managed services and unmanaged services
Authenticating methods (password-based, certificate-based, and role-based)
Differentiating AWS managed policies and customer managed policies
Build Skills
To update VPC security groups
To create and update IAM groups, roles, endpoints, and services
To create and rotate credentials for password management
To set up IAM roles for access
To apply IAM policies to roles, endpoints, and services
4.2: Explain and apply authorization mechanisms
Candidates should have knowledge of -
Various Authorization methods (role-based, policy-based, tag-based, and attributebased)
Principle of least privilege applicable to AWS security
Role-based access control and expected access patterns
Methods of protecting data from unauthorized access across services
Build Skills
To create custom IAM policies when a managed policy does not meet the requirements
To store application and database credentials
To provide database users, groups, and roles access and authority in a database
To manage permissions through Lake Formation
4.3: Explain and ensure data encryption and masking.
Candidates should have knowledge of -
Available Data encryption options in AWS analytics services
Differentiating client-side encryption and server-side encryption
Protecting sensitive data
Data anonymization, masking, and key salting
Build Skills
To apply data masking and anonymization according to compliance laws or company policies
To use encryption keys to encrypt or decrypt data
To configure encryption across AWS account boundaries
To enable encryption in transit for data.
4.4: Explain and prepare logs for audit
Candidates should have knowledge of -
Logging application data
Logging access to AWS services
Managing Centralized AWS logs
Build Skills
To use CloudTrail to track API calls
To use CloudWatch Logs to store application logs
To use AWS CloudTrail Lake for centralized logging queries
To analyze logs by using AWS services
To integrate various AWS services to perform logging
4.5: Explain data privacy and governance
Candidates should have Knowledge of -
Protecting personally identifiable information (PII)
Managing Data sovereignty
Build Skills
To grant permissions for data sharing
To implement PII identification
To implement data privacy strategies for preventing backups or replications of data to disallowed AWS Regions
To manage configuration changes that occurred in an account