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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q77-Q82):

NEW QUESTION # 77
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: C

Explanation:
The best solution for managing SSL/TLS certificates on EC2 instances withminimal operational overheadis to useAWS Certificate Manager (ACM). ACM simplifies certificate management by automating the provisioning, renewal, and deployment of certificates.
* AWS Certificate Manager (ACM):
* ACM managesSSL/TLS certificatesfor 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:AWS Certificate Manager
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.
References:
AWS Certificate Manager Documentation


NEW QUESTION # 78
A company stores a 100 MB dataset in an Amazon S3 bucket as an Apache Parquet file. A data engineer needs to profile the data before performing data preparation steps on the data.
Which solution will meet this requirement in the MOST operationally efficient way?

Answer: C


NEW QUESTION # 79
A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.
Which solution will meet these requirements with the LEAST ongoing maintenance?

Answer: C

Explanation:
The requirement is to manage the size of an Amazon DynamoDB table by automatically deleting data older than 1 month without disrupting ongoing read or write operations. The simplest and most maintenance-free solution is to use DynamoDB Time-to-Live (TTL).
Option A: Use the DynamoDB TTL feature to automatically expire data based on timestamps.DynamoDB TTL allows you to specify an attribute (e.g., a timestamp) that defines when items in the table should expire.
After the expiration time, DynamoDB automatically deletes the items, freeing up storage space and keeping the table size under control without manual intervention or disruptions to ongoing operations.
Other options involve higher maintenance and manual scheduling or scanning operations, which increase complexity unnecessarily compared to the native TTL feature.
References:
DynamoDB Time-to-Live (TTL)


NEW QUESTION # 80
A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies.
The sales table for the first company is named Table S1 and contains 10 billion records. The sales table for the second company is named Table S2 and contains 900 million records. The query becomes slow after the modification.
A data engineer must improve the query performance.
Which solutions will meet these requirements? (Select TWO)

Answer: B,D

Explanation:
Amazon Redshift query performance for large joins depends heavily on data distribution and query optimization. Using the KEY distribution style on both tables ensures that rows with the same join key are co-located on the same compute nodes, minimizing costly data redistribution during joins.
Selecting a high-cardinality column as the distribution key is critical to evenly distribute data across nodes and prevent data skew. Low-cardinality keys can cause hotspots where too much data resides on a small number of nodes, degrading performance.
Using EVEN distribution on the 10-billion-row table and ALL distribution on the 900-million-row table is inefficient because ALL distribution replicates the entire table to every node, which is not appropriate for large datasets. The Amazon Redshift query optimizer operates automatically and does not require manual selection.
Amazon Redshift Advisor analyzes workload patterns and provides actionable recommendations for distribution styles, sort keys, and query optimizations. Using Redshift Advisor allows the data engineer to apply proven optimizations based on actual cluster usage.
Therefore, combining KEY distribution with a high-cardinality join column and leveraging Amazon Redshift Advisor recommendations is the most effective solution to improve query performance.


NEW QUESTION # 81
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Which solution will meet this requirement with the LEAST coding effort?

Answer: D

Explanation:
The NEST TO MAP transformation allows you to combine multiple columns into a single column that contains a JSON object with key-value pairs. This is the easiest way to achieve the desired format for the physical address data, as you can simply select the columns to nest and specify the keys for each column. The NEST TO ARRAY transformation creates a single column that contains an array of values, which is not the same as the JSON object format. The PIVOT transformation reshapes the data by creating new columns from unique values in a selected column, which is not applicable for this use case. Writing a Lambda function in Python requires more coding effort than using AWS Glue DataBrew, which provides a visual and interactive interface for data transformations. References:
7 most common data preparation transformations in AWS Glue DataBrew (Section: Nesting and unnesting columns) NEST TO MAP - AWS Glue DataBrew (Section: Syntax)


NEW QUESTION # 82
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