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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Store Management	26%	- Choose a data store 1. Data lakes vs. data warehouses 2. Access and storage patterns 3. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift 4. Data characteristics (structured, semi-structured, unstructured) - Manage data lifecycle 1. Data retention policies 2. Amazon S3 storage classes 3. Data archiving - Design data models 1. Schema design 2. Partitioning and indexing strategies 3. Normalization and denormalization - Understand data cataloging 1. AWS Glue Data Catalog 2. Data discovery and classification 3. Schema evolution
Data Security and Governance	18%	- Ensure data encryption 1. Encryption at rest and in transit 2. AWS KMS - Apply authentication and authorization 1. AWS IAM policies and roles 2. Amazon S3 bucket policies 3. Service control policies (SCPs) - Implement data quality checks 1. Data validation 2. AWS Glue DataBrew - Manage data privacy and compliance 1. PII data handling 2. Data masking and tokenization 3. AWS Lake Formation permissions
Data Ingestion and Transformation	34%	- Apply programming concepts 1. SQL, Python, Scala 2. Version control 3. Infrastructure as Code (IaC) - Perform data ingestion 1. Streaming data ingestion 2. Throughput and latency characteristics for AWS services 3. Batch data ingestion (scheduled ingestion, event-driven ingestion) 4. Data ingestion patterns (frequency and data history) 5. Replayability of data - Transform and process data 1. Batch and stream processing 2. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda) 3. Data partitioning and compression 4. ETL/ELT patterns - Orchestrate data pipelines 1. Event-driven architectures 2. AWS Glue Workflows 3. Amazon Managed Workflows for Apache Airflow (MWAA) 4. AWS Step Functions
Data Operations and Support	22%	- Automate data pipelines 1. Scheduling jobs 2. Event-driven triggers 3. AWS Lambda triggers - Manage and troubleshoot data processes 1. Performance tuning 2. Cost optimization 3. Debugging failed jobs - Monitor data pipelines 1. Amazon CloudWatch 2. AWS CloudTrail 3. Logging and metrics

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q244-Q249):

NEW QUESTION # 244
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Select TWO.)

A. The producer experienced network-related timeouts.
B. There was a change in the number of shards, record processors, or both.
C. The AggregationEnabled configuration property was set to true.
D. The stream's value for the IteratorAgeMilliseconds metric was too high.
E. The max_records configuration property was set to a number that was too high.

Answer: A,B

Explanation:
Problem Analysis:
The company uses Kinesis Data Streams for both inventory management and reordering.
The Kinesis Producer Library (KPL) publishes data, and the Kinesis Client Library (KCL) consumes data.
Duplicate records were observed in the inventory reordering system.
Key Considerations:
Kinesis streams are designed for durability but may produce duplicates under certain conditions.
Factors such as network timeouts, shard splits, or changes in record processors can cause duplication.
Solution Analysis:
Option A: Network-Related Timeouts
If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.
Option B: High IteratorAgeMilliseconds
High iterator age suggests delays in processing but does not directly cause duplication.
Option C: Changes in Shards or Processors
Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.
Option D: AggregationEnabled Set to True
AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.
Option E: High max_records Value
A high max_records value increases batch size but does not lead to duplication.
Final Recommendation:
Network-related timeouts and changes in shards or processors are the most likely causes of duplicate data in this scenario.
Reference:
Amazon Kinesis Data Streams Best Practices
Kinesis Producer Library (KPL) Overview
Kinesis Client Library (KCL) Overview

NEW QUESTION # 245
A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company's marketing, claims, and analytics teams need to be able to access the customer data.
The marketing team should have access to obfuscated claim information but should have full access to customer contact information.
The claims team should have access to customer information for each claim that the team processes.
The analytics team should have access only to obfuscated PII data.
Which solution will enforce these data access requirements with the LEAST administrative overhead?

A. Create a separate Redshift cluster for each team. Load only the required data for each team. Restrict access to clusters based on the teams.
B. Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.
C. Create a separate Amazon Redshift database role for each team. Define masking policies that apply for each team separately. Attach appropriate masking policies to each team role.
D. Move the customer data to an Amazon S3 bucket. Use AWS Lake Formation to create a data lake. Use fine-grained security capabilities to grant each team appropriate permissions to access the data.

Answer: B

Explanation:
Step 1: Understand the Data Access Requirements
The question presents distinct access needs for three teams:
Marketing team: Needs full access to customer contact info but only obfuscated claim information.
Claims team: Needs access to customer information relevant to the claims they process.
Analytics team: Needs only obfuscated PII data.
These teams require different levels of access, and the solution needs to enforce data security while keeping administrative overhead low.
Step 2: Why Option B is Correct
Option B (Creating Views) is a common best practice in Amazon Redshift to restrict access to specific data without duplicating data or managing multiple clusters. By creating views:
You can define customized views of the data with obfuscated fields for the analytics team and marketing team while still providing full access where necessary.
Views provide a logical separation of data and allow Redshift administrators to grant access permissions based on roles or groups, ensuring that each team sees only what they are allowed to.
Obfuscation or masking of PII can be easily applied to the views by transforming or hiding sensitive data fields.
This approach avoids the complexity of managing multiple Redshift clusters or S3-based data lakes, which introduces higher operational and administrative overhead.
Step 3: Why Other Options Are Not Ideal
Option A (Separate Redshift Clusters) introduces unnecessary administrative overhead by managing multiple clusters. Maintaining several clusters for each team is costly, redundant, and inefficient.
Option C (Separate Redshift Roles) involves creating multiple roles and managing complex masking policies, which adds to administrative burden and complexity. While Redshift does support column-level access control, it's still more overhead than managing simple views.
Option D (Move to S3 and Lake Formation) is a more complex and heavy-handed solution, especially when the data is already stored in Redshift. Migrating the data to S3 and setting up a data lake with Lake Formation introduces significant operational complexity that isn't needed for this specific requirement.
Conclusion:
Creating views in Amazon Redshift allows for flexible, fine-grained access control with minimal overhead, making it the optimal solution to meet the data access requirements of the marketing, claims, and analytics teams.

NEW QUESTION # 246
A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII).
The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.
Which solution will meet the requirements with the LEAST operational overhead?

A. Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.
B. Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.
C. Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.
D. Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

Answer: B

Explanation:
Option B is the best solution to meet the requirements with the least operational overhead because S3 Object Lambda is a feature that allows you to add your own code to process data retrieved from S3 before returning it to an application. S3 Object Lambda works with S3 GET requests and can modify both the object metadata and the object data. By using S3 Object Lambda, you can implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data. This way, you can avoid creating and maintaining multiple copies of the dataset with different levels of redaction.
Option A is not a good solution because it involves creating and managing multiple copies of the dataset with different levels of redaction for each application. This option adds complexity and storage cost to the data protection process and requires additional resources and configuration. Moreover, S3 bucket policies cannot enforce fine-grained data access control at the row and column level, so they are not sufficient to redact PII.
Option C is not a good solution because it involves using AWS Glue to transform the data for each application. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. However, in this scenario, using AWS Glue to redact PII is not the best option because it requires creating and maintaining multiple copies of the dataset with different levels of redaction for each application. This option also adds extra time and cost to the data protection process and requires additional resources and configuration.
Option D is not a good solution because it involves creating and configuring an API Gateway endpoint that has custom authorizers. API Gateway is a service that allows you to create, publish, maintain, monitor, and secure APIs at any scale. API Gateway can also integrate with other AWS services, such as Lambda, to provide custom logic for processing requests. However, in this scenario, using API Gateway to redact PII is not the best option because it requires writing and maintaining custom code and configuration for the API endpoint, the custom authorizers, and the REST API call. This option also adds complexity and latency to the data protection process and requires additional resources and configuration.
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
Introducing Amazon S3 Object Lambda - Use Your Code to Process Data as It Is Being Retrieved from S3 Using Bucket Policies and User Policies - Amazon Simple Storage Service AWS Glue Documentation What is Amazon API Gateway? - Amazon API Gateway

NEW QUESTION # 247
A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.
B. Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket.
Create a pipeline in Apache Spark.
C. Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket.
Create a pipeline in Apache Spark.
D. Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.

Answer: B

Explanation:
AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. References:
* AWS Glue
* [AWS Glue Data Catalog]
* [AWS Glue Developer Guide]
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

NEW QUESTION # 248
A transportation company wants to track vehicle movements by capturing geolocation records. The records are 10 bytes in size. The company receives up to 10,000 records every second. Data transmission delays of a few minutes are acceptable because of unreliable network conditions.
The transportation company wants to use Amazon Kinesis Data Streams to ingest the geolocation data. The company needs a reliable mechanism to send data to Kinesis Data Streams. The company needs to maximize the throughput efficiency of the Kinesis shards.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Kinesis SDK
B. Amazon Data Firehose
C. Kinesis Producer Library (KPL)
D. Kinesis Agent

Answer: C

Explanation:
Problem Analysis:
The company ingests geolocation records (10 bytes each) at 10,000 records per second into Kinesis Data Streams.
Data transmission delays are acceptable, but the solution must maximize throughput efficiency.
Key Considerations:
The Kinesis Producer Library (KPL) batches records and uses aggregation to optimize shard throughput.
Efficiently handles high-throughput scenarios with minimal operational overhead.
Solution Analysis:
Option A: Kinesis Agent
Designed for file-based ingestion; not optimized for geolocation records.
Option B: KPL
Aggregates records into larger payloads, significantly improving shard throughput.
Suitable for applications generating small, high-frequency records.
Option C: Kinesis Firehose
Firehose is for delivery to destinations like S3 or Redshift and is not optimized for direct ingestion to Kinesis Data Streams.
Option D: Kinesis SDK
The SDK lacks advanced features like aggregation, resulting in lower throughput efficiency.
Final Recommendation:
Use Kinesis Producer Library (KPL) for its built-in aggregation and batching capabilities.
Reference:
Kinesis Producer Library (KPL) Overview

NEW QUESTION # 249
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