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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q150-Q155):

NEW QUESTION # 150
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?

A. Ingest the data into Amazon Redshift Spectrum. Use SQL queries to profile the data.
B. Stream the data into Amazon Managed Service for Apache Flink for SQL queries. Use the Apache Flink dashboard to profile the data.
C. Create a profile job on the dataset in AWS Glue DataBrew. Review the profile job results.
D. Load the data into an Amazon QuickSight dataset. Build a topic to profile the data with questions.

Answer: C

Explanation:
AWS Glue DataBrew is purpose-built for data profiling and data preparation with minimal operational overhead. A DataBrew profile job automatically analyzes datasets stored in Amazon S3 and generates detailed statistics such as column completeness, distinct values, data types, min/max values, and potential data quality issues. This is exactly what is required before data preparation steps.
The dataset size (100 MB) and Parquet format are fully supported by DataBrew, and no infrastructure provisioning, cluster management, or SQL development is required. The profiling results are produced automatically and can be reviewed directly in the AWS Management Console, making this the most efficient and lowest-effort solution.
Amazon Managed Service for Apache Flink is designed for real-time streaming analytics and introduces unnecessary complexity. Amazon Redshift Spectrum requires schema definition, SQL development, and additional setup, which is excessive for simple profiling. Amazon QuickSight is a visualization and BI service, not a data profiling tool.
Therefore, creating a DataBrew profile job is the most operationally efficient and exam-aligned solution.

NEW QUESTION # 151
A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?

A. Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.
B. Specify a combination of distribution, sort, and partition keys for all tables.
C. Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.
D. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.

Answer: B

Explanation:
This solution meets the requirements of optimizing the performance of table SQL queries without increasing the size of the cluster. By using the ALL distribution style for rarely updated small tables, you can ensure that the entire table is copied to every node in the cluster, which eliminates the need for data redistribution during joins. This can improve query performance significantly, especially for frequently joined dimension tables.
However, using the ALL distribution style also increases the storage space and the load time, so it is only suitable for small tables that are not updated frequently or extensively. By specifying primary and foreign keys for all tables, you can help the query optimizer to generate better query plans and avoid unnecessary scans or joins. You can also use the AUTO distribution style to let Amazon Redshift choose the optimal distribution style based on the table size and the query patterns. References:
* Choose the best distribution style
* Distribution styles
* Working with data distribution styles

NEW QUESTION # 152
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?

A. Create an Oracle database in Amazon RDS. Use AWS Database Migration Service (AWS DMS) to migrate the on-premises Oracle database to Amazon RDS. Configure triggers on the tables to invoke AWS Lambda functions to write changed records to Amazon S3 in Parquet format.
B. Create an Apache Sqoop job in Amazon EMR to read the data from the Oracle database. Configure the Sqoop job to write the data to Amazon S3 in Parquet format.
C. Create an AWS Glue connection to the Oracle database. Create an AWS Glue bookmark job to ingest the data incrementally and to write the data to Amazon S3 in Parquet format.
D. Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.

Answer: D

Explanation:
The company needs to ingest tables from an on-premises Oracle database into a data lake on Amazon S3 in Apache Parquet format. The most efficient solution, requiring the least manual effort, would be to use AWS Database Migration Service (DMS) for continuous data replication.
Option C: Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.AWS DMS can continuously replicate data from the Oracle database into Amazon S3, transforming it into Parquet format as it ingests the data. DMS simplifies the process by providing ongoing replication with minimal setup, and it automatically handles the conversion to Parquet format without requiring manual transformations or separate jobs. This option is the least effort solution since it automates both the ingestion and transformation processes.
Other options:
Option A (Apache Sqoop on EMR) involves more manual configuration and management, including setting up EMR clusters and writing Sqoop jobs.
Option B (AWS Glue bookmark job) involves configuring Glue jobs, which adds complexity. While Glue supports data transformations, DMS offers a more seamless solution for database replication.
Option D (RDS and Lambda triggers) introduces unnecessary complexity by involving RDS and Lambda for a task that DMS can handle more efficiently.
References:
AWS Database Migration Service (DMS)
DMS S3 Target Documentation

NEW QUESTION # 153
A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day. The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.
The company runs a daily report on the S3 dat
a. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.
Which solution will meet this requirement with the LEAST operational overhead?

A. Create data quality checks on the source datasets that the daily reports use. Create a new Amazon EMR cluster. Use Apache Spark SQL to create Apache Spark jobs in the EMR cluster that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow. Configure the workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
B. Create data quality checks for the source datasets that the daily reports use. Create a new AWS managed Apache Airflow cluster. Run the data quality checks by using Airflow tasks that run data quality queries on the columns data type and the presence of null values. Configure Airflow Directed Acyclic Graphs (DAGs) to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
C. Create data quality checks on the source datasets that the daily reports use. Create data quality actions by using AWS Glue workflows to confirm the completeness and consistency of the datasets. Configure the data quality actions to create an event in Amazon EventBridge if a dataset is incomplete. Configure EventBridge to send the event that informs the data engineer about the incomplete datasets to the Amazon SNS topic.
D. Create AWS Lambda functions that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow that runs the Lambda functions. Configure the Step Functions workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.

Answer: C

Explanation:
AWS Glue workflows are designed to orchestrate the ETL pipeline, and you can create data quality checks to ensure the uploaded datasets are complete before running reports. If there is an issue with the data, AWS Glue workflows can trigger an Amazon EventBridge event that sends a message to an SNS topic.
AWS Glue Workflows:
AWS Glue workflows allow users to automate and monitor complex ETL processes. You can include data quality actions to check for null values, data types, and other consistency checks.
In the event of incomplete data, an EventBridge event can be generated to notify via SNS.
Reference:
Alternatives Considered:
A (Airflow cluster): Managed Airflow introduces more operational overhead and complexity compared to Glue workflows.
B (EMR cluster): Setting up an EMR cluster is also more complex compared to the Glue-centric solution.
D (Lambda functions): While Lambda functions can work, using Glue workflows offers a more integrated and lower operational overhead solution.
AWS Glue Workflow Documentation

NEW QUESTION # 154
A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an S3 event notification that has an event type of s3:ObjectTagging:* for objects that have a tag set to .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
B. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set an Amazon Simple Notification Service (Amazon SNS) topic as the destination for the event notification. Subscribe the Lambda function to the SNS topic.
C. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
D. Create an S3 event notification that has an event type of s3:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.

Answer: C

Explanation:
Option A is the correct answer because it meets the requirements with the least operational overhead. Creating an S3 event notification that has an event type of s3:ObjectCreated:* will trigger the Lambda function whenever a new object is created in the S3 bucket. Using a filter rule to generate notifications only when the suffix includes .csv will ensure that the Lambda function only runs for .csv files. Setting the ARN of the Lambda function as the destination for the event notification will directly invoke the Lambda function without any additional steps.
Option B is incorrect because it requires the user to tag the objects with .csv, which adds an extra step and increases the operational overhead.
Option C is incorrect because it uses an event type of s3:*, which will trigger the Lambda function for any S3 event, not just object creation. This could result in unnecessary invocations and increased costs.
Option D is incorrect because it involves creating and subscribing to an SNS topic, which adds an extra layer of complexity and operational overhead.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.2: S3 Event Notifications and Lambda Functions, Pages 67-69
* Building Batch Data Analytics Solutions on AWS, Module 4: Data Transformation, Lesson 4.2: AWS Lambda, Pages 4-8
* AWS Documentation Overview, AWS Lambda Developer Guide, Working with AWS Lambda Functions, Configuring Function Triggers, Using AWS Lambda with Amazon S3, Pages 1-5

NEW QUESTION # 155
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