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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q73-Q78):

NEW QUESTION # 73
A company runs multiple applications on AWS. The company configured each application to output logs. The company wants to query and visualize the application logs in near real time.
Which solution will meet these requirements?

A. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an Amazon S3 bucket. Create an AWS Lambda function that runs on a schedule to export the required log groups to the S3 bucket. Use Amazon Athena to query the log data in the S3 bucket.
B. Update the application code to send the log data to Amazon QuickSight by using Super-fast, Parallel, In- memory Calculation Engine (SPICE). Create the required analyses and dashboards in QuickSight.
C. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Use CloudWatch log anomaly detection to query and visualize the log data.
D. Create an Amazon OpenSearch Service domain. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an OpenSearch Service subscription filter for each log group to stream the data to OpenSearch. Create the required queries and dashboards in OpenSearch Service to analyze and visualize the data.

Answer: D

Explanation:
The optimal solution for near-real-time querying and visualization of logs is to integrateAmazon CloudWatch LogswithAmazon OpenSearch Serviceusingsubscription filters, which stream the logs directly into OpenSearch for querying and dashboarding:
"Use OpenSearch Service with CloudWatch Logs and create a subscription filter to stream log data in near real time into OpenSearch. Then use OpenSearch dashboards for visualization."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This approach offers low latency and avoids batch exports, unlike the scheduled Athena + S3 pattern.

NEW QUESTION # 74
A company uses Amazon Redshift for its data warehouse. A data engineer must query a table named orders.
complete_orders_history, which contains 100 columns. The query must return all columns except columns named company_id and unique_system_id.
Which Amazon Redshift SQL statement will meet this requirement?

A. SELECT * NOT IN company_id, unique_system_idFROM orders.complete_orders_history;
B. SELECT * TRUNCATE company_id, unique_system_idFROM orders.complete_orders_history;
C. SELECT * EXCEPT company_id, unique_system_idFROM orders.complete_orders_history;
D. SELECT * EXCLUDE company_id, unique_system_idFROM orders.complete_orders_history;

Answer: D

Explanation:
Option A is correct because Amazon Redshift supports the EXCLUDE clause in the SELECT list for exactly this use case: returning all columns from a wide table except a small set of unwanted columns. AWS documentation states that "EXCLUDE column_list names the columns that are excluded from the query results" and notes that the option is helpful when only a subset of columns must be excluded from a wide table. That matches this question precisely because the table contains 100 columns and only company_id and unique_system_id need to be omitted.
Option B is invalid SQL syntax in Amazon Redshift because NOT IN is used in predicates, not in the select list to remove columns. Option C is also incorrect because EXCEPT in Redshift is a set operator used between query result sets, not a select-list column exclusion feature. AWS documents EXCEPT alongside UNION and INTERSECT as result-set comparison operators, not as a way to omit columns from SELECT *.
Option D is not valid Redshift SQL syntax for column selection. Therefore, the correct and documented Redshift statement is SELECT * EXCLUDE company_id, unique_system_id FROM orders.
complete_orders_history;

NEW QUESTION # 75
A company uploads .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.
An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.
If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.
Which solution will meet these requirements?

A. Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.
B. Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.
C. Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.
D. Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.

Answer: B

Explanation:
To avoid duplicate records in Amazon Redshift, the most effective solution is to perform the ETL in a way that first loads the data into a staging table and then uses SQL commands like MERGE or UPDATE to insert new records and update existing records without introducing duplicates.
* Using Staging Tables in Redshift:
* The AWS Glue job can write data to a staging table in Redshift. Once the data is loaded, SQL commands can be executed to compare the staging data with the target table and update or insert records appropriately. This ensures no duplicates are introduced during re-runs of the Glue job.

NEW QUESTION # 76
A company builds a new data pipeline to process data for business intelligence reports. Users have noticed that data is missing from the reports.
A data engineer needs to add a data quality check for columns that contain null values and for referential integrity at a stage before the data is added to storage.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Glue ETL jobs to perform a data quality evaluation transform on the data. Use an IsComplete rule on the requested columns. Use a ReferentialIntegrity rule for each join.
B. Use AWS Glue ETL jobs to perform a SQL transform on the data to determine whether requested columns contain null values. Use a second SQL transform to check referential integrity.
C. Use Amazon SageMaker Data Wrangler to create a Data Quality and Insights report.
D. Use Amazon SageMaker Data Wrangler and a custom Python transform to create custom rules to check for null values and referential integrity.

Answer: A

Explanation:
AWS Glue Data Quality transforms allow you to define built-in rules like IsComplete for null validation and ReferentialIntegrity for relationship validation-all with minimal code and operational overhead.
"Use AWS Glue Data Quality rules such as IsComplete and ReferentialIntegrity within ETL jobs to automatically validate incoming data."

NEW QUESTION # 77
A company stores sales data in an Amazon RDS for MySQL database. The company needs to start a reporting process between 6:00 A.M. and 6:10 A.M. every Monday. The reporting process must generate a CSV file and store the file in an Amazon S3 bucket.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)

A. Create and invoke an AWS Glue ETL job to generate the report and to save it to the S3 bucket.
B. Create and invoke an AWS Batch job that runs a script in an Amazon Elastic Container Service (Amazon ECS) container. Configure the script to generate the report and to save it to the S3 bucket.
C. Create and invoke an Amazon EMR Serverless job to generate the report and to save it to the S3 bucket.
D. Create an Amazon EventBridge rule to run every Monday at 6:00 A.M.
E. Create an Amazon EventBridge Scheduler to run every Monday at 6:00 A.M.

Answer: A,E

Explanation:
The Amazon EventBridge Scheduler offers a simple, serverless cron-based execution mechanism. It can trigger an AWS Glue ETL job that extracts data from Amazon RDS, formats it as CSV, and writes it to Amazon S3 - all without manual orchestration or servers.
"For scheduled data extraction and transformation, use AWS Glue jobs triggered by EventBridge Scheduler for fully managed, low-maintenance workflows."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Glue natively integrates with RDS and S3, avoiding the need to manage Batch or EMR infrastructure.

NEW QUESTION # 78
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