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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Store Management	26%	- Design and implement data storage solutions 1. Data lakes, data warehouses, databases 2. S3, Redshift, DynamoDB, RDS, Lake Formation - Optimize storage performance and cost - Manage data lifecycle and storage tiers
Topic 2: Data Security and Governance	18%	- Protect sensitive data - Implement access control and authentication 1. IAM, Lake Formation, resource policies - Encrypt data at rest and in transit - Enforce compliance and data governance 1. Data lineage, audit logging, regulatory requirements
Topic 3: Data Operations and Support	22%	- Ensure reliability and scalability - Backup, restore, and disaster recovery - Monitor and troubleshoot data pipelines 1. CloudWatch, X-Ray, logging and metrics - Automate operational tasks
Topic 4: Data Ingestion and Transformation	34%	- Implement data quality and validation - Ingest data from various sources 1. Batch and streaming data ingestion 2. Use services like Kinesis, DMS, Glue, S3 - Transform and enrich data 1. Orchestrate data pipelines 2. Apply data processing logic 3. Use Spark, EMR, Step Functions

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q69-Q74):

NEW QUESTION # 69
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers.
The data stream runs in Account A.
The company wants to use an AWS Lambda function in Account B to process the data from the stream. The company creates a Lambda execution role in Account B that has permissions to access data from the stream in Account A.
What additional step must the company take to meet this requirement?

A. Add a resource-based policy to the data stream to allow read access for the cross-account Lambda execution role.
B. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account B.
C. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account A.
D. Add a resource-based policy to the cross-account Lambda function to grant the data stream read access to the function.

Answer: A

Explanation:
To allow cross-account access to a Kinesis Data Stream, you must add a resource-based policy to the Kinesis stream in Account A, explicitly granting the Lambda execution role in Account B the required permissions.
* SCPs (A & C) set permissions boundaries, but do not grant access.
* Option D incorrectly refers to the Lambda function - but the Kinesis resource must allow access.
"You must add a resource-based policy to the Kinesis Data Stream in Account A to allow a Lambda function in Account B to consume from the stream." Reference: AWS Documentation - Cross-account Lambda access to Kinesis

NEW QUESTION # 70
A company has a data lake in Amazon 53. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?

A. Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.
B. Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.
C. Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.
D. Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.

Answer: D

Explanation:
* Problem Analysis:
* The company uses AWS Glue for ETL pipelines and must enforcedata quality checksduring pipeline execution.
* The goal is to implement quality checks withminimal implementation effort.
* Key Considerations:
* AWS Glue provides anEvaluate Data Quality transformthat allows for defining quality checks directly in the pipeline.
* DQDL (Data Quality Definition Language)simplifies the process by allowing declarative rule definitions.
* Solution Analysis:
* Option A: SQL Transform
* SQL queries can implement rules but require manual effort for each rule and do not integrate natively with Glue.
* Option B: Evaluate Data Quality Transform + DQDL
* AWS Glue's built-inEvaluate Data Quality transformis designed for this use case.
* Allows defining thresholds and rules in DQDL with minimal coding effort.
* Option C: Custom Transform with PyDeequ
* PyDeequ is a powerful library but adds unnecessary complexity compared to Glue's native features.
* Option D: Custom Transform with Great Expectations
* Similar to PyDeequ, Great Expectations adds operational complexity and external dependencies.
* Final Recommendation:
* Use theEvaluate Data Quality transformwithDQDLto implement data quality rules in AWS Glue pipelines.
:
AWS Glue Data Quality
DQDL Syntax and Examples
AWS Glue Studio Documentation

NEW QUESTION # 71
A company uses an Amazon Redshift cluster as a data warehouse that is shared across two departments. To comply with a security policy, each department must have unique access permissions.
Department A must have access to tables and views for Department A. Department B must have access to tables and views for Department B.
The company often runs SQL queries that use objects from both departments in one query.
Which solution will meet these requirements with the LEAST operational overhead?

A. Group tables and views for each department into dedicated schemas. Manage permissions at the schema level.
B. Update the names of the tables and views to follow a naming convention that contains the department names. Manage permissions based on the new naming convention.
C. Group tables and views for each department into dedicated databases. Manage permissions at the database level.
D. Create an IAM user group for each department. Use identity-based IAM policies to grant table and view permissions based on the IAM user group.

Answer: A

NEW QUESTION # 72
A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.
Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.
Which solution will meet these requirements?

A. Create a row-level security policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the security policy to the table.
B. Create a dynamic data masking policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the masking policy to the column that contains sensitive data.
C. Create an AWS Glue job to redact the sensitive data and to load the data into a new Redshift table.
D. Enable metadata security on the Redshift cluster. Create IAM users and IAM roles for the customer support users and the audit users. Grant the IAM users and IAM roles permissions to view the metadata in the Redshift cluster.

Answer: B

Explanation:
Amazon Redshift supportsdynamic data masking, which enables you to limit sensitive data visibility to specific users and roles without duplicating the data. This approach supports showing only parts of a column's values (e.g., last four digits) and full visibility for authorized roles (e.g., auditors).
"With dynamic data masking, you can control how much sensitive data a user sees in query results without changing the data in the table."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf IAM roles are used to associate users with the appropriate masking rules, keeping security tight and avoiding the creation of duplicate data views or tables.

NEW QUESTION # 73
A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.
Which solution will meet these requirements?

A. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.
B. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Specify a database name for the output.
C. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.
D. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.

Answer: B

Explanation:
To make the S3 data accessible daily in the AWS Glue Data Catalog, the data engineer needs to create a crawler that can crawl the S3 data and write the metadata to the Data Catalog. The crawler also needs to run on a daily schedule to keep the Data Catalog updated with the latest data. Therefore, the solution must include the following steps:
Create an IAM role that has the necessary permissions to access the S3 data and the Data Catalog. The AWSGlueServiceRole policy is a managed policy that grants these permissions1.
Associate the role with the crawler.
Specify the S3 bucket path of the source data as the crawler's data store. The crawler will scan the data and infer the schema and format2.
Create a daily schedule to run the crawler. The crawler will run at the specified time every day and update the Data Catalog with any changes in the data3.
Specify a database name for the output. The crawler will create or update a table in the Data Catalog under the specified database. The table will contain the metadata about the data in the S3 bucket, such as the location, schema, and classification.
Option B is the only solution that includes all these steps. Therefore, option B is the correct answer.
Option A is incorrect because it configures the output destination to a new path in the existing S3 bucket. This is unnecessary and may cause confusion, as the crawler does not write any data to the S3 bucket, only metadata to the Data Catalog.
Option C is incorrect because it allocates data processing units (DPUs) to run the crawler every day. This is also unnecessary, as DPUs are only used for AWS Glue ETL jobs, not crawlers.
Option D is incorrect because it combines the errors of option A and C. It configures the output destination to a new path in the existing S3 bucket and allocates DPUs to run the crawler every day, both of which are irrelevant for the crawler.
Reference:
1: AWS managed (predefined) policies for AWS Glue - AWS Glue
2: Data Catalog and crawlers in AWS Glue - AWS Glue
3: Scheduling an AWS Glue crawler - AWS Glue
[4]: Parameters set on Data Catalog tables by crawler - AWS Glue
[5]: AWS Glue pricing - Amazon Web Services (AWS)

NEW QUESTION # 74
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