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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q81-Q86):

NEW QUESTION # 81
A data engineer needs to onboard a new data producer into AWS. The data producer needs to migrate data products to AWS.
The data producer maintains many data pipelines that support a business application. Each pipeline must have service accounts and their corresponding credentials. The data engineer must establish a secure connection from the data producer's on-premises data center to AWS. The data engineer must not use the public internet to transfer data from an on-premises data center to AWS.
Which solution will meet these requirements?

A. Create an AWS Direct Connect connection to the on-premises data center. Store the application keys in AWS Secrets Manager. Create Amazon S3 buckets that contain resigned URLS that have one-day expiration dates.
B. Instruct the new data producer to create Amazon Machine Images (AMIs) on Amazon Elastic Container Service (Amazon ECS) to store the code base of the application. Create security groups in a public subnet that allow connections only to the on-premises data center.
C. Create a security group in a public subnet. Configure the security group to allow only connections from the CIDR blocks that correspond to the data producer. Create Amazon S3 buckets than contain presigned URLS that have one-day expiration dates.
D. Create an AWS Direct Connect connection to the on-premises data center. Store the service account credentials in AWS Secrets manager.

Answer: D

Explanation:
For secure migration of data from an on-premises data center to AWS without using the public internet, AWS Direct Connect is the most secure and reliable method. Using Secrets Manager to store service account credentials ensures that the credentials are managed securely with automatic rotation.
AWS Direct Connect:
Direct Connect establishes a dedicated, private connection between the on-premises data center and AWS, avoiding the public internet. This is ideal for secure, high-speed data transfers.
Reference:
AWS Secrets Manager:
Secrets Manager securely stores and rotates service account credentials, reducing operational overhead while ensuring security.
Alternatives Considered:
A (ECS with security groups): This does not address the need for a secure, private connection from the on-premises data center.
C (Public subnet with presigned URLs): This involves using the public internet, which does not meet the requirement.
D (Direct Connect with presigned URLs): While Direct Connect is correct, presigned URLs with short expiration dates are unnecessary for this use case.
AWS Direct Connect Documentation
AWS Secrets Manager Documentation

NEW QUESTION # 82
A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?

A. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
B. Amazon Kinesis
C. AWS Glue Data Catalog
D. Amazon AppFlow

Answer: D

Explanation:
Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and Amazon Redshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks. Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines. Reference:
Amazon AppFlow
Amazon AppFlow | SaaS Integrations List
Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions

NEW QUESTION # 83
A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command:
MERGE INTO accounts t USING monthly_accounts_update s
ON t.customer = s.customer
WHEN MATCHED THEN DELETE
What will happen when the data engineer runs the SQL command?

A. No records will be deleted because the command syntax is not valid in AWS Glue.
B. All customer records that exist in both the customer accounts table and the monthly_accounts_update table will be deleted from the accounts table.
C. Only customer records that are present in both tables will be retained in the customer accounts table.
D. The monthly_accounts_update table will be deleted.

Answer: B

Explanation:
In AWS Glue's SQL implementation (Spark SQL-compatible), the MERGE INTO statement supports conditional actions.
The clause WHEN MATCHED THEN DELETE deletes matching records from the target table (accounts) where the join condition is true.
"A MERGE INTO statement can perform updates, inserts, or deletes based on the match condition between source and target tables."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

NEW QUESTION # 84
A data engineer is launching an Amazon EMR cluster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit.
The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.
Which solution will meet these requirements?

A. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for local disk encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
B. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption.
Create the EMR cluster, and attach the security configuration to the cluster.
C. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Create a second security configuration. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach both security configurations to the cluster.
D. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.

Answer: D

Explanation:
To meet both encryption at rest and in transit, a single Amazon EMR security configuration can be created specifying the AWS KMS key for encryption at rest and the PEM file for in-transit encryption. The study guide clearly states:
"AWS Key Management Service (KMS) provides encryption for data at rest, and SSL/TLS ensures encryption for data in transit, providing end-to-end encryption within an AWS environment."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf A single security configuration is sufficient and the cleanest way to apply these security features during EMR cluster setup.

NEW QUESTION # 85
A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple user groups need to access the raw data. The company must ensure that user groups can access only the PII that they require.
Which solution will meet these requirements with the LEAST effort?

A. Create IAM roles that have different levels of granular access. Assign the IAM roles to IAM user groups. Use an identity-based policy to assign access levels to user groups at the column level.
B. Build a custom query builder UI that will run Athena queries in the background to access the data.
Create user groups in Amazon Cognito. Assign access levels to the user groups based on the PII access requirements of the users.
C. Use Amazon Athena to query the data. Set up AWS Lake Formation and create data filters to establish levels of access for the company's IAM roles. Assign each user to the IAM role that matches the user's PII access requirements.
D. Use Amazon QuickSight to access the data. Use column-level security features in QuickSight to limit the PII that users can retrieve from Amazon S3 by using Amazon Athena. Define QuickSight access levels based on the PII access requirements of the users.

Answer: C

Explanation:
Amazon Athena is a serverless, interactive query service that enables you to analyze data in Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build, secure, and manage data lakes on AWS.
You can use AWS Lake Formation to create data filters that define the level of access for different IAM roles based on the columns, rows, or tags of the data. By using Amazon Athena to query the data and AWS Lake Formation to create data filters, the company can meet the requirements of ensuring that user groups can access only the PII that they require with the least effort. The solution is to use Amazon Athena to query the data in the data lake that is in Amazon S3. Then, set up AWS Lake Formation and create data filters to establish levels of access for the company's IAM roles. For example, a data filter can allow a user group to access only the columns that contain the PII that they need, such as name and email address, and deny access to the columns that contain the PII that they do not need, such as phone number and social security number.
Finally, assign each user to the IAM role that matches the user's PII access requirements. This way, the user groups can access the data in the data lake securely and efficiently. The other options are either not feasible or not optimal. Using Amazon QuickSight to access the data (option B) would require the company to pay for the QuickSight service and to configure the column-level security features for each user. Building a custom query builder UI that will run Athena queries in the background to access the data (option C) would require the company to develop and maintain the UI and to integrate it with Amazon Cognito. Creating IAM roles that have different levels of granular access (option D) would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema. References:
Amazon Athena
AWS Lake Formation
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.3: Amazon Athena

NEW QUESTION # 86
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