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Amazon Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Security and Governance18%- Implement access control and authentication
  • 1. IAM, Lake Formation, resource policies
- Protect sensitive data
- Encrypt data at rest and in transit
- Enforce compliance and data governance
  • 1. Data lineage, audit logging, regulatory requirements
Topic 2: Data Ingestion and Transformation34%- Transform and enrich data
  • 1. Apply data processing logic
  • 2. Orchestrate data pipelines
  • 3. Use Spark, EMR, Step Functions
- Ingest data from various sources
  • 1. Batch and streaming data ingestion
  • 2. Use services like Kinesis, DMS, Glue, S3
- Implement data quality and validation
Topic 3: Data Operations and Support22%- Backup, restore, and disaster recovery
- Ensure reliability and scalability
- Monitor and troubleshoot data pipelines
  • 1. CloudWatch, X-Ray, logging and metrics
- Automate operational tasks
Topic 4: Data Store Management26%- Manage data lifecycle and storage tiers
- Optimize storage performance and cost
- Design and implement data storage solutions
  • 1. Data lakes, data warehouses, databases
  • 2. S3, Redshift, DynamoDB, RDS, Lake Formation

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q232-Q237):

232. Frage
A data engineer is processing a large amount of log data from web servers. The data is stored in an Amazon S3 bucket. The data engineer uses AWS services to process the data every day. The data engineer needs to extract specific fields from the raw log data and load the data into a data warehouse for analysis.

Antwort: A


233. Frage
A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time.
Which solution will run the Glue jobs in the MOST cost-effective way?

Antwort: B

Begründung:
The FLEX execution class allows you to run AWS Glue jobs on spare compute capacity instead of dedicated hardware. This can reduce the cost of running non-urgent or non-time sensitive data integration workloads, such as testing and one-time data loads. The FLEX execution class is available for AWS Glue 3.0 Spark jobs. The other options are not as cost-effective as FLEX, because they either use dedicated resources (STANDARD) or do not affect the cost at all (Spot Instance type and GlueVersion). Reference:
Introducing AWS Glue Flex jobs: Cost savings on ETL workloads
Serverless Data Integration - AWS Glue Pricing
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 125)


234. Frage
A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)

Antwort: B,E

Begründung:
AWS Lake Formation is a service that helps you build, secure, and manage data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a data lake location, and enable fine-grained access control to limit access to the records based on the HR department's Region. You can use data filters to specify which S3 prefixes or partitions each HR department can access, and grant permissions to the IAM roles of the HR departments accordingly. This solution will meet the requirement with the least operational overhead, as it simplifies the data lake management and security, and leverages the existing IAM roles of the HR departments12.
The other options are not optimal for the following reasons:
* A. Use data filters for each Region to register the S3 paths as data locations. This option is not possible, as data filters are not used to register S3 paths as data locations, but to grant permissions to access specific S3 prefixes or partitions within a data location. Moreover, this option does not specify how to limit access to the records based on the HR department's Region.
* C. Modify the IAM roles of the HR departments to add a data filter for each department's Region. This option is not possible, as data filters are not added to IAM roles, but to permissions granted by AWS Lake Formation. Moreover, this option does not specify how to register the S3 path as a data lake location, or how to enable fine-grained access control in AWS Lake Formation.
* E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. This option is not recommended, as it would require more operational overhead to create and manage multiple S3 buckets, and to configure and maintain IAM policies for each HR department. Moreover, this option does not leverage the benefits of AWS Lake Formation, such as data cataloging, data transformation, and data governance.
References:
* 1: AWS Lake Formation
* 2: AWS Lake Formation Permissions
* : AWS Identity and Access Management
* : Amazon S3


235. Frage
A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-node Amazon Redshift cluster. The company organizes the data files in the data lake based on the data source of each data file.
The company loads all the data files into one table in the Redshift cluster by using a separate COPY command for each data file location. This approach takes a long time to load all the data files into the table. The company must increase the speed of the data ingestion. The company does not want to increase the cost of the process.
Which solution will meet these requirements?

Antwort: A

Begründung:
The company is facing performance issues loading data into Amazon Redshift because it is issuing separate COPY commands for each data file location. The most efficient way to increase the speed of data ingestion into Redshift without increasing the cost is to use a manifest file.
* Option D: Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.A manifest file provides a list of all the data files, allowing the COPY command to load all files in parallel from different locations in Amazon S3. This significantly improves the loading speed without adding costs, as it optimizes the data loading process in a single COPY operation.
Other options (A, B, C) involve additional steps that would either increase the cost (provisioning clusters, using Glue, etc.) or do not address the core issue of needing a unified and efficient COPY process.
References:
* Amazon Redshift COPY Command
* Redshift Manifest File Documentation


236. Frage
The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first 30 days after the records are created. The company accesses records that are older than 30 days infrequently.
The company needs to cost-optimize its Amazon S3 storage.
Which solution will meet these requirements MOST cost-effectively?

Antwort: C

Begründung:
The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here's why:
Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from the S3 Standard storage class to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but still needs to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.
Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.
Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use of S3 Standard for that period ensures compliance with regulatory requirements. After 30 days, transitioning to S3 Standard-IA continues to meet access requirements for infrequent access while reducing storage costs.
Alternatives Considered:
Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data to S3 Standard-IA directly would be more cost-effective since the pattern of access is well-known (frequent for 30 days, infrequent thereafter).
Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.
Option D (S3 Standard-IA for all records): Using S3 Standard-IA for all records would result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.
Reference:
Amazon S3 Lifecycle Policies
S3 Storage Classes
Cost Management and Data Optimization Using Lifecycle Policies
AWS Data Engineering Documentation


237. Frage
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