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Amazon Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Data Ingestion and Transformation34%- Perform data ingestion
  • 1. Streaming data ingestion
  • 2. Batch data ingestion (scheduled ingestion, event-driven ingestion)
  • 3. Data ingestion patterns (frequency and data history)
  • 4. Throughput and latency characteristics for AWS services
  • 5. Replayability of data
- Transform and process data
  • 1. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda)
  • 2. Batch and stream processing
  • 3. ETL/ELT patterns
  • 4. Data partitioning and compression
- Apply programming concepts
  • 1. SQL, Python, Scala
  • 2. Infrastructure as Code (IaC)
  • 3. Version control
- Orchestrate data pipelines
  • 1. AWS Glue Workflows
  • 2. AWS Step Functions
  • 3. Amazon Managed Workflows for Apache Airflow (MWAA)
  • 4. Event-driven architectures
Data Security and Governance18%- Apply authentication and authorization
  • 1. AWS IAM policies and roles
  • 2. Service control policies (SCPs)
  • 3. Amazon S3 bucket policies
- Implement data quality checks
  • 1. AWS Glue DataBrew
  • 2. Data validation
- Manage data privacy and compliance
  • 1. AWS Lake Formation permissions
  • 2. PII data handling
  • 3. Data masking and tokenization
- Ensure data encryption
  • 1. AWS KMS
  • 2. Encryption at rest and in transit
Data Store Management26%- Understand data cataloging
  • 1. Data discovery and classification
  • 2. Schema evolution
  • 3. AWS Glue Data Catalog
- Design data models
  • 1. Partitioning and indexing strategies
  • 2. Normalization and denormalization
  • 3. Schema design
- Choose a data store
  • 1. Data lakes vs. data warehouses
  • 2. Access and storage patterns
  • 3. Data characteristics (structured, semi-structured, unstructured)
  • 4. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift
- Manage data lifecycle
  • 1. Data retention policies
  • 2. Data archiving
  • 3. Amazon S3 storage classes
Data Operations and Support22%- Manage and troubleshoot data processes
  • 1. Performance tuning
  • 2. Debugging failed jobs
  • 3. Cost optimization
- Automate data pipelines
  • 1. AWS Lambda triggers
  • 2. Event-driven triggers
  • 3. Scheduling jobs
- Monitor data pipelines
  • 1. Amazon CloudWatch
  • 2. AWS CloudTrail
  • 3. Logging and metrics

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q208-Q213):

NEW QUESTION # 208
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?

Answer: B

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.
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 # 209
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.)

Answer: B,E

Explanation:
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.
1: AWS Lake Formation
2: AWS Lake Formation Permissions
AWS Identity and Access Management
Amazon S3


NEW QUESTION # 210
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming dat
a. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
The streaming ingestion feature of Amazon Redshift enables you to ingest data from streaming sources, such as Amazon Kinesis Data Streams, into Amazon Redshift tables in near real-time. You can use the streaming ingestion feature to process the streaming data from the wearable devices, hospital equipment, and patient records. The streaming ingestion feature also supports incremental updates, which means you can append new data or update existing data in the Amazon Redshift tables. This way, you can store the data in an Amazon Redshift Serverless warehouse and support near real-time analytics of the streaming data and the previous day's data. This solution meets the requirements with the least operational overhead, as it does not require any additional services or components to ingest and process the streaming data. The other options are either not feasible or not optimal. Loading data into Amazon Kinesis Data Firehose and then into Amazon Redshift (option A) would introduce additional latency and cost, as well as require additional configuration and management. Loading data into Amazon S3 and then using the COPY command to load the data into Amazon Redshift (option C) would also introduce additional latency and cost, as well as require additional storage space and ETL logic. Using the Amazon Aurora zero-ETL integration with Amazon Redshift (option D) would not work, as it requires the data to be stored in Amazon Aurora first, which is not the case for the streaming data from the healthcare company. Reference:
Using streaming ingestion with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.5: Amazon Redshift Streaming Ingestion


NEW QUESTION # 211
A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.
The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.
Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

Answer: B,C

Explanation:
To enable the Lambda function to connect to the RDS DB instance privately without using the public internet, the best combination of steps is to configure the Lambda function to run in the same subnet that the DB instance uses, and attach the same security group to the Lambda function and the DB instance. This way, the Lambda function and the DB instance can communicate within the same private network, and the security group can allow traffic between them on the database port. This solution has the least operational overhead, as it does not require any changes to the public access setting, the network ACL, or the security group of the DB instance.
The other options are not optimal for the following reasons:
A: Turn on the public access setting for the DB instance. This option is not recommended, as it would expose the DB instance to the public internet, which can compromise the security and privacy of the data. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
B: Update the security group of the DB instance to allow only Lambda function invocations on the database port. This option is not sufficient, as it would only modify the inbound rules of the security group of the DB instance, but not the outbound rules of the security group of the Lambda function.
Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
E: Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port. This option is not necessary, as the network ACL of the private subnet already allows all traffic within the subnet by default. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
References:
1: Connecting to an Amazon RDS DB instance
2: Configuring a Lambda function to access resources in a VPC
3: Working with security groups
4: Network ACLs


NEW QUESTION # 212
A data engineering team is using an Amazon Redshift data warehouse for operational reporting. The team wants to prevent performance issues that might result from long- running queries. A data engineer must choose a system table in Amazon Redshift to record anomalies when a query optimizer identifies conditions that might indicate performance issues.
Which table views should the data engineer use to meet this requirement?

Answer: B

Explanation:
The STL ALERT EVENT LOG table view records anomalies when the query optimizer identifies conditions that might indicate performance issues. These conditions include skewed data distribution, missing statistics, nested loop joins, and broadcasted data. The STL ALERT EVENT LOG table view can help the data engineer to identify and troubleshoot the root causes of performance issues and optimize the query execution plan. The other table views are not relevant for this requirement. STL USAGE CONTROL records the usage limits and quotas for Amazon Redshift resources. STL QUERY METRICS records the execution time and resource consumption of queries. STL PLAN INFO records the query execution plan and the steps involved in each query. Reference:
STL ALERT EVENT LOG
System Tables and Views
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 213
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