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真實DATA-ENGINEER-ASSOCIATE套裝 -在VCESOFT中獲得最好的

 

 

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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最新的 AWS Certified Data Engineer Data-Engineer-Associate 免費考試真題 (Q266-Q271):

問題 #266
A food delivery company manages thousands of deliveries simultaneously. Each delivery vehicle transmits real-time telemetry data as JSON events. The company wants to accelerate downstream analytics and simplify data processing. The company needs to flatten the telemetry data and then store the data in an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST latency?

  • A. Send the real-time JSON events as messages to an Amazon Simple Queue Service (Amazon SQS) queue. Schedule an AWS Glue batch job by using a cron expression. Configure the batch job to read, flatten, and write the data to Amazon S3.
  • B. Create an Amazon Data Firehose delivery stream that ingests real-time telemetry data, automatically flattens the data, and delivers the data to the S3 bucket.
  • C. Use Amazon Kinesis Data Streams to ingest real-time JSON events. Configure an AWS Glue streaming job to read, flatten, and write the data to Amazon S3.
  • D. Use Amazon Kinesis Data Streams to ingest real-time JSON events. Use the Amazon Athena flatten function to flatten the JSON data and write the data to the S3 bucket.

答案：C

解題說明：
The correct answer is B because AWS Glue streaming ETL jobs are built to continuously consume data from streaming sources such as Amazon Kinesis Data Streams, transform the data, and load the results into Amazon S3 data lakes. The requirement is not just delivery to S3; the telemetry JSON must be flattened before storage, and this needs to happen with low latency. Amazon Data Firehose can deliver streaming data, but the option incorrectly assumes automatic flattening without a transformation layer.
SQS plus scheduled batch Glue introduces batch delay and does not meet the lowest-latency requirement. Athena is a query engine, not a real-time transformation and ingestion service for writing flattened streaming output to S3. AWS Glue streaming ETL is the correct serverless streaming transformation layer.

 

問題 #267
A company hosts its applications on Amazon EC2 instances. The company must use SSL/TLS connections that encrypt data in transit to communicate securely with AWS infrastructure that is managed by a customer.
A data engineer needs to implement a solution to simplify the generation, distribution, and rotation of digital certificates. The solution must automatically renew and deploy SSL/TLS certificates.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon Elastic Container Service (Amazon ECS) Service Connect.
  • B. Implement custom automation scripts in AWS Secrets Manager.
  • C. Store self-managed certificates on the EC2 instances.
  • D. Use AWS Certificate Manager (ACM).

答案：D

解題說明：
The best solution for managing SSL/TLS certificates on EC2 instances withminimal operational overheadis to useAWS Certificate Manager (ACM). ACM simplifies certificate management by automating the provisioning, renewal, and deployment of certificates.
* AWS Certificate Manager (ACM):
* ACM managesSSL/TLS certificatesfor EC2 and other AWS resources, including automatic certificate renewal. This reduces the need for manual management and avoids operational complexity.
* ACM also integrates with other AWS services to simplify secure connections between AWS infrastructure and customer-managed environments.
Reference:AWS Certificate Manager
Alternatives Considered:
A (Self-managed certificates): Managing certificates manually on EC2 instances increases operational overhead and lacks automatic renewal.
C (Secrets Manager automation): While Secrets Manager can store keys and certificates, it requires custom automation for rotation and does not handle SSL/TLS certificates directly.
D (ECS Service Connect): This is unrelated to SSL/TLS certificate management and would not address the operational need.
References:
AWS Certificate Manager Documentation

 

問題 #268
A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII). The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.
Which solution will meet the requirements with the LEAST operational overhead?

  • A. Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.
  • B. Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.
  • C. Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.
  • D. Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.

答案：D

解題說明：
Option B is the best solution to meet the requirements with the least operational overhead because S3 Object Lambda is a feature that allows you to add your own code to process data retrieved from S3 before returning it to an application. S3 Object Lambda works with S3 GET requests and can modify both the object metadata and the object data. By using S3 Object Lambda, you can implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data. This way, you can avoid creating and maintaining multiple copies of the dataset with different levels of redaction.
Option A is not a good solution because it involves creating and managing multiple copies of the dataset with different levels of redaction for each application. This option adds complexity and storage cost to the data protection process and requires additional resources and configuration. Moreover, S3 bucket policies cannot enforce fine-grained data access control at the row and column level, so they are not sufficient to redact PII.
Option C is not a good solution because it involves using AWS Glue to transform the data for each application. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. However, in this scenario, using AWS Glue to redact PII is not the best option because it requires creating and maintaining multiple copies of the dataset with different levels of redaction for each application. This option also adds extra time and cost to the data protection process and requires additional resources and configuration.
Option D is not a good solution because it involves creating and configuring an API Gateway endpoint that has custom authorizers. API Gateway is a service that allows you to create, publish, maintain, monitor, and secure APIs at any scale. API Gateway can also integrate with other AWS services, such as Lambda, to provide custom logic for processing requests. However, in this scenario, using API Gateway to redact PII is not the best option because it requires writing and maintaining custom code and configuration for the API endpoint, the custom authorizers, and the REST API call. This option also adds complexity and latency to the data protection process and requires additional resources and configuration.
Reference:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
Introducing Amazon S3 Object Lambda - Use Your Code to Process Data as It Is Being Retrieved from S3 Using Bucket Policies and User Policies - Amazon Simple Storage Service AWS Glue Documentation What is Amazon API Gateway? - Amazon API Gateway

 

問題 #269
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?

  • A. Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.
  • B. Create an AWS Glue connection to the Oracle database. Create an AWS Glue bookmark job to ingest the data incrementally and to write the data to Amazon S3 in Parquet format.
  • C. Create an Oracle database in Amazon RDS. Use AWS Database Migration Service (AWS DMS) to migrate the on-premises Oracle database to Amazon RDS. Configure triggers on the tables to invoke AWS Lambda functions to write changed records to Amazon S3 in Parquet format.
  • D. Create an Apache Sqoop job in Amazon EMR to read the data from the Oracle database. Configure the Sqoop job to write the data to Amazon S3 in Parquet format.

答案：A

解題說明：
The company needs to ingest tables from an on-premises Oracle database into a data lake on Amazon S3 in Apache Parquet format. The most efficient solution, requiring the least manual effort, would be to use AWS Database Migration Service (DMS) for continuous data replication.
Option C: Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.
AWS DMS can continuously replicate data from the Oracle database into Amazon S3, transforming it into Parquet format as it ingests the data. DMS simplifies the process by providing ongoing replication with minimal setup, and it automatically handles the conversion to Parquet format without requiring manual transformations or separate jobs. This option is the least effort solution since it automates both the ingestion and transformation processes.
Other options:
Option A (Apache Sqoop on EMR) involves more manual configuration and management, including setting up EMR clusters and writing Sqoop jobs.
Option B (AWS Glue bookmark job) involves configuring Glue jobs, which adds complexity. While Glue supports data transformations, DMS offers a more seamless solution for database replication.
Option D (RDS and Lambda triggers) introduces unnecessary complexity by involving RDS and Lambda for a task that DMS can handle more efficiently.
Reference:
AWS Database Migration Service (DMS)
DMS S3 Target Documentation

 

問題 #270
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?

  • A. AWS Glue workflows
  • B. AWS Glue Studio
  • C. AWS Step Functions
  • D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

答案：A

解題說明：
AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don't have to manage any infrastructure.
AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines.
AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing.
Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage.
Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References:
AWS Glue Workflows
AWS Step Functions
AWS Glue Studio
Amazon MWAA
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

 

問題 #271
......

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Data-Engineer-Associate PDF: https://www.vcesoft.com/Data-Engineer-Associate-pdf.html

順便提一下，可以從雲存儲中下載VCESoft Data-Engineer-Associate考試題庫的完整版：https://drive.google.com/open?id=1rAtp8nk9o3IgPrCaYXlizqRIQTdxzQMX

 

 

 

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