그 외, Itexamdump Data-Engineer-Associate 시험 문제집 일부가 지금은 무료입니다: https://drive.google.com/open?id=1ZvSqUPpN0fy6Fxn8b06LpWhJexlVGp_3
Data-Engineer-Associate덤프를 퍼펙트하게 공부하시면 보다 쉽게 시험에서 패스할수 있습니다. 다년간 IT업계에 종사하신 전문가들이 Data-Engineer-Associate인증시험을 부단히 연구하고 분석한 성과가 Data-Engineer-Associate덤프에 고스란히 담겨져 있어 시험합격율이 100%에 달한다고 해도 과언이 아닌것 같습니다.Data-Engineer-Associate덤프 구매의향이 있으신 분은 구매페이지에서 덤프 데모문제를 다운받아 보시고 구매결정을 하시면 됩니다.Itexamdump는 모든 분들이 시험에서 합격하시길 항상 기원하고 있습니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Store Management | 26% | - Manage data lifecycle
|
| Topic 2: Data Operations and Support | 22% | - Monitor data pipelines
|
| Topic 3: Data Security and Governance | 18% | - Implement data quality checks
|
| Topic 4: Data Ingestion and Transformation | 34% | - Orchestrate data pipelines
|
>> Data-Engineer-Associate최신 업데이트 덤프자료 <<
Amazon Data-Engineer-Associate인증시험은 전문적인 관련지식을 테스트하는 인증시험입니다. Itexamdump는 여러분이Amazon Data-Engineer-Associate인증시험을 통과할 수 잇도록 도와주는 사이트입니다. 많은 분들이 많은 시간과 돈을 들여 혹은 여러 학원 등을 다니면서Amazon Data-Engineer-Associate인증시험패스에 노력을 다합니다. 하지만 우리Itexamdump에서는 20시간 좌우만 투자하면 무조건Amazon Data-Engineer-Associate시험을 패스할 수 있도록 도와드립니다.
질문 # 204
A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01.
A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.
Which solution will meet these requirements with the LEAST latency?
정답：C
설명：
The best solution to ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket with the least latency is to use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create partition API call. This way, the Data Catalog is updated as soon as new data is written to S3, and the partition information is immediately available for querying by other services. The Boto3 AWS Glue create partition API call allows you to create a new partition in the Data Catalog by specifying the table name, the database name, and the partition values1. You can use this API call in your code that writes data to S3, such as a Python script or an AWS Glue ETL job, to create a partition for each new S3 object key that matches the partitioning scheme.
Option A is not the best solution, as scheduling an AWS Glue crawler to run every morning would introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. Crawlers can be scheduled to run periodically, such as daily or hourly, but they cannot run continuously or in real-time. Therefore, using a crawler to synchronize the Data Catalog with the S3 storage would not meet the requirement of the least latency.
Option B is not the best solution, as manually running the AWS Glue CreatePartition API twice each day would also introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. Moreover, manually running the API would require more operational overhead and human intervention than using code that writes data to S3 to invoke the API automatically.
Option D is not the best solution, as running the MSCK REPAIR TABLE command from the AWS Glue console would also introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. The MSCK REPAIR TABLE command is a SQL command that you can run in the AWS Glue console to add partitions to the Data Catalog based on the S3 object keys that match the partitioning scheme3. However, this command is not meant to be run frequently or in real-time, as it can take a long time to scan the entire S3 bucket and add the partitions. Therefore, using this command to synchronize the Data Catalog with the S3 storage would not meet the requirement of the least latency. Reference:
AWS Glue CreatePartition API
Populating the AWS Glue Data Catalog
MSCK REPAIR TABLE Command
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
질문 # 205
A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?
정답：A
설명：
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze streaming data in real time. You can use Kinesis Data Streams to capture sensor data from various sources, such as IoT devices, web applications, or mobile apps. You can create data streams that can scale up to handle any amount of data from thousands of producers. You can also use the Kinesis Client Library (KCL) or the Kinesis Data Streams API to write applications that process and analyze the data in the streams1.
Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. You can use DynamoDB to store the sensor data in nested JSON format, as DynamoDB supports document data types, such as lists and maps. You can also use DynamoDB to query the data with a latency of less than 10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of data. You can use the DynamoDB API or the AWS SDKs to perform queries on the data, such as using key-value lookups, scans, or queries2.
The solution that meets the requirements with the least operational overhead is to use Amazon Kinesis Data Streams to capture the sensor data and store the data in Amazon DynamoDB for querying. This solution has the following advantages:
It does not require you to provision, manage, or scale any servers, clusters, or queues, as Kinesis Data Streams and DynamoDB are fully managed services that handle all the infrastructure for you. This reduces the operational complexity and cost of running your solution.
It allows you to ingest sensor data in near real time, as Kinesis Data Streams can capture data records as they are produced and deliver them to your applications within seconds. You can also use Kinesis Data Firehose to load the data from the streams to DynamoDB automatically and continuously3.
It allows you to store the data in nested JSON format, as DynamoDB supports document data types, such as lists and maps. You can also use DynamoDB Streams to capture changes in the data and trigger actions, such as sending notifications or updating other databases.
It allows you to query the data with a latency of less than 10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of data. You can also use DynamoDB Accelerator (DAX) to improve the read performance by caching frequently accessed data.
Option A is incorrect because it suggests using a self-hosted Apache Kafka cluster to capture the sensor data and store the data in Amazon S3 for querying. This solution has the following disadvantages:
It requires you to provision, manage, and scale your own Kafka cluster, either on EC2 instances or on- premises servers. This increases the operational complexity and cost of running your solution.
It does not allow you to query the data with a latency of less than 10 milliseconds, as Amazon S3 is an object storage service that is not optimized for low-latency queries. You need to use another service, such as Amazon Athena or Amazon Redshift Spectrum, to query the data in S3, which may incur additional costs and latency.
Option B is incorrect because it suggests using AWS Lambda to process the sensor data and store the data in Amazon S3 for querying. This solution has the following disadvantages:
It does not allow you to ingest sensor data in near real time, as Lambda is a serverless compute service that runs code in response to events. You need to use another service, such as API Gateway or Kinesis Data Streams, to trigger Lambda functions with sensor data, which may add extra latency and complexity to your solution.
It does not allow you to query the data with a latency of less than 10 milliseconds, as Amazon S3 is an object storage service that is not optimized for low-latency queries. You need to use another service, such as Amazon Athena or Amazon Redshift Spectrum, to query the data in S3, which may incur additional costs and latency.
Option D is incorrect because it suggests using Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data and use AWS Glue to store the data in Amazon RDS for querying. This solution has the following disadvantages:
It does not allow you to ingest sensor data in near real time, as Amazon SQS is a message queue service that delivers messages in a best-effort manner. You need to use another service, such as Lambda or EC2, to poll the messages from the queue and process them, which may add extra latency and complexity to your solution.
It does not allow you to store the data in nested JSON format, as Amazon RDS is a relational database service that supports structured data types, such as tables and columns. You need to use another service, such as AWS Glue, to transform the data from JSON to relational format, which may add extra cost and overhead to your solution.
1: Amazon Kinesis Data Streams - Features
2: Amazon DynamoDB - Features
3: Loading Streaming Data into Amazon DynamoDB - Amazon Kinesis Data Firehose
[4]: Capturing Table Activity with DynamoDB Streams - Amazon DynamoDB
[5]: Amazon DynamoDB Accelerator (DAX) - Features
[6]: Amazon S3 - Features
[7]: AWS Lambda - Features
[8]: Amazon Simple Queue Service - Features
[9]: Amazon Relational Database Service - Features
[10]: Working with JSON in Amazon RDS - Amazon Relational Database Service
[11]: AWS Glue - Features
질문 # 206
A banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.
A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.
Which solution will meet this requirement?
정답：A
설명：
For exactly-once delivery and processing in Amazon Kinesis Data Streams, the best approach is to design the application so that it handlesidempotency. By embedding aunique IDin each record, the application can identify and remove duplicate records during processing.
* Exactly-Once Processing:
* Kinesis Data Streams does not natively support exactly-once processing. Therefore,idempotency should be designed into the application, ensuring that each record has a unique identifier so that the same event is processed only once, even if it is ingested multiple times.
* This pattern is widely used for achieving exactly-once semantics in distributed systems.
Reference:Building Idempotent Applications with Kinesis
Alternatives Considered:
B (Checkpoint configuration): While updating the checkpoint configuration can help with some aspects of duplicate processing, it is not a full solution for exactly-once delivery.
C (Design data source): Ensuring events are not ingested multiple times is ideal, but network outages can make this difficult, and it doesn't guarantee exactly-once delivery.
D (Using EMR): While using EMR with Flink or Spark could work, it introduces unnecessary complexity compared to handling idempotency at the application level.
References:
Amazon Kinesis Best Practices for Exactly-Once Processing
Achieving Idempotency with Amazon Kinesis
질문 # 207
A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01.
A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.
Which solution will meet these requirements with the LEAST latency?
정답：B
설명：
The best solution to ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket with the least latency is to use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create partition API call. This way, the Data Catalog is updated as soon as new data is written to S3, and the partition information is immediately available for querying by other services. The Boto3 AWS Glue create partition API call allows you to create a new partition in the Data Catalog by specifying the table name, the database name, and the partition values1. You can use this API call in your code that writes data to S3, such as a Python script or an AWS Glue ETL job, to create a partition for each new S3 object key that matches the partitioning scheme.
Option A is not the best solution, as scheduling an AWS Glue crawler to run every morning would introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. Crawlers can be scheduled to run periodically, such as daily or hourly, but they cannot runcontinuously or in real-time.
Therefore, using a crawler to synchronize the Data Catalog with the S3 storage would not meet the requirement of the least latency.
Option B is not the best solution, as manually running the AWS Glue CreatePartition API twice each day would also introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. Moreover, manually running the API would require more operational overhead and human intervention than using code that writes data to S3 to invoke the API automatically.
Option D is not the best solution, as running the MSCK REPAIR TABLE command from the AWS Glue console would also introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. The MSCK REPAIR TABLE command is a SQL command that you can run in the AWS Glue console to add partitions to the Data Catalog based on the S3 object keys that match the partitioning scheme3. However, this command is not meant to be run frequently or in real-time, as it can take a long time to scan the entire S3 bucket and add the partitions. Therefore, using this command to synchronize the Data Catalog with the S3 storage would not meet the requirement of the least latency. References:
AWS Glue CreatePartition API
Populating the AWS Glue Data Catalog
MSCK REPAIR TABLE Command
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
질문 # 208
A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data.
Which solution will meet these requirements MOST cost-effectively?
정답：A
설명：
The best solution to meet the requirements of creating a data catalog that includes the IoT data, and allowing the analytics department to index the data, most cost-effectively, is to create an Amazon Athena workgroup, explore the data that is in Amazon S3 by using Apache Spark through Athena, and provide the Athena workgroup schema and tables to the analytics department.
Amazon Athena is a serverless, interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL or Python1. Amazon Athena also supports Apache Spark, an open-source distributed processing framework that can run large-scale data analytics applications across clusters of servers2. You can use Athena to run Spark code on data in Amazon S3 without having to set up, manage, or scale anyinfrastructure. You can also use Athena to create and manage external tables that pointto your data in Amazon S3, and store them in an external data catalog, such as AWS Glue Data Catalog, Amazon Athena Data Catalog, or your own Apache Hive metastore3. You can create Athena workgroups to separate query execution and resource allocation based on different criteria, such as users, teams, or applications4. You can share the schemas and tables in your Athena workgroup with other users or applications, such as Amazon QuickSight, for data visualization and analysis5.
Using Athena and Spark to create a data catalog and explore the IoT data in Amazon S3 is the most cost- effective solution, as you pay only for the queries you run or the compute you use, and you pay nothing when the service is idle1. You also save on the operational overhead and complexity of managing data warehouse infrastructure, as Athena and Spark are serverless and scalable. You can also benefit from the flexibility and performance of Athena and Spark, as they support various data formats, including JSON, and can handle schema changes and complex queries efficiently.
Option A is not the best solution, as creating an AWS Glue Data Catalog, configuring an AWS Glue Schema Registry, creating a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless, would incur more costs and complexity than using Athena and Spark. AWS Glue Data Catalog is a persistent metadata store that contains table definitions, job definitions, and other control information to help you manage your AWS Glue components6. AWS Glue Schema Registry is a service that allows you to centrally store and manage the schemas of your streaming data in AWS Glue Data Catalog7. AWS Glue is a serverless data integration service that makes it easy to prepare, clean, enrich, and move data between data stores8. Amazon Redshift Serverless is a feature of Amazon Redshift, a fully managed data warehouse service, that allows you to run and scale analytics without having to manage data warehouse infrastructure9. While these services are powerful and useful for many data engineering scenarios, they are not necessary or cost-effective for creating a data catalog and indexing the IoT data in Amazon S3. AWS Glue Data Catalog and Schema Registry charge you based on the number of objects stored and the number of requests made67. AWS Glue charges you based on the compute time and the data processed by your ETL jobs8. Amazon Redshift Serverless charges you based on the amount of data scanned by your queries and the compute time used by your workloads9. These costs can add up quickly, especially if you have large volumes of IoT data and frequent schema changes. Moreover, using AWS Glue and Amazon Redshift Serverless would introduce additional latency and complexity, as you would have to ingest the data from Amazon S3 to Amazon Redshift Serverless, and then query it from there, instead of querying it directly from Amazon S3 using Athena and Spark.
Option B is not the best solution, as creating an Amazon Redshift provisioned cluster, creating an Amazon Redshift Spectrum database for the analytics department to explorethe data that is in Amazon S3, and creating Redshift stored procedures to load the data into Amazon Redshift, would incur more costs and complexity than using Athena and Spark. Amazon Redshift provisioned clusters are clusters that you create and manage by specifying the number and type of nodes, and the amount of storage and compute capacity10. Amazon Redshift Spectrum is a feature of Amazon Redshift that allows you to query and join data across your data warehouse and your data lake using standard SQL11. Redshift stored procedures are SQL statements that you can define and store in Amazon Redshift, and then call them by using the CALL command12. While these features are powerful and useful for many data warehousing scenarios, they are not necessary or cost-effective for creating a data catalog and indexing the IoT data in Amazon S3. Amazon Redshift provisioned clusters charge you based on the node type, the number of nodes, and the duration of the cluster10. Amazon Redshift Spectrum charges you based on the amount of data scanned by your queries11. These costs can add up quickly, especially if you have large volumes of IoT data and frequent schema changes. Moreover, using Amazon Redshift provisioned clusters and Spectrum would introduce additional latency and complexity, as you would have to provision and manage the cluster, create an external schema and database for the data in Amazon S3, and load the data into the cluster using stored procedures, instead of querying it directly from Amazon S3 using Athena and Spark.
Option D is not the best solution, as creating an AWS Glue Data Catalog, configuring an AWS Glue Schema Registry, creating AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API, and creating an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless, would incur more costs and complexity than using Athena and Spark. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers13. AWS Lambda UDFs are Lambda functions that you can invoke from within an Amazon Redshift query. Amazon Redshift Data API is a service that allows you to run SQL statements on Amazon Redshift clusters using HTTP requests, without needing a persistent connection. AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. While these services are powerful and useful for many data engineering scenarios, they are not necessary or cost-effective for creating a data catalog and indexing the IoT data in Amazon S3. AWS Glue Data Catalog and Schema Registry charge you based on thenumber of objects stored and the number of requests made67. AWS Lambda charges you based on the number of requests and the duration of your functions13. Amazon Redshift Serverless charges you based on the amount of data scanned by your queries and the compute time used by your workloads9. AWS Step Functions charges you based on the number of state transitions in your workflows. These costs can add up quickly, especially if you have large volumes of IoT data and frequent schema changes. Moreover, using AWS Glue, AWS Lambda, Amazon Redshift Data API, and AWS Step Functions would introduce additionallatency and complexity, as you would have to create and invoke Lambda functions to ingest the data from Amazon S3 to Amazon Redshift Serverless using the Data API, and coordinate the ingestion process using Step Functions, instead of querying it directly from Amazon S3 using Athena and Spark. References:
* What is Amazon Athena?
* Apache Spark on Amazon Athena
* Creating tables, updating the schema, and adding new partitions in the Data Catalog from AWS Glue ETL jobs
* Managing Athena workgroups
* Using Amazon QuickSight to visualize data in Amazon Athena
* AWS Glue Data Catalog
* AWS Glue Schema Registry
* What is AWS Glue?
* Amazon Redshift Serverless
* Amazon Redshift provisioned clusters
* Querying external data using Amazon Redshift Spectrum
* Using stored procedures in Amazon Redshift
* What is AWS Lambda?
* [Creating and using AWS Lambda UDFs]
* [Using the Amazon Redshift Data API]
* [What is AWS Step Functions?]
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
질문 # 209
......
Itexamdump를 선택함으로 여러분은 Amazon 인증Data-Engineer-Associate시험에 대한 부담은 사라질 것입니다.우리 Itexamdump는 끊임없는 업데이트로 항상 최신버전의 Amazon 인증Data-Engineer-Associate시험덤프임을 보장해드립니다.만약 덤프품질을 확인하고 싶다면Itexamdump 에서 무료로 제공되는Amazon 인증Data-Engineer-Associate덤프의 일부분 문제를 체험하시면 됩니다.Itexamdump 는 100%의 보장도를 자랑하며Amazon 인증Data-Engineer-Associate시험을 한번에 패스하도록 도와드립니다.
Data-Engineer-Associate퍼펙트 최신 덤프공부자료: https://www.itexamdump.com/Data-Engineer-Associate.html
참고: Itexamdump에서 Google Drive로 공유하는 무료, 최신 Data-Engineer-Associate 시험 문제집이 있습니다: https://drive.google.com/open?id=1ZvSqUPpN0fy6Fxn8b06LpWhJexlVGp_3