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퍼펙트한Data-Engineer-Associate합격보장가능덤프최신버전덤프데모문제

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DumpTOP의 높은 적중율을 보장하는 최고품질의Amazon Data-Engineer-Associate덤프는 최근Amazon Data-Engineer-Associate실제인증시험에 대비하여 제작된것으로 엘리트한 전문가들이 실제시험문제를 분석하여 답을 작성한 만큼 시험문제 적중율이 아주 높습니다. DumpTOP의 Amazon Data-Engineer-Associate 덤프는Amazon Data-Engineer-Associate시험을 패스하는데 가장 좋은 선택이기도 하고Amazon Data-Engineer-Associate인증시험을 패스하기 위한 가장 힘이 되어드리는 자료입니다.

DumpTOP의 Amazon인증 Data-Engineer-Associate덤프를 구매하시고 공부하시면 밝은 미래를 예약한것과 같습니다. DumpTOP의 Amazon인증 Data-Engineer-Associate덤프는 고객님이 시험에서 통과하여 중요한 IT인증자격증을 취득하게끔 도와드립니다. IT인증자격증은 국제적으로 인정받기에 취직이나 승진 혹은 이직에 힘을 가해드립니다. 학원공부나 다른 시험자료가 필요없이DumpTOP의 Amazon인증 Data-Engineer-Associate덤프만 공부하시면Amazon인증 Data-Engineer-Associate시험을 패스하여 자격증을 취득할수 있습니다.

>> Data-Engineer-Associate합격보장 가능 덤프 <<

Data-Engineer-Associate합격보장 가능 덤프 덤프는 시험패스에 가장 좋은 공부자료

DumpTOP는 고품질의 IT Amazon Data-Engineer-Associate시험공부자료를 제공하는 차별화 된 사이트입니다. DumpTOP는Amazon Data-Engineer-Associate응시자들이 처음 시도하는Amazon Data-Engineer-Associate시험에서의 합격을 도와드립니다. 가장 적은 시간은 투자하여 어려운Amazon Data-Engineer-Associate시험을 통과하여 자격증을 많이 취득하셔서 IT업계에서 자신만의 가치를 찾으세요.

최신 AWS Certified Data Engineer Data-Engineer-Associate 무료샘플문제 (Q125-Q130):

질문 # 125
A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

A. Use Athena partition projection based on the S3 bucket prefix.
B. Bucketthe data based on a column thatthe data have in common in a WHERE clause of the user query
C. Create an AWS Glue partition index. Enable partition filtering.
D. Transform the data that is in the S3 bucket to Apache Parquet format.
E. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.

정답：A,C

설명：
The best solutions to resolve the performance bottleneck and reduce Athena query planning time are to create an AWS Glue partition index and enable partition filtering, and to use Athena partition projection based on the S3 bucket prefix.
AWS Glue partition indexes are a feature that allows you to speed up query processing of highly partitioned tables cataloged in AWS Glue Data Catalog. Partition indexes are available for queries in Amazon EMR, Amazon Redshift Spectrum, and AWS Glue ETL jobs. Partition indexes are sublists of partition keys defined in the table. When you create a partition index, you specify a list of partition keys that already exist on a given table. AWS Glue then creates an index for the specified keys and stores it in the Data Catalog. When you run a query that filters on the partition keys, AWS Glue uses the partition index to quickly identify the relevant partitions without scanning the entiretable metadata. This reduces the query planning time and improves the query performance1.
Athena partition projection is a feature that allows you to speed up query processing of highly partitioned tables and automate partition management. In partition projection, Athena calculates partition values and locations using the table properties that you configure directly on your table in AWS Glue. The table properties allow Athena to 'project', or determine, the necessary partition information instead of having to do a more time-consuming metadata lookup in the AWS Glue Data Catalog. Because in-memory operations are often faster than remote operations, partition projection can reduce the runtime of queries against highly partitioned tables. Partition projection also automates partition management because it removes the need to manually create partitions in Athena, AWS Glue, or your external Hive metastore2.
Option B is not the best solution, as bucketing the data based on a column that the data have in common in a WHERE clause of the user query would not reduce the query planning time. Bucketing is a technique that divides data into buckets based on a hash function applied to a column. Bucketing can improve the performance of join queries by reducing the amount of data that needs to be shuffled between nodes. However, bucketing does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario3.
Option D is not the best solution, as transforming the data that is in the S3 bucket to Apache Parquet format would not reduce the query planning time. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, Parquet does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario4.
Option E is not the best solution, as using the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects would not reduce the query planning time. S3DistCP is a tool that can copy large amounts of data between Amazon S3 buckets or from HDFS to Amazon S3. S3DistCP can also aggregate smaller files into larger files to improve the performance of sequential access. However, S3DistCP does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario5. References:
Improve query performance using AWS Glue partition indexes
Partition projection with Amazon Athena
Bucketing vs Partitioning
Columnar Storage Formats
S3DistCp
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

질문 # 126
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

A. Use Hadoop Distributed File System (HDFS) as a persistent data store.
B. Use Graviton instances for core nodes and task nodes.
C. Use Spot Instances for all primary nodes.
D. Use x86-based instances for core nodes and task nodes.
E. Use Amazon S3 as a persistent data store.

정답：B,E

설명：
The best combination of resources to meet the requirements of high reliability, cost-optimization, and performance for running Apache Spark jobs on Amazon EMR is to use Amazon S3 as a persistent data store and Graviton instances for core nodes and task nodes.
Amazon S3 is a highly durable, scalable, and secure object storage service that can store any amount of data for a variety of use cases, including big data analytics1. Amazon S3 is a better choice than HDFS as a persistent data store for Amazon EMR, as it decouples the storage from the compute layer, allowing for more flexibility and cost-efficiency. Amazon S3 also supports data encryption, versioning, lifecycle management, and cross-region replication1. Amazon EMR integrates seamlessly with Amazon S3, using EMR File System (EMRFS) to access data stored in Amazon S3 buckets2. EMRFS also supports consistent view, which enables Amazon EMR to provide read-after-write consistency for Amazon S3 objects that are accessed through EMRFS2.
Graviton instances are powered by Arm-based AWS Graviton2 processors that deliver up to 40% better price performance over comparable current generation x86-based instances3. Graviton instances are ideal for running workloads that are CPU-bound, memory-bound, or network-bound, such as big data analytics, web servers, and open-source databases3. Graviton instances are compatible with Amazon EMR, and can be used for both core nodes and task nodes. Core nodes are responsible for running the data processing frameworks, such as Apache Spark, and storing data in HDFS or the local file system. Task nodes are optional nodes that can be added to a cluster to increase the processing power and throughput. By using Graviton instances for both core nodes and task nodes, you can achieve higher performance and lower cost than using x86-based instances.
Using Spot Instances for all primary nodes is not a good option, as it can compromise the reliability and availability of the cluster. Spot Instances are spare EC2 instances that are available at up to 90% discount compared to On-Demand prices, but they can be interrupted by EC2 with a two-minute notice when EC2 needs the capacity back. Primary nodes are the nodes that run the cluster software, such as Hadoop, Spark, Hive, and Hue, and are essential for the cluster operation. If a primary node is interrupted by EC2, the cluster will fail or become unstable. Therefore, it is recommended to use On-Demand Instances or Reserved Instances for primary nodes, and use Spot Instances only for task nodes that can tolerate interruptions. Reference:
Amazon S3 - Cloud Object Storage
EMR File System (EMRFS)
AWS Graviton2 Processor-Powered Amazon EC2 Instances
[Plan and Configure EC2 Instances]
[Amazon EC2 Spot Instances]
[Best Practices for Amazon EMR]

질문 # 127
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?

A. Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function's alias.
B. Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.
C. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
D. Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.

정답：C

설명：
Lambda layers are a way to share code and dependencies across multiple Lambda functions. By packaging the custom Python scripts into Lambda layers, the data engineer can update the scripts in one place and have them automatically applied to all the Lambda functions that use the layer. This reduces the manual effort and ensures consistency across the Lambda functions. The other options are either not feasible or not efficient. Storing a pointer to the custom Python scripts in the execution context object or in environment variables would require the Lambda functions to download the scripts from Amazon S3 every time they are invoked, which would increase latency and cost. Assigning the same alias to each Lambda function would not help with updating the Python scripts, as the alias only points to a specific version of the Lambda function code. Reference:
AWS Lambda layers
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.4: AWS Lambda

질문 # 128
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with the sales opportunities. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the process.
B. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to fetch both datasets. Use AWS Lambda functions to correlate the datasets. Use AWS Step Functions to orchestrate the process.
C. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use Amazon Kinesis Data Streams to fetch sales records from the MySQL database. Use Amazon Managed Service for Apache Flink to correlate the datasets. Use AWS Step Functions to orchestrate the process.
D. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with sales opportunities. Use AWS Step Functions to orchestrate the process.

정답：D

설명：
Problem Analysis:
The company processes 2 GB of daily sales records and 100 GB of Salesforce sales opportunities.
The goal is to analyze and correlate the two datasets with low operational overhead.
The process must run once nightly.
Key Considerations:
Amazon AppFlow simplifies data integration with Salesforce.
AWS Glue can extract data from MySQL and perform ETL operations.
Step Functions can orchestrate workflows with minimal manual intervention.
Apache Airflow and Flink add complexity, which conflicts with the requirement for low operational overhead.
Solution Analysis:
Option A: MWAA + Lambda + Step Functions
Requires custom Lambda code for dataset correlation, increasing development and operational complexity.
Option B: AppFlow + Glue + MWAA
MWAA adds orchestration overhead compared to the simpler Step Functions.
Option C: AppFlow + Glue + Step Functions
AppFlow fetches Salesforce data, Glue extracts MySQL data, and Step Functions orchestrate the entire process.
Minimal setup and operational overhead, making it the best choice.
Option D: AppFlow + Kinesis + Flink + Step Functions
Using Kinesis and Flink for batch processing introduces unnecessary complexity.
Final Recommendation:
Use Amazon AppFlow to fetch Salesforce data, AWS Glue to process MySQL data, and Step Functions for orchestration.
Reference:
Amazon AppFlow Overview
AWS Glue ETL Documentation
AWS Step Functions

질문 # 129
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?

A. Add HAVING sumfsales amount) > 0 after the GROUP BY clause.
B. Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.
C. Remove the GROUP BY clause
D. Replace sum(sales amount) with count(*J for the aggregation.

정답：B

설명：
The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the query will return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products. References:
* Troubleshooting JSON queries - Amazon Athena (Section: JSON related errors)
* When I query a table in Amazon Athena, the TIMESTAMP result is empty (Section: Resolution)
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)

질문 # 130
......

저희는 수많은 IT자격증시험에 도전해보려 하는 IT인사들께 편리를 가져다 드리기 위해 Amazon Data-Engineer-Associate실제시험 출제유형에 근거하여 가장 퍼펙트한 시험공부가이드를 출시하였습니다. 많은 사이트에서 판매하고 있는 시험자료보다 출중한DumpTOP의 Amazon Data-Engineer-Associate덤프는 실제시험의 거의 모든 문제를 적중하여 고득점으로 시험에서 한방에 패스하도록 해드립니다. Amazon Data-Engineer-Associate시험은DumpTOP제품으로 간편하게 도전해보시면 후회없을 것입니다.

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지난 몇년동안 IT산업의 지속적인 발전과 성장을 통해 Data-Engineer-Associate시험은 IT인증시험중의 이정표로 되어 많은 인기를 누리고 있습니다, Data-Engineer-Associate시험은 최근 제일 인기있는 인증시험입니다, 무료샘플을 보시면DumpTOP Amazon인증Data-Engineer-Associate시험대비자료에 믿음이 갈것입니다.고객님의 이익을 보장해드리기 위하여DumpTOP는 시험불합격시 덤프비용전액환불을 무조건 약속합니다, Data-Engineer-Associate덤프로 Data-Engineer-Associate시험을 패스하여 자격증을 취득하면 정상에 오를수 있습니다, Amazon Data-Engineer-Associate합격보장 가능 덤프 학원에 등록하자니 시간도 없고 돈도 많이 들고 쉽게 엄두가 나지 않는거죠, 아직도 Data-Engineer-Associate덤프구매를 망설이고 있다면 우선 해당 덤프 구매사이트에서 Data-Engineer-Associate덤프 무료샘플을 다운받아 보세요.

깨똑이 왔네에, 제가 약속을 어겼습니다, 지난 몇년동안 IT산업의 지속적인 발전과 성장을 통해 Data-Engineer-Associate시험은 IT인증시험중의 이정표로 되어 많은 인기를 누리고 있습니다, Data-Engineer-Associate시험은 최근 제일 인기있는 인증시험입니다.

최신 Data-Engineer-Associate합격보장 가능 덤프 덤프샘플 다운

무료샘플을 보시면DumpTOP Amazon인증Data-Engineer-Associate시험대비자료에 믿음이 갈것입니다.고객님의 이익을 보장해드리기 위하여DumpTOP는 시험불합격시 덤프비용전액환불을 무조건 약속합니다, Data-Engineer-Associate덤프로 Data-Engineer-Associate시험을 패스하여 자격증을 취득하면 정상에 오를수 있습니다.

학원에 등록하자니 시간도 없고 돈도 많이 들고 쉽게 엄두가 나지 않는거죠?

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