시험패스가능한DEA-C01인증공부문제최신덤프자료

2026 ITDumpsKR 최신 DEA-C01 PDF 버전 시험 문제집과 DEA-C01 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1-vc3R1xWHiDnOlgcEC4lZGMukRx5KvqA

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Exam Price:	$375 USD
Exam Format:	Multiple Choice, Multiple Select
Passing Score:	~70% (exact passing score not publicly disclosed by Snowflake)
Related Certifications:	SnowPro Core Certification (Recommended Prerequisite)
Certificate Validity Period:	2 years (recertification required)
Exam Duration:	90 minutes
Available Languages:	English
Real Exam Qty:	approximately 50 questions
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored exam (via Pearson VUE) or in-person testing center
Pre Condition:	SnowPro Core Certification is strongly recommended before attempting this advanced-level exam. Practical experience with Snowflake data engineering projects is essential.
Official Syllabus URL:	https://www.snowflake.com/certification/

>> DEA-C01인증공부문제 <<

DEA-C01시험패스 인증덤프공부 & DEA-C01퍼펙트 공부자료

Snowflake DEA-C01 시험을 보시는 분이 점점 많아지고 있는데 하루빨리 다른 분들보다 Snowflake DEA-C01시험을 패스하여 자격증을 취득하는 편이 좋지 않을가요? 자격증이 보편화되면 자격증의 가치도 그만큼 떨어지니깐요. Snowflake DEA-C01덤프는 이미 많은분들의 시험패스로 검증된 믿을만한 최고의 시험자료입니다.

Snowflake DEA-C01 시험요강:

주제	소개
주제 1	Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
주제 2	Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
주제 3	Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
주제 4	Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
주제 5	Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.

최신 SnowPro Advanced DEA-C01 무료샘플문제 (Q218-Q223):

질문 # 218
Mark the Correct Statements:
Statement 1. Enable failover for a primary database to one or more accounts in your organization using an ALTER DATABASE ... ENABLE FAILOVER TO ACCOUNTS statement.
Statement 2. Enabling failover for a primary database can be done by Data Engineer either before or after a replica of the primary database has been created in a specified account.

A. Statement 1
B. Both are False.
C. Statement 2
D. Both are Correct.

정답：D

질문 # 219
A company has a business intelligence platform on AWS. The company uses an AWS Storage Gateway Amazon S3 File Gateway to transfer files from the company's on-premises environment to an Amazon S3 bucket.
A data engineer needs to setup a process that will automatically launch an AWS Glue workflow to run a series of AWS Glue jobs when each file transfer finishes successfully.
Which solution will meet these requirements with the LEAST operational overhead?

A. Set up an on-demand AWS Glue workflow so that the data engineer can start the AWS Glue workflow when each file transfer is complete.
B. Set up an AWS Lambda function that will invoke the AWS Glue Workflow. Set up an event for the creation of an S3 object as a trigger for the Lambda function.
C. Determine when the file transfers usually finish based on previous successful file transfers. Set up an Amazon EventBridge scheduled event to initiate the AWS Glue jobs at that time of day.
D. Set up an Amazon EventBridge event that initiates the AWS Glue workflow after every successful S3 File Gateway file transfer event.

정답：D

설명：
Using EventBridge directly to trigger the AWS Glue workflow upon S3 events is straightforward and leverages AWS's event-driven architecture, requiring minimal maintenance.

질문 # 220
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
B. Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
C. Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.
D. Write a custom extract, transform, and load (ETL) job in Python. Use the DataFrame.drop_duplicates() function by importing the Pandas library to perform data deduplication.

정답：C

질문 # 221
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)

A. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
B. Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
C. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.
D. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
E. Use an S3 bucket that is in the same account that uses Athena to query the data.

정답：C,D

설명：
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html

질문 # 222
A company utilizes AWS Glue for ETL (Extract, Transform, Load) jobs to process large volumes of data stored in Amazon S3. Recently, they have been experiencing performance issues with their Glue jobs, resulting in longer processing times and delays in data delivery. Upon investigation, they found that the bottleneck occurred during data extraction from S3. Which of the following could be the potential cause of this performance issue?

A. The size and structure of the S3 data files are causing overhead during data extraction, resulting in longer processing times for Glue jobs.
B. The underlying network bandwidth between AWS Glue and Amazon S3 is insufficient, causing slow data transfer rates and impacting job performance.
C. There are issues with the IAM (Identity and Access Management) roles associated with the Glue job, leading to permission errors or delays in accessing S3 data.
D. The Glue job's configuration lacks optimization for parallelism, causing inefficient data extraction from S3, leading to longer processing times.

정답：D

설명：
The bottleneck occurs during data extraction from S3, indicating a potential issue with the Glue job's configuration in this scenario. If the job is not optimized for parallelism, it may not efficiently utilize available resources, leading to slower data extraction. Optimizing the Glue job's configuration to enable parallel processing can improve the efficiency of data extraction from S3, reducing processing times.
Reference:
https://docs.aws.amazon.com/glue/latest/dg/optimize-spark-etl-job-performance.html

질문 # 223
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DEA-C01시험패스 인증덤프공부: https://www.itdumpskr.com/DEA-C01-exam.html

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