시험패스가능한DEA-C01인기자격증덤프문제최신공부자료

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Real Exam Qty:	Approximately 65 questions
Certificate Validity Period:	2 years
Passing Score:	Not publicly disclosed (Snowflake uses scaled scoring)
Exam Duration:	115 minutes
Exam Format:	Multiple choice, Multiple select, Scenario-based questions
Exam Price:	$375 USD
Available Languages:	English
Related Certifications:	SnowPro Core Certification
Recommended Training:	SnowPro Advanced Data Engineer Exam Guide Snowflake University Training
Exam Registration:	Snowflake Certification Portal
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored or authorized testing center
Pre Condition:	Recommended: SnowPro Core Certification or equivalent Snowflake experience
Official Syllabus URL:	https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/

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Snowflake DEA-C01 시험요강:

주제	소개
주제 1	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 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	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 4	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.
주제 5	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.

최신 SnowPro Advanced DEA-C01 무료샘플문제 (Q341-Q346):

질문 # 341
A company has developed an application in which Amazon Managed Service for Apache Flink reads streaming data from Amazon Kinesis Data Streams and performs an analysis. A parallelism of 10 is defined for subtasks in the Apache Flink application, while 2 numbers of shards are defined in the Amazon Kinesis Data Streams. The developer team is observing idle subtasks due to this configuration. They are looking to distribute the load across all subtasks.
What configuration settings can be done to resolve this issue?

A. Increase the number of shards in the Amazon Kinesis Data Stream to match the count of subtasks.
B. Decrease the number of subtasks in the Apache Flink application.
C. Disable deserialization at the source and rebalance the data to distribute it across all the tasks.
Then, deserialize the data at the task level.
D. Enable serialization at the source and rebalance the data to distribute across all the tasks.Serialize data at the task level.

정답：A

설명：
Shards and Parallelism: In Amazon Kinesis Data Streams, each shard provides a unit of parallelism. With only 2 shards and 10 subtasks, there are not enough shards to distribute the load evenly across all subtasks. Increasing the number of shards to match the number of subtasks (10 in this case) will ensure that each subtask has data to process, thereby eliminating idle subtasks

질문 # 342
A company has an application that is deployed on AWS. The application uses Amazon Simple Notification Service (Amazon SNS) with multiple topics. The company's security team needs to be able to audit all Publish and PublishBatch API actions for all the SNS topics. The company's application team and security team must also be able to query the audit data. The company has already established an event data store in AWS CloudTrail Lake to collect all events.
Which solution will meet these requirements with the LEAST operational overhead?

A. Enable management events for the SNS topics. Use CloudTrail Lake to query the audit data.
B. Enable management events for the SNS topics. Create a table in AWS Glue Data Catalog. Query the data by using Amazon Athena.
C. Enable data events for the SNS topics. Use CloudTrail Lake to query the audit data.
D. Enable data events for the SNS topics. Create a table in AWS Glue Data Catalog. Query the data by using Amazon Athena.

정답：C

설명：
Amazon SNS Publish and PublishBatch actions are logged by AWS CloudTrail as data events for SNS topics, not as management events. Because the company already has an event data store in CloudTrail Lake, the lowest-overhead solution is to enable SNS data events for the topics and use CloudTrail Lake directly to query the audit records.

질문 # 343
What are Invalid rules applicable when using stored procedure contains transaction?

A. You cannot start a transaction inside the stored procedure, then complete the transac-tion after returning from the procedure.
B. If a transaction is started inside a stored procedure and is still active when the stored procedure finishes, then an error occurs, and the transaction is rolled back.
C. A transaction can be inside a stored procedure, or a stored procedure can be inside a transaction.
D. All Rules are Applicable.
E. You cannot start a transaction before calling the stored procedure, then complete the transaction inside the stored procedure.
F. A transaction inside a stored procedure can include a call to another stored procedure that contains a transaction.

정답：D

질문 # 344
While working with Multi Cluster Warehouses, Select the incorrect understanding of Data Engineer about its usage?

A. Unless you have a specific requirement for running in Maximized mode, multi-cluster warehouses should be configured to run in Auto-scale mode, which enables Snowflake to automatically start and stop clusters as needed.
B. Multi-cluster warehouses generally improve query performance, particularly for larger, more complex queries.
C. When choosing the maximum number of clusters for a multi-cluster warehouse set its value as large as possible.
D. When choosing the minimum number of clusters for a multi-cluster warehouse keep the default value as
1.
E. Multi-cluster warehouses are designed specifically for handling queuing and perfor-mance issues related to large numbers of concurrent users and/or queries.

정답：B

설명：
Explanation
Resizing a warehouse generally improves query performance, particularly for larger, more complex queries.
Multi-cluster warehouses are designed specifically for handling queuing and performance issues re-lated to large numbers of concurrent users and/or queries. In addition, multi-cluster warehouses can help automate this process if your number of users/queries tend to fluctuate.
Rest all are correct understanding.

질문 # 345
A company has an extensive script in Scala that transforms data by leveraging DataFrames. A Data engineer needs to move these transformations to Snowpark.
...characteristics of data transformations in Snowpark should be considered to meet this requirement? (Select TWO)

A. User-Defined Functions (UDFs) are not pushed down to Snowflake
B. Columns in different DataFrames with the same name should be referred to with squared brackets
C. Snowpark requires a separate cluster outside of Snowflake for computations
D. Snowpark operations are executed lazily on the server.
E. It is possible to join multiple tables using DataFrames.

정답：D,E

설명：
Explanation
The characteristics of data transformations in Snowpark that should be considered to meet this requirement are:
It is possible to join multiple tables using DataFrames.
Snowpark operations are executed lazily on the server.
These characteristics indicate how Snowpark can perform data transformations using DataFrames, which are similar to the ones used in Scala. DataFrames are distributed collections of rows that can be manipulated using various operations, such as joins, filters, aggregations, etc. DataFrames can be created from different sources, such as tables, files, or SQL queries. Snowpark operations are executed lazily on the server, which means that they are not performed until an action is triggered, such as a write or a collect operation. This allows Snowpark to optimize the execution plan and reduce the amount of data transferred between the client and the server.
The other options are not characteristics of data transformations in Snowpark that should be considered to meet this requirement. Option C is incorrect because User-Defined Functions (UDFs) are pushed down to Snowflake and executed on the server. Option D is incorrect because Snowpark does not require a separate cluster outside of Snowflake for computations, but rather uses virtual warehouses within Snowflake. Option E is incorrect because columns in different DataFrames with the same name should be referred to with dot notation, not squared brackets.

질문 # 346
......

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