SNOWFLAKE DEA-C01 PRACTICE TEST - 100% EXAM PASSING GUARANTEE (2025)

Snowflake DEA-C01 Practice Test - 100% Exam Passing Guarantee (2025)

Snowflake DEA-C01 Practice Test - 100% Exam Passing Guarantee (2025)

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Tags: DEA-C01 Hot Questions, DEA-C01 Valid Mock Exam, DEA-C01 Latest Dumps Book, DEA-C01 Download Demo, DEA-C01 Latest Dumps

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 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.
Topic 3
  • 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.
Topic 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.
Topic 5
  • 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.

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100% Pass Quiz Snowflake - DEA-C01 Fantastic Hot Questions

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
Find out the odd one out:

  • A. 1. Bulk Data Load: Loads are always performed in a single transaction.
    2. SnowPipe: Loads are combined or split into a single or multiple transactions based on the number and size of the rows in each data file.
  • B. 1. Bulk Data Load: Load history Stored in the metadata of the target table for 365 days.
    2. SnowPipe: Load history Stored in the metadata of the pipe for 64 days.
  • C. 1. Bulk Data Load: Requires a user-specified warehouse to execute COPY statements.
    2. SnowPipe: Uses Snowflake-supplied compute resources.
  • D. 1. Bulk Data Load: Billed for the amount of time each virtual warehouse is active.
    2. SnowPipe: Billed according to the compute resources used in the Snowpipe ware-house while loading the files.

Answer: B

Explanation:
Explanation
Bulk data load
Load History Stored in the metadata of the target table for 64 days. Available upon completion of the COPY statement as the statement output.
Snowpipe
Load History Stored in the metadata of the pipe for 14 days. Must be requested from Snowflake via a REST endpoint, SQL table function, or ACCOUNT_USAGE view.
Rest are correct statements.


NEW QUESTION # 92
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

  • A. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
  • B. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.
  • C. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
  • D. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.

Answer: D

Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api-monitoring-events.html


NEW QUESTION # 93
A CSV file around 1 TB in size is generated daily on an on-premise server A corresponding table. Internal stage, and file format have already been created in Snowflake to facilitate the data loading process How can the process of bringing the CSV file into Snowflake be automated using the LEAST amount of operational overhead?

  • A. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage. Create a pipe that runs a copy into statement that references the internal stage Snowpipe auto-ingest will automatically load the file from the internal stage when the new file lands in the internal stage.
  • B. Create a task in Snowflake that executes once a day and runs a copy into statement that references the internal stage The internal stage will read the files directly from the on-premise server and copy the newest file into the table from the on-premise server to the Snowflake table
  • C. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage Create a task that executes once a day m Snowflake and runs a OOPY WTO statement that references the internal stage Schedule the task to start after the file lands in the internal stage
  • D. On the on premise server schedule a Python file that uses the Snowpark Python library. The Python script will read the CSV data into a DataFrame and generate an insert into statement that will directly load into the table The script will bypass the need to move a file into an internal stage

Answer: A

Explanation:
Explanation
This option is the best way to automate the process of bringing the CSV file into Snowflake with the least amount of operational overhead. SnowSQL is a command-line tool that can be used to execute SQL statements and scripts on Snowflake. By scheduling a SQL file that executes a PUT command, the CSV file can be pushed from the on-premise server to the internal stage in Snowflake. Then, by creating a pipe that runs a COPY INTO statement that references the internal stage, Snowpipe can automatically load the file from the internal stage into the table when it detects a new file in the stage. This way, there is no need to manually start or monitor a virtual warehouse or task.


NEW QUESTION # 94
Can the same column be specified in both a Dynamic data masking policy signature and a row ac-cess policy signature at the same time?

  • A. YES
  • B. NO

Answer: B


NEW QUESTION # 95
Which ones are the false statements about Materialized Views?

  • A. Snowflake does not allow users to truncate materialized views.
  • B. Materialized views can be secure views.
  • C. Snowflake does not allow standard DML (e.g. INSERT, UPDATE, DELETE) on ma-terialized views.
  • D. Clustering a subset of the materialized views on a table tends to be more cost-effective than clustering the table itself.
  • E. A materialized view can also be used as the data source for a subquery.
  • F. Materialized views are first-class account objects.

Answer: F

Explanation:
Explanation
Materialized views are first-class Database objects & rest of the understandings are true.


NEW QUESTION # 96
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