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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Available Languages:	Japanese, English
Exam Format:	Multiple select, Multiple choice
Exam Price:	$375 USD
Real Exam Qty:	65
Passing Score:	750 / 1000
Certificate Validity Period:	2 years
Exam Duration:	115 minutes
Related Certifications:	SnowPro Advanced: Architect SnowPro Advanced: Security Engineer SnowPro Core SnowPro Advanced: Data Analyst SnowPro Advanced: Administrator SnowPro Advanced: Data Scientist
Recommended Training:	Snowflake University Training Courses SnowPro Advanced: Data Engineer Exam Study Guide
Exam Registration:	Snowflake Certification Portal Pearson VUE Registration
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored or onsite at Pearson VUE test centers
Pre Condition:	Must hold active SnowPro Core Certification; recommended 2+ years hands-on data engineering experience
Official Syllabus URL:	https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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	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.
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	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q136-Q141):

NEW QUESTION # 136
To help manage STAGE storage costs, Data engineer recommended to monitor stage files and re-move them from the stages once the data has been loaded and the files which are no longer needed. Which option he can choose to remove these files either during data loading or afterwards?

A. He can choose to remove stage files during data loading (using the COPY INTO <table> command).
B. Files no longer needed, can be removed using the REMOVE command.
C. Script can be used during data loading & post data loading with DELETE command.
D. Files no longer needed, can be removed using the PURGE=TRUE command.

Answer: A,D

Explanation:
Explanation
Managing Data Files
Staged files can be deleted from a Snowflake stage (user stage, table stage, or named stage) using the following methods:
Files that were loaded successfully can be deleted from the stage during a load by specifying the PURGE copy option in the COPY INTO <table> command.
After the load completes, use the REMOVE command to remove the files in the stage.
Removing files ensures they aren't inadvertently loaded again. It also improves load performance, because it reduces the number of files that COPY commands must scan to verify whether existing files in a stage were loaded already.

NEW QUESTION # 137
Snowpipe loads data from files as soon as they are available in a stage. Automated data loads lever-age event notifications for cloud storage to inform Snowpipe of the arrival of new data files to load. Which Cloud hosted platform provides cross cloud support for automated data loading via Snow-pipe?

A. AWS
B. AZURE
C. GCP
D. None of the Above currently provide cross cloud support for Snowpipe.

Answer: A

Explanation:
Explanation
Cross-cloud support only available to accounts hosted on Amazon Web Services currently.

NEW QUESTION # 138
Database XYZ has the data_retention_time_in_days parameter set to 7 days and table xyz.public.ABC has the data_retention_time_in_daysset to 10 days.
A Developer accidentally dropped the database containing this single table 8 days ago and just discovered the mistake.
How can the table be recovered?

A. create -able abc_restore as select * from xyz.public.abc at {offset => -60*60*24*8};
B. create table abc_restore clone xyz.public.abc at (offset => -300G*24*3);
C. Create a Snowflake Support case lo restore the database and tab e from "a i-safe
D. undrop database xyz;

Answer: D

Explanation:
Explanation
The table can be recovered by using the undrop database xyz; command. This command will restore the database that was dropped within the last 14 days, along with all its schemas and tables, including the customer table. The data_retention_time_in_days parameter does not affect this command, as it only applies to time travel queries that reference historical data versions of tables or databases. The other options are not valid ways to recover the table. Option B is incorrect because creating a table as select * from xyz.public.ABC at
{offset => -6060248} will not work, as this query will try to access a historical data version of the ABC table that does not exist anymore after dropping the database. Option C is incorrect because creating a table clone xyz.public.ABC at {offset => -360024*3} will not work, as this query will try to clone a historical data version of the ABC table that does not exist anymore after dropping the database. Option D is incorrect because creating a Snowflake Support case to restore the database and table from fail-safe will not work, as fail-safe is only available for disaster recovery scenarios and cannot be accessed by customers.

NEW QUESTION # 139
What are characteristics of Snowpark Python packages? (Select THREE).
Third-party packages can be registered as a dependency to the Snowpark session using the session, import () method.

A. Python packages can only be loaded in a local environment
B. Third-party supported Python packages are locked down to prevent hitting
C. The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
D. Python packages can access any external endpoints
E. Querying information__schema .packages will provide a list of supported Python packages and versions

Answer: C,D,E

Explanation:
Explanation
The characteristics of Snowpark Python packages are:
Third-party packages can be registered as a dependency to the Snowpark session using the session.import() method.
The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
Querying information_schema.packages will provide a list of supported Python packages and versions.
These characteristics indicate how Snowpark Python packages can be imported, inspected, and verified in Snowflake. The other options are not characteristics of Snowpark Python packages. Option B is incorrect because Python packages can be loaded in both local and remote environments using Snowpark. Option C is incorrect because third-party supported Python packages are not locked down to prevent hitting external endpoints, but rather restricted by network policies and security settings.

NEW QUESTION # 140
Jeff, a Data Engineer, accessing elements in JSON object in its 3 data loading scripts, he unknow-ingly use the upper case while accessing the elements. e.g.
Script 1 --> fruits:apple.sweet
Script 2 --> FRUITS:apple.sweet
Script 3 --> FRUITS:Apple.Sweet
Which are the correct statements?

A. Script 2&3 traverse path will be same.
B. Script 1 & Script 2 traverse path twill be treated same, but Script 2 will not.
C. Script 1 & 3 traverse path will be treated in same way.
D. Script 1,2,3 traverse path will treat in same way.

Answer: B

Explanation:
Explanation
There are two ways to access elements in a JSON object:
Dot Notation
Bracket Notation
Regardless of which notation you use, the column name is case-insensitive but element names are case-sensitive.

NEW QUESTION # 141
......

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