Snowflake DEA-C01 Latest Guide Files, Valid DEA-C01 Exam Topics

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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 50 questions
Exam Format:	Multiple Choice, Multiple Select
Passing Score:	~70% (exact passing score not publicly disclosed by Snowflake)
Exam Price:	$375 USD
Related Certifications:	SnowPro Core Certification (Recommended Prerequisite)
Available Languages:	English
Exam Duration:	90 minutes
Certificate Validity Period:	2 years (recertification required)
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/

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Valid Snowflake DEA-C01 Exam Topics | Latest DEA-C01 Questions

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.
Topic 4	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 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 (Q319-Q324):

NEW QUESTION # 319
The following is returned fromSYSTEMCLUSTERING_INFORMATION () for a tablenamed orders with adate column named O_ORDERDATE:

What does the total_constant_partition_count value indicate about this table?

A. The table is clustered very well on_ORDERDATE, as there are 493 micro-partitions that could not be significantly improved by reclustering
B. The table is not clustered well on O_ORDERDATE, as there are 493 micro-partitions where the range of values in that column overlap with every other micro partition in the table.
C. The data inO_ORDERDATEhas a very low cardinality as there are 493 micro-partitions where there is only a single distinct value in that column for all rows in the micro-partition
D. The data inO_ORDERDATEdoes not change very often as there are 493 micro-partitionscontaining rows where that column has not been modified since the row was created

Answer: B

Explanation:
Explanation
The total_constant_partition_count value indicates the number of micro-partitions where the clustering key column has a constant value across all rows in the micro-partition. However, this does not necessarily mean that the table is clustered well on that column, as there could be other micro-partitions where the range of values in that column overlap with each other. This is the case for the orders table, as the clustering depth is 1, which means that every micro-partition overlaps with every other micro-partition on O_ORDERDATE. This indicates that the table is not clustered well on O_ORDERDATE and could benefit from reclustering.

NEW QUESTION # 320
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 # 321
A retail company uses AWS Glue for extract, transform, and load (ETL) operations on a dataset that contains information about customer orders. The company wants to implement specific validation rules to ensure data accuracy and consistency.
Which solution will meet these requirements?

A. Use AWS Glue Data Catalog to maintain a centralized data schema and metadata repository.
B. Create custom AWS Glue Data Quality rulesets to define specific data quality checks.
C. Use AWS Glue job bookmarks to track the data for accuracy and consistency.
D. Use the built-in AWS Glue Data Quality transforms for standard data quality validations.

Answer: B

Explanation:
Custom AWS Glue Data Quality rulesets allow you to define precise data quality checks tailored to your specific needs, ensuring that the data meets the required standards of accuracy and consistency. This approach provides flexibility to implement a wide range of validation rules based on your business requirements.

NEW QUESTION # 322
A ride-sharing company stores records for all rides in an Amazon DynamoDB table. The table includes the following columns and types of values:

The table currently contains billions of items. The table is partitioned by RideID and uses TripStartTime as the sort key. The company wants to use the data to build a personal interface to give drivers the ability to view the rides that each driver has completed, based on RideStatus. The solution must access the necessary data without scanning the entire table.
Which solution will meet these requirements?

A. Create a local secondary index (LSI) on DriverID.
B. Create a global secondary index (GSI) that uses RiderID as the partition key and RideStatus as the sort key.
C. Create a global secondary index (GSI) that uses DriverID as the partition key and RideStatus as the sort key.
D. Create a filter expression that uses RiderID and RideStatus.

Answer: C

Explanation:
To let drivers efficiently query only their completed rides, you need a global secondary index (GSI) with DriverID as the partition key (so queries can be targeted per driver) and RideStatus as the sort key (so you can query for "Completed" rides without scanning the full table). This avoids costly scans and supports fast, targeted lookups at scale.

NEW QUESTION # 323
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 1,2,3 traverse path will treat in same way.
B. Script 1 & 3 traverse path will be treated in same way.
C. Script 2&3 traverse path will be same.
D. Script 1 & Script 2 traverse path twill be treated same, but Script 2 will not.

Answer: D

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 # 324
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