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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
  • 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
  • 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 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q255-Q260):

NEW QUESTION # 255
Snowflake supports using key pair authentication for enhanced authentication security as an alterna-tive to basic authentication (i.e. username and password). Select the list of SnowFlake Clients sup-port the same?
[Select All that Apply]

Answer: A,B,C,D


NEW QUESTION # 256
A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data.
The company uses an Amazon Redshift cluster for a data warehouse.
An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster.
A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.
Which combination of steps will meet these requirements? (Choose two.)

Answer: D,E

Explanation:
Choice A ensures that live transactional data from PostgreSQL can be accessed directly within Redshift queries.
Choice C archives historical data in Amazon S3, reducing storage costs in Redshift while still making the data accessible via Redshift Spectrum.


NEW QUESTION # 257
The following code is executed ina Snowflake environment with the default settings:

What will be the result of the select statement?

Answer: A


NEW QUESTION # 258
A company has several new datasets in CSV and JSON formats. A data engineer needs to make the data available to a team of data analysts who will analyze the data by using SQL queries.
Which solution will meet these requirements in the MOST cost-effective way?

Answer: D

Explanation:
By storing the CSV and JSON files in Amazon S3 and running an AWS Glue crawler, you automatically catalog them as tables without upfront ETL.
Amazon Athena then lets analysts run SQL queries directly against those tables on a pay-per- query basis. Using an Athena workgroup with a usage threshold keeps costs under control, making this the most cost-effective, low-operational-overhead solution.


NEW QUESTION # 259
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.
The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.
Which solution will meet this requirement?

Answer: C

Explanation:
The ANALYZE COMPRESSION command in Amazon Redshift evaluates the existing data in a table and suggests the most optimal compression encoding for each column. After running this command, the data engineer can manually update the table to apply the recommended compression encodings. This approach ensures the best fit for data compression, improving storage efficiency and query performance.
The ANALYZE command collects statistics for query optimization, but it does not provide compression encoding recommendations. It is focused on query performance, not data compression.
The VACUUM REINDEX command does not exist in Amazon Redshift. VACUUM commands are generally used for reclaiming disk space and sorting tables, not for compression optimization.
VACUUM RECLUSTER does not exist in Amazon Redshift. VACUUM operations focus on reorganizing and sorting data but do not address compression encoding.


NEW QUESTION # 260
......

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