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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 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.
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 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
  • 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 5
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q382-Q387):

NEW QUESTION # 382
Mark the correct Statements with respect to Secure views & its creation in the SnowFlake Account?

Answer: A,B,C,D

Explanation:
Explanation
Why Should I Use Secure Views?
For a non-secure view, internal optimizations can indirectly expose data.
Some of the internal optimizations for views require access to the underlying data in the base tables for the view. This access might allow data that is hidden from users of the view to be exposed through user code, such as user-defined functions, or other programmatic methods. Secure views do not utilize these optimizations, ensuring that users have no access to the underlying data.
For a non-secure view, the view definition is visible to other users.
By default, the query expression used to create a standard view, also known as the view definition or text, is visible to users in various commands and interfaces.
For security or privacy reasons, you might not wish to expose the underlying tables or internal struc-tural details for a view. With secure views, the view definition and details are visible only to author-ized users (i.e.
users who are granted the role that owns the view).
When Should I Use a Secure View?
Views should be defined as secure when they are specifically designated for data privacy (i.e. to limit access to sensitive data that should not be exposed to all users of the underlying table(s)).
Secure views should not be used for views that are defined solely for query convenience, such as views created to simplify queries for which users do not need to understand the underlying data representation. Secure views can execute more slowly than non-secure views.
Secure views are defined using the SECURE keyword with the standard DDL for views:
To create a secure view, specify the SECURE keyword in the CREATE VIEW or CREATE MA-TERIALIZED VIEW command.
To convert an existing view to a secure view and back to a regular view, set/unset the SECURE keyword in the ALTER VIEW or ALTER MATERIALIZED VIEW command.
The definition of a secure view is only exposed to authorized users (i.e. users who have been grant-ed the role that owns the view). If an unauthorized user uses any of the following commands or in-terfaces, the view definition is not displayed:
SHOW VIEWS and SHOW MATERIALIZED VIEWS commands.
GET_DDL utility function.
VIEWS Information Schema view.
VIEWS Account Usage view.
For non-materialized views, the IS_SECURE column in the Information Schema and Account Us-age views identifies whether a view is secure.
The internals of a secure view are not exposed in Query Profile (in the web interface). This is the case even for the owner of the secure view, because non-owners might have access to an owner's Query Profile.


NEW QUESTION # 383
A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time.
Which solution will run the Glue jobs in the MOST cost-effective way?

Answer: D

Explanation:
Flex allows you to optimize your costs on your non-urgent or non-time sensitive data integration workloads such as testing, and one-time data loads. With Flex, AWS Glue jobs run on spare compute capacity instead of dedicated hardware. The start and runtimes of jobs using Flex can vary because spare compute resources aren't readily available and can be reclaimed during the run of a job.
https://aws.amazon.com/blogs/big-data/introducing-aws-glue-flex-jobs-cost-savings-on-etl- workloads/


NEW QUESTION # 384
Given the table sales which has a clustering key of column CLOSED_DATE which table function will return the average clustering depth for the SALES_REPRESENTATIVEcolumn for the North American region?

Answer: B

Explanation:
Explanation
The table function SYSTEM$CLUSTERING_DEPTH returns the average clustering depth for a specified column or set of columns in a table. The function takes two arguments: the table name and the column name(s). In this case, the table name is sales and the column name is SALES_REPRESENTATIVE. The function also supports a WHERE clause to filter the rows for which the clustering depth is calculated. In this case, the WHERE clause is REGION = 'North America'. Therefore, the function call in Option B will return the desired result.


NEW QUESTION # 385
How Data Engineer can do Monitoring of Files which are Staged Internally during Continuous data pipelines loading process? [Select all that apply]

Answer: B,C,D

Explanation:
Explanation
Monitoring Files Staged Internally
Snowflake maintains detailed metadata for each file uploaded into internal stage (for users, tables, and stages), including:
File name
File size (compressed, if compression was specified during upload)
LAST_MODIFIED date, i.e. the timestamp when the data file was initially staged or when it was last modified, whichever is later In addition, Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days. The metadata can be used to monitor and manage the loading process, including de-leting files after upload completes:
Use the LIST command to view the status of data files that have been staged.
Monitor the status of each COPY INTO <table> command on the History tab page of the classic web interface.
Use the VALIDATE function to validate the data files you've loaded and retrieve any errors en-countered during the load.
Use the LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.


NEW QUESTION # 386
A company is building a new application that ingests CSV files into Amazon Redshift. The company has developed the frontend for the application.
The files are stored in an Amazon S3 bucket. Files are no larger than 5 MB.
A data engineer is developing the extract, transform, and load (ETL) pipeline for the CSV files.
The data engineer configured a Redshift cluster and an AWS Lambda function that copies the data out of the files into the Redshift cluster.
Which additional steps should the data engineer perform to meet these requirements?

Answer: C

Explanation:
By sending S3 "Object Created" events to EventBridge and matching those events with a rule that invokes your Lambda function, you trigger your ETL whenever a new CSV lands in S3, without extra polling or queue management. This direct, event-driven pattern keeps operational overhead to a minimum.


NEW QUESTION # 387
......

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