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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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	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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q298-Q303):

NEW QUESTION # 298
A data engineer finished testing an Amazon Redshift stored procedure that processes and inserts data into a table that is not mission critical. The engineer wants to automatically run the stored procedure on a daily basis.
Which solution will meet this requirement in the MOST cost-effective way?

A. Use query editor v2 to run the stored procedure on a schedule.
B. Schedule and run the stored procedure by using the Amazon Redshift Data API in an Amazon EC2 Spot Instance.
C. Create an AWS Lambda function to schedule a cron job to run the stored procedure.
D. Schedule an AWS Glue Python shell job to run the stored procedure.

Answer: A

Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/query-editor-v2-schedule-query.html

NEW QUESTION # 299
Which of the following System keeps the following characteristics?
a. It will keep in it all the raw data.
b. Generally, the users of it is data scientists and data developers.
c. Flat architecture
d. Highly agile

A. Data Hub
B. Data Warehouse
C. Data Mart
D. Data Lake

Answer: D

NEW QUESTION # 300
If external software i.e. TIBCO, exports Data fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, How Snowflake handle it? [Select 2]

A. COPY command trims the leading space and removes the quotation marks enclosing each field
1.copy into SFtable
2.from @%SFtable
3.file_format = (type = csv trim_space=true field_optionally_enclosed_by = '0x22');
B. field_optionally_enclosed_by option along with TRIM_IF function in COPY INTO statement can be used to handle this scenario successfully.
C. Snowflake automatically handles leading spaces by trimming implicitly & removes the quotation marks enclosing each field.
D. Snowflake reads the leading space rather than the opening quotation character as the beginning of the field and the quotation characters are interpreted as string data.
(Correct)

Answer: A

Explanation:
Explanation
If your external software exports fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, Snowflake reads the leading space rather than the open-ing quotation character as the beginning of the field. The quotation characters are interpreted as string data.
Use the TRIM_SPACE file format option to remove undesirable spaces during the data load.

NEW QUESTION # 301
Data Engineer wants to analyze query performance & looking out for profiling information, He went to Query/Operator Details also called Profile Overview of Query Profile Interface & searching for statistics attributes around I/O. Which of the following information he can't get from there?

A. Bytes read from result - bytes read from the result object.
B. Bytes written - bytes written (e.g. when loading into a table).
C. Bytes sent over the wireframe - amount of data sent over the wireframe
D. External bytes scanned - bytes read from an external object, e.g. a stage.
E. Percentage scanned from cache - the percentage of data scanned from the local disk cache.

Answer: C

Explanation:
Explanation
To help you analyze query performance, Query/Operator Details panel also called Profile overview panel provides two classes of profiling information:
Execution time, broken down into categories
Detailed statistics
Apart from Option a Bytes sent over the wireframe - amount of data sent over the wireframe , Rest of the Statistics Information provided by Query/Operator details in the Query Profile Inter-face.
To Know More about the Query/Operator Details options , please refer the link:
https://docs.snowflake.com/en/user-guide/ui-query-profile#query-operator-details

NEW QUESTION # 302
A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?

A. Use an AWS Glue workflow to refresh the materialized views.
B. Use Apache Airflow to refresh the materialized views.
C. Use the query editor v2 in Amazon Redshift to refresh the materialized views.
D. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.

Answer: D

Explanation:
AWS Lambda allows running code in response to triggers without needing to provision or manage servers. However, creating a UDF within Amazon Redshift to call a Lambda function for this purpose involves writing custom code and managing permissions between Lambda and Redshift.

NEW QUESTION # 303
......

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