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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Exam Price:	$375 USD
Related Certifications:	SnowPro Advanced: Data Scientist SnowPro Advanced: Data Analyst SnowPro Core SnowPro Advanced: Administrator SnowPro Advanced: Security Engineer SnowPro Advanced: Architect
Real Exam Qty:	65
Available Languages:	English, Japanese
Passing Score:	750 / 1000
Exam Format:	Multiple choice, Multiple select
Exam Duration:	115 minutes
Certificate Validity Period:	2 years
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	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	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 3	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 4	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 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 (Q127-Q132):

NEW QUESTION # 127
A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day.
Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM.
A business intelligence team queries the tables between 10 AM and 8 PM every day.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Use AWS Glue to load a full copy of the data warehouse tables into Amazon S3 every day.Overwrite the previous day's full-load copy every day.
B. Use an AWS Glue Java Database Connectivity (JDBC) connection. Configure a job bookmark for a column that contains monotonically increasing values. Write custom logic to append the daily incremental data to a full-load copy that is in Amazon S3.
C. Use an AWS Database Migration Service (AWS DMS) full load migration to load the data warehouse tables into Amazon S3 every day. Overwrite the previous day's full-load copy every day.
D. Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.

Answer: D

NEW QUESTION # 128
A retail company stores data from a product lifecycle management (PLM) application in an on- premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on- premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?

A. Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.
B. Use the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.
C. Run a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.
D. Run scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.

Answer: A

Explanation:
https://aws.amazon.com/ko/blogs/apn/change-data-capture-from-on-premises-sql-server-to- amazon-redshift-target/

NEW QUESTION # 129
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?

A. Use a create ok replace table as statement that references the staging table and includes all the transformation SQL. Use a task to run the full create or replace table as statement on a scheduled basis
B. Schedule a task that dynamically retrieves the last time the task was run from information_schema-rask_hiSwOry and use that timestamp to process the delta of the new rows since the last time the task was run.
C. Transform the data during the data load with Snowpipe by modifying the related copy into statement to include transformation steps such as case statements andJOIN'S.
D. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.

Answer: D

Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.

NEW QUESTION # 130
SYSTEM$CLUSTERING_INFORMATION functions returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function returns a JSON object containing average_overlaps name/value pairs. Does High average_overlaps indicates well organized Clustering?

A. NO
B. YES

Answer: A

Explanation:
Explanation
Higher the avg_overlap indicates poorly organized clustering.

NEW QUESTION # 131
What is the purpose of the BUILD_FILE_URL function in Snowflake?

A. It generates a permanent URL for accessing files in a stage.
B. It generates an encrypted URL foe accessing a file in a stage.
C. It generates a temporary URL for accessing a file in a stage.
D. It generates a staged URL for accessing a file in a stage.

Answer: D

Explanation:
Explanation
The BUILD_FILE_URL function in Snowflake generates a temporary URL for accessing a file in a stage. The function takes two arguments: the stage name and the file path. The generated URL is valid for 24 hours and can be used to download or view the file contents. The other options are incorrect because they do not describe the purpose of the BUILD_FILE_URL function.

NEW QUESTION # 132
......

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