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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 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 (Q311-Q316):

NEW QUESTION # 311
Mark the Correct Statements for the VALIDATION_MODE option used by Data Engineer for Da-ta loading operations in his/her COPY INTO <table> command:

A. VALIDATION_MODE instructs the COPY command to validate the data files instead of loading them into the specified table; i.e., the COPY command tests the files for er-rors but does not load them.
B. VALIDATION_MODE only support Data loading operation i.e., do not work while da-ta unloading.
C. VALIDATION_MODE option supported these values:
RETURN_n_ROWS,
RETURN_ERRORS,
RETURN_ALL_ERRORS
D. VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error.

Answer: A,C,D

Explanation:
Explanation
All the Statements are correct except the statement saying VALIDATION_MODE only support Data loading operation.
VALIDATION_MODE can be used with COPY INTO <location> command as well i.e for data unloading operation.
VALIDATION_MODE = RETURN_ROWS can be used at the time of Data unloading.
This option instructs the COPY command to return the results of the query in the SQL statement instead of unloading the results to the specified cloud storage location. The only supported valida-tion option is RETURN_ROWS. This option returns all rows produced by the query.
When you have validated the query, you can remove the VALIDATION_MODE to perform the unload operation.

NEW QUESTION # 312
A Data Engineer is building a pipeline to transform a 1 TD tab e by joining it with supplemental tables The Engineer is applying filters and several aggregations leveraging Common TableExpressions (CTEs) using a size Medium virtual warehouse in a single query in Snowflake.
After checking the Query Profile, what is the recommended approach to MAXIMIZE performance of this query if the Profile shows data spillage?

A. Enable clustering on the table
B. Rewrite the query to remove the CTEs.
C. Increase the warehouse size
D. Switch to a multi-cluster virtual warehouse

Answer: C

Explanation:
Explanation
The recommended approach to maximize performance of this query if the Profile shows data spillage is to increase the warehouse size. Data spillage occurs when the query requires more memory than the warehouse can provide and has to spill some intermediate results to disk. This can degrade the query performance by increasing the disk IO time. Increasing the warehouse size can increase the amount of memory available for the query and reduce or eliminate data spillage.

NEW QUESTION # 313
A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team. The compliance team must have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

A. Create an Amazon Redshift data share that includes the tables that need to be shared.
B. Create views of the tables that need to be shared. Include only the required columns.
C. Create an Amazon Redshift managed VPC endpoint in the marketing team's account. Grant the marketing team access to the views.
D. Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.
E. Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.

Answer: A,D

NEW QUESTION # 314
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time. Which solution will meet these requirements?

A. Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.
B. Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
C. Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.
D. Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.

Answer: B

Explanation:
Amazon Athena Federated Query can run SQL across data in Amazon S3 and external sources by using data source connectors, and AWS provides connectors for Amazon DynamoDB, Amazon RDS, and Amazon Redshift. That lets the company perform a one-time cross-source join and analysis without first centralizing or copying the data, which minimizes unnecessary data movement and setup effort.
Reference:
https://docs.aws.amazon.com/athena/latest/ug/federated-queries.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-redshift.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-dynamodb.html

NEW QUESTION # 315
Alex, a Data Engineer with one of the Data analytics Organization, created the Materialized view over External tables to improve Data Reporting Experience.
Step 1: He created materialized view named DataReportMV
1.create or replace materialized view DataReportMV as
2.select Item_id, Item_price from Items;
Step 2: He joined a materialized view with a sales table as
1.create or replace view Revenue as
2.select m.item_id, sum(ifnull(s.quantity, 0)) as quantity,
3.sum(ifnull(quantity * (s.price - m.item_price), 0)) as profit
4.from DataReportMV as m left outer join sales as s on s.item_id = m.item_id
5.group by m.item_id;
Step 3: After 1 hour, he decided to temporarily suspend the use (and maintenance) of the DataRe-portMV materialized view for cost saving purpose.
alter materialized view DataReportMV suspend;
Please select what Alex is doing wrong here?

A. There is no command like suspend for temporarily suspension of Materialized views, Step 3 will give error like invalid Suspend command.
B. A materialized view, DataReportMV does not support Join operations, so Step 2 would be failed & he cannot proceed further.
C. Once DataReportMV got suspended , any query on the top of the view will generate er-ror like:
Failure during expansion of view 'DATAREPORTMV': SQL compilation error: Material-ized view DataReportMV is invalid.
D. Materialized view on top of External tables is not supported feature.
E. Alex is doing everything correct.

Answer: E

Explanation:
Explanation
All Steps will be executed successfully by Alex without any error.

NEW QUESTION # 316
......

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