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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	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	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 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 (Q35-Q40):

NEW QUESTION # 35
A table is loaded using Snowpipe and truncated afterwards Later, a Data Engineer finds that the table needs to be reloaded but the metadata of the pipe will not allow the same files to be loaded again.
How can this issue be solved using the LEAST amount of operational overhead?

A. Recreate the pipe by using the create or replace pipe command
B. Wait until the metadata expires and then reload the file using Snowpipe
C. Modify the file by adding a blank row to the bottom and re-stage the file
D. Set the FORCE=TRUE option in the Snowpipe COPY INTO command

Answer: D

Explanation:
Explanation
The FORCE=TRUE option in the Snowpipe COPY INTO command allows Snowpipe to load files that have already been loaded before, regardless of the metadata. This is the easiest way to reload the same files without modifying them or recreating the pipe.

NEW QUESTION # 36
Marko, a Data Engineer is using Snowpipe for data loading in micro batches for one of the Finance Data workloads. There are set of files he attempted to load into the snowflake table using Snow-pipe. While monitoring he found that there are set of files has multiple issue, He queried the COPY_HISTORY view & checked the STATUS column which indicates whether a particular set of files was loaded, partially loaded, or failed to load. But he wants to view all errors in the files along with Load status, how he can check all errors?

A. Marko can look out for FIRST_ERROR_MESSAGE column in the COPY_HISTORY view which can provides a reason why a file partially loaded or failed for all the files.
B. He can view all errors in the files, by executing a COPY INTO <table> statement with the VALIDATION_MODE copy option set to RETURN_ALL_ERRORS.
C. He can view all errors in the files, by executing a COPY INTO <table> statement with the VALIDATION_ERROR_MODE copy option set to RE-TURN_ALL_PIPE_ERRORS.
D. He can check RETURN_ALL_ERROR_MESSAGE column in the COPY_HISTORY view which can provides a reason and view all errors in the files.

Answer: B

Explanation:
Explanation
The STATUS column indicates whether a particular set of files was loaded, partially loaded, or failed to load.
The FIRST_ERROR_MESSAGE column provides a reason when an attempt partial-ly loaded or failed.
Note that if a set of files has multiple issues, the FIRST_ERROR_MESSAGE column only indi-cates the first error encountered. To view all errors in the files, execute a COPY INTO <table> statement with the VALIDATION_MODE copy option set to RETURN_ALL_ERRORS. The VALIDATION_MODE copy option instructs a COPY statement to validate the data to be loaded and return results based on the validation option specified. No data is loaded when this copy option is specified.

NEW QUESTION # 37
A marketing company collects clickstream data. The company sends the clickstream data to Amazon Kinesis Data Firehose and stores the clickstream data in Amazon S3. The company wants to build a series of dashboards that hundreds of users from multiple departments will use.
The company will use Amazon QuickSight to develop the dashboards. The company wants a solution that can scale and provide daily updates about clickstream activity.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

A. Access the query data through a QuickSight direct SQL query.
B. Use Amazon Redshift to store and query the clickstream data.
C. Use Amazon S3 analytics to query the clickstream data.
D. Use Amazon Athena to query the clickstream data
E. Access the query data through QuickSight SPICE (Super-fast, Parallel, In-memory Calculation Engine). Configure a daily refresh for the dataset.

Answer: D,E

Explanation:
Athena would be cheaper than Redshift. S3 analytics is irrelevant. The functionality in SPICE should be more cost effective than direct SQL by reducing the frequency and volume of queries.

NEW QUESTION # 38
A data engineer creates an AWS Glue Data Catalog table by using an AWS Glue crawler that is named Orders. The data engineer wants to add the following new partitions:
s3://transactions/orders/order_date=2023-01-01
s3://transactions/orders/order_date=2023-01-02
The data engineer must edit the metadata to include the new partitions in the table without scanning all the folders and files in the location of the table.
Which data definition language (DDL) statement should the data engineer use in Amazon Athena?

A. MSCK REPAIR TABLE Orders;
B. ALTER TABLE Orders ADD PARTITION(order_date='2023-01-01') LOCATION
's3://transactions/orders/order_date=2023-01-01';
ALTER TABLE Orders ADD PARTITION(order_date='2023-01-02') LOCATION
's3://transactions/orders/order_date=2023-01-02';
C. REPAIR TABLE Orders;
D. ALTER TABLE Orders MODIFY PARTITION(order_date='2023-01-01') LOCATION
's3://transactions/orders/2023-01-01';
ALTER TABLE Orders MODIFY PARTITION(order_date='2023-01-02') LOCATION
's3://transactions/orders/2023-01-02';

Answer: B

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/alter-table-add-partition.html

NEW QUESTION # 39
Which two Account usage views can be used for auditing Dynamic data masking purpose?

A. MASKING POLICIES
B. DYNAMIC POLICY_REFERENCES
C. POLICY_REFERENCES
D. DYNAMIC MASKING POLICIES

Answer: A,C

NEW QUESTION # 40
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