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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 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 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	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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
Data Engineer is using existing pipe that automates data loads using event notifications, later he figured out the needs to modify pipe properties. For the same, He decided to recreate the pipe as best practice. He followed the below steps for the same.
1. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
2. Recreate the pipe (using CREATE OR REPLACE PIPE).
3. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
Which are the Missing recommended steps while Recreating Pipes for Automated Data Loads?

A. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true) Pre & Post recreation & Resume after recreation (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = false).
B. Terminate the existing pipe (using ALTER PIPE ... SET PIPE_EXECUTION_TERMINATE = true) before recreation.
C. Force the pipe to resume (using SYSTEM$PIPE_FORCE_RESUME).
D. CREATE OR REPLACE PIPE command will recreate the PIPE successfully.

Answer: A

Explanation:
Explanation
Recreating a pipe (using a CREATE OR REPLACE PIPE statement) is necessary to modify most pipe properties.
Recreating Pipes for Automated Data Loads
When recreating a pipe that automates data loads using event notifications, it's recommended that Data Engineer complete the following steps:
1. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true).
2. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is PAUSED.
3. Recreate the pipe (using CREATE OR REPLACE PIPE).
4. Pause the pipe again.
5. Review the configuration steps for your cloud messaging service to ensure the settings are still accurate.
6. Query the SYSTEM$PIPE_STATUS function again and verify that the pipe execution state is RUNNING.

NEW QUESTION # 66
1.+--------------------------------------------------------------+
2.| SYSTEM$CLUSTERING_INFORMATION('SF_DATA', '(COL1, COL3)') |
3.|--------------------------------------------------------------|
4.| { |
5.| "cluster_by_keys" : "(COL1, COL3)", |
6.| "total_partition_count" : 1156, |
7.| "total_constant_partition_count" : 0, |
8.| "average_overlaps" : 117.5484, |
9.| "average_depth" : 64.0701, |
10.| "partition_depth_histogram" : { |
11.| "00000" : 0, |
12.| "00001" : 0, |
13.| "00002" : 3, |
14.| "00003" : 3, |
15.| "00004" : 4, |
16.| "00005" : 6, |
17.| "00006" : 3, |
18.| "00007" : 5, |
19.| "00008" : 10, |
20.| "00009" : 5, |
21.| "00010" : 7, |
22.| "00011" : 6, |
23.| "00012" : 8, |
24.| "00013" : 8, |
25.| "00014" : 9, |
26.| "00015" : 8, |
27.| "00016" : 6, |
28.| "00032" : 98, |
29.| "00064" : 269, |
30.| "00128" : 698 |
31.| } |
32.| } |
33.+--------------------------------------------------------------+
The Above example indicates that the SF_DATA table is not well-clustered for which of following valid reasons?

A. ALL of the above
B. Most of the micro-partitions are grouped at the lower-end of the histogram, with the majority of micro-partitions having an overlap depth between 64 and 128.
C. Zero (0) constant micro-partitions out of 1156 total micro-partitions.
D. High average of overlapping micro-partitions.
E. High average of overlap depth across micro-partitions.

Answer: A

NEW QUESTION # 67
David, a Lead Data engineer with XYZ company looking out to improve query performance & oth-er benefits while working with Tables, Regular Views, MVs and Cached Results.
Which one of the following does not shows key similarities and differences between tables, regular views, cached query results, and materialized views while choosing any of them by David?

A. Both materialized views and regular views enhance data security by allowing data to be exposed or hidden at the row level or column level.
B. As with non-materialized views, a materialized view automatically inherits the privileges of its base table.
C. Cached Query Results: Used only if data has not changed and if query only uses de-terministic functions (e.g. not CURRENT_DATE).
D. Regular views do not cache data, and therefore cannot improve performance by cach-ing.
E. Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.

Answer: B

Explanation:
Explanation
Materialized Views, like other database objects (tables, views, UDFs, etc.), are owned by a role and have privileges that can be granted to other roles.
You can grant the following privileges on a materialized view:
SELECT
As with non-materialized views, a materialized view does not automatically inherit the privileges of its base table. You should explicitly grant privileges on the materialized view to the roles that should use that view.
As with non-materialized views, a user who wishes to access a materialized view needs privileges only on the view, not on the underlying object(s) that the view references.
Rest is correct.

NEW QUESTION # 68
A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.
A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.
Which solution will meet this requirement with the LEAST operational effort?

A. Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.
B. Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.
C. Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.
D. Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.

Answer: C

Explanation:
AWS Glue DataBrew: AWS Glue DataBrew is a visual data preparation tool that allows data engineers and data analysts to clean and normalize data without writing code. Using DataBrew, a data engineer could create a recipe that includes the concatenation of the customer first and last names and then use the COUNT_DISTINCT function. This would not require complex code and could be performed through the DataBrew user interface, representing a lower operational effort.

NEW QUESTION # 69
A company needs to implement a workflow to process transactions. Each transaction goes through multiple levels of validation. Each validation level depends on the preceding validation level.
The workflow must either process or reject each transaction within 24-hours. The workflow must run for less than 24 hours total.
Which solution will meet these requirements with the LEAST operational cost?

A. Use AWS Lambda functions to implement the workflow. Use Amazon EventBridge to invoke the validation steps.
B. Create an express workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
C. Create a standard workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to implement the workflow.

Answer: C

Explanation:
Standard Step Functions support long-running, sequential workflows and the Wait for Callback pattern without charging for idle wait time, minimizing cost while ensuring completion within 24 hours. Express would bill for long waits, Lambda+EventBridge adds complexity and limits, and MWAA has higher operational overhead and cost.

NEW QUESTION # 70
......

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