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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 Duration:	115 minutes
Available Languages:	English, Japanese
Related Certifications:	SnowPro Advanced: Administrator SnowPro Advanced: Security Engineer SnowPro Advanced: Architect SnowPro Advanced: Data Analyst SnowPro Advanced: Data Scientist SnowPro Core
Certificate Validity Period:	2 years
Exam Format:	Multiple choice, Multiple select
Passing Score:	750 / 1000
Real Exam Qty:	65
Exam Price:	$375 USD
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 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 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	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	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	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q313-Q318):

NEW QUESTION # 313
As a skilled data engineer, you have been tasked with managing the Spark ETL jobs that run on the EMR and store incremental data in S3. However, you have noticed that some jobs are failing, and you are eager to take proactive steps to troubleshoot the issue. What would be the primary course of action to resolve this problem and ensure the smooth operation of the ETL process?

A. Check the available disk space on the HDFS cluster.
B. Review the Spark job logs for errors or warnings.
C. Restart the EMR cluster.
D. Increase the number of EC2 instances in the cluster.

Answer: B

Explanation:
Reviewing Spark job logs is the first step to identify errors, warnings, or slow stages that indicate the root cause of the performance issue.

NEW QUESTION # 314
A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job.
The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?

A. The AWS Glue job does not have a required commit statement.
B. The AWS Glue job does not have the s3:GetObjectAcl permission that is required for bookmarks to work correctly.
C. The data engineer incorrectly specified an older version of AWS Glue for the Glue job.
D. The maximum concurrency for the AWS Glue job is set to 1.

Answer: A

Explanation:
https://docs.aws.amazon.com/glue/latest/dg/glue-troubleshooting-errors.html#error-job- bookmarks-reprocess-data

NEW QUESTION # 315
Mark the correct Statements with respect to Secure views & its creation in the SnowFlake Account?

A. The internals of a secure view are not exposed in Query Profile (in the web interface). This is the case even for the owner of the secure view, because non-owners might have access to an owner's Query Profile.
B. To convert an existing view to a secure view and back to a regular view, set/unset the SECURE keyword in the ALTER VIEW or ALTER MATERIALIZED VIEW com-mand.
C. For a secure view, internal optimizations can indirectly expose data & the view defini-tion is visible to other users.
D. For non-materialized views, the IS_SECURE column in the Information Schema and Account Usage views identifies whether a view is secure.
E. Secure views should not be used for views that are defined solely for query conven-ience, such as views created to simplify queries for which users do not need to under-stand the underlying data representation.

Answer: A,B,D,E

Explanation:
Explanation
Why Should I Use Secure Views?
For a non-secure view, internal optimizations can indirectly expose data.
Some of the internal optimizations for views require access to the underlying data in the base tables for the view. This access might allow data that is hidden from users of the view to be exposed through user code, such as user-defined functions, or other programmatic methods. Secure views do not utilize these optimizations, ensuring that users have no access to the underlying data.
For a non-secure view, the view definition is visible to other users.
By default, the query expression used to create a standard view, also known as the view definition or text, is visible to users in various commands and interfaces.
For security or privacy reasons, you might not wish to expose the underlying tables or internal struc-tural details for a view. With secure views, the view definition and details are visible only to author-ized users (i.e.
users who are granted the role that owns the view).
When Should I Use a Secure View?
Views should be defined as secure when they are specifically designated for data privacy (i.e. to limit access to sensitive data that should not be exposed to all users of the underlying table(s)).
Secure views should not be used for views that are defined solely for query convenience, such as views created to simplify queries for which users do not need to understand the underlying data representation. Secure views can execute more slowly than non-secure views.
Secure views are defined using the SECURE keyword with the standard DDL for views:
To create a secure view, specify the SECURE keyword in the CREATE VIEW or CREATE MA-TERIALIZED VIEW command.
To convert an existing view to a secure view and back to a regular view, set/unset the SECURE keyword in the ALTER VIEW or ALTER MATERIALIZED VIEW command.
The definition of a secure view is only exposed to authorized users (i.e. users who have been grant-ed the role that owns the view). If an unauthorized user uses any of the following commands or in-terfaces, the view definition is not displayed:
SHOW VIEWS and SHOW MATERIALIZED VIEWS commands.
GET_DDL utility function.
VIEWS Information Schema view.
VIEWS Account Usage view.
For non-materialized views, the IS_SECURE column in the Information Schema and Account Us-age views identifies whether a view is secure.
The internals of a secure view are not exposed in Query Profile (in the web interface). This is the case even for the owner of the secure view, because non-owners might have access to an owner's Query Profile.

NEW QUESTION # 316
A company needs a solution that restricts access to Amazon S3 data and encrypts the data by using AWS managed keys. The solution must manage database credentials that an AWS Lambda function uses and must rotate the credentials automatically. Which solution will meet these requirements?

A. Use S3 ACLs to control access. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the data. Store the credentials in AWS Systems Manager Parameter Store and automatically rotate the credentials by using a Lambda function.
B. Use IAM policies to control access. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the data. Store the credentials in AWS Systems Manager Parameter Store.Configure a scheduled Lambda function to rotate the credentials.
C. Use IAM policies to control access. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the data. Configure AWS Secrets Manager to store and automatically rotate the credentials by using a Lambda function.
D. Use S3 bucket policies to control access. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the data. Store the database credentials as Lambda environment variables.

Answer: C

Explanation:
IAM policies can be used to control access to Amazon S3 data, and server-side encryption with AWS KMS keys (SSE-KMS) supports encryption by using an AWS managed KMS key such as aws/s3. AWS Secrets Manager is the managed service designed to store database credentials and rotate them automatically, including rotation by using a Lambda function. This combination satisfies the requirements for S3 access control, encryption with AWS managed keys, and automatic credential rotation for the Lambda-based application.
Reference:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html
https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-policy-language-overview.html
https://docs.aws.amazon.com/secretsmanager/latest/userguide/intro.html

NEW QUESTION # 317
Select the Incorrect statement about External Functions in SnowFlake?

A. An external function does not contain its own code; instead, the external function calls code that is stored and executed outside Snowflake.
B. Inside Snowflake, the external function is stored as a database object that contains in-formation that Snowflake uses to call the remote service.
C. An external function is a type of UDF.
D. Inside Snowflake, the external function is stored as a API Integration object.

Answer: D

NEW QUESTION # 318
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