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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	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	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 (Q110-Q115):

NEW QUESTION # 110
Which Role inherits the privileges of the USERADMIN role via the system role hierarchy?

A. CUSTOM ROLE
B. SYSADMIN
C. SECURITYADMIN
D. PUBLIC

Answer: C

NEW QUESTION # 111
You have been tasked with migrating an on-premises MySQL database to Amazon Aurora PostgreSQL using AWS Database Migration Service (DMS). The stakeholder emphasizes that the source database must remain fully operational during the migration process.
Which of the following statements about DMS is accurate with respect to this scenario?

A. AWS DMS can convert the MySQL database schema directly to PostgreSQL without any manual intervention.
B. AWS DMS only supports full-load migrations, which would require downtime for the source database.
C. When using DMS, the target Amazon Aurora PostgreSQL instance cannot be accessed or queried until the migration is complete.
D. AWS DMS requires the source MySQL database to be version 5.7 or higher for migrating to Amazon Aurora PostgreSQL.
E. AWS DMS supports both full-load and continuous replication, allowing the source MySQL database to remain operational during migration.

Answer: E

NEW QUESTION # 112
A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

A. Transform the data that is in the S3 bucket to Apache Parquet format.
B. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.
C. Use Athena partition projection based on the S3 bucket prefix.
D. Create an AWS Glue partition index. Enable partition filtering.
E. Bucket the data based on a column that the data have in common in a WHERE clause of the user query.

Answer: C,D

Explanation:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/ Optimizing Partition Processing using partition projection Processing partition information can be a bottleneck for Athena queries when you have a very large number of partitions and aren't using AWS Glue partition indexing. You can use partition projection in Athena to speed up query processing of highly partitioned tables and automate partition management. Partition projection helps minimize this overhead by allowing you to query partitions by calculating partition information rather than retrieving it from a metastore. It eliminates the need to add partitions' metadata to the AWS Glue table.

NEW QUESTION # 113
A Data Engineer is working on a Snowflake deployment in AWS eu-west-1 (Ireland). The Engineer is planning to load data from staged files into target tables using the copy into command Which sources are valid? (Select THREE)

A. External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland)
B. External stage on GCP us-central1 (Iowa)
C. SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland)
D. External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt)
E. Internal stage on GCP us-central1 (Iowa)
F. Internal stage on AWS eu-central-1 (Frankfurt)

Answer: A,B,D

Explanation:
Explanation
The valid sources for loading data from staged files into target tables using the copy into command are:
External stage on GCP us-central1 (Iowa): This is a valid source because Snowflake supports cross-cloud data loading from external stages on different cloud platforms and regions than the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland): This is a valid source because Snowflake supports data loading from external stages on the same cloud platform and region as the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt): This is a valid source because Snowflake supports cross-region data loading from external stages on different regions than the Snowflake deployment within the same cloud platform. The invalid sources are:
Internal stage on GCP us-central1 (Iowa): This is an invalid source because internal stages are always located on the same cloud platform and region as the Snowflake deployment. Therefore, an internal stage on GCP us-central1 (Iowa) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
Internal stage on AWS eu-central-1 (Frankfurt): This is an invalid source because internal stages are always located on the same region as the Snowflake deployment. Therefore, an internal stage on AWS eu-central-1 (Frankfurt) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland): This is an invalid source because SSO stands for Single Sign-On, which is a security integration feature in Snowflake, not a data staging option.

NEW QUESTION # 114
What are characteristics of Snowpark Python packages? (Select THREE).
Third-party packages can be registered as a dependency to the Snowpark session using the session, import () method.

A. The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
B. Python packages can only be loaded in a local environment
C. Python packages can access any external endpoints
D. Querying information__schema .packages will provide a list of supported Python packages and versions
E. Third-party supported Python packages are locked down to prevent hitting

Answer: A,C,D

Explanation:
Explanation
The characteristics of Snowpark Python packages are:
Third-party packages can be registered as a dependency to the Snowpark session using the session.import() method.
The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
Querying information_schema.packages will provide a list of supported Python packages and versions.
These characteristics indicate how Snowpark Python packages can be imported, inspected, and verified in Snowflake. The other options are not characteristics of Snowpark Python packages. Option B is incorrect because Python packages can be loaded in both local and remote environments using Snowpark. Option C is incorrect because third-party supported Python packages are not locked down to prevent hitting external endpoints, but rather restricted by network policies and security settings.

NEW QUESTION # 115
......

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New Snowflake DEA-C01 Exam Pdf - Instant DEA-C01 Download

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q110-Q115):

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