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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Pipeline Orchestration & Automation	20%	- Integrate with external orchestration tools - Build, schedule, monitor, and manage workflows - Error handling, retry logic, and dependency management
Topic 2: Data Transformation & Processing	30%	- Design and implement transformations: SQL, stored procedures, user-defined functions - Use streams, tasks, dynamic tables for pipelines and CDC - Work with semi-structured and unstructured data - Implement data quality, validation, and schema evolution
Topic 3: Data Governance, Security & Sharing	10%	- Apply access control, masking, encryption, and compliance - Share data securely across accounts and platforms - Track lineage, audit, and maintain data integrity
Topic 4: Performance Optimization & Scalability	15%	- Design scalable compute and storage solutions - Optimize warehouses, clustering, partitioning, and query performance - Monitor and tune workloads and resource usage
Topic 5: Data Ingestion	25%	- Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming - Ingest from various sources: cloud storage, on-premises, APIs, data lakes - Handle file formats, compression, encoding, and schema detection

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q121-Q126):

NEW QUESTION # 121
Ryan, a Data Engineer, accidently drop the Share named SF_SHARE which results in immediate access revoke for all the consumers (i.e., accounts who have created a database from that SF_SHARE). What action he can take to recover the dropped Share?

A. He can recreate a share with the same name as a previous share which does restore the databases created (by any consumers) from the share SF_SHARE.
B. By Executing UNDROP command he could possibly recover the dropped Share SF_SHARE & its associated Databases for immediate consumer access.
C. Consumer accounts that have created databases from the share will still be able to que-ry these databases as Share is separate securable object & it's still accessible using time travel feature.
D. A dropped share cannot be restored. The share must be created again using the CRE-ATE SHARE command and then configured using GRANT <privilege> ... TO SHARE and ALTER SHARE.

Answer: D

Explanation:
Explanation
You can drop a share at any time using the DROP SHARE command.
Dropping a share instantly invalidates all databases created from the share by consumer accounts.
All queries and other operations performed on these databases will no longer work.
After dropping a share, you can recreate it with the same name;
however, this does not restore any of the databases created from the share by consumer accounts.
The recreated share is treated as a new share and all consumer accounts must create a new database from the new share.

NEW QUESTION # 122
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. Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
B. Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.
C. 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.
D. 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.

Answer: A

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 # 123
A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?

A. Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an IAM policy that uses the tags to apply appropriate permissions to the workgroup.
B. Create an IAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.
C. Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.
D. Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.

Answer: A

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/user-created-workgroups.html

NEW QUESTION # 124
A company uses Apache Airflow to orchestrate the company's current on-premises data pipelines. The company runs SQL data quality check tasks as part of the pipelines. The company wants to migrate the pipelines to AWS and to use AWS managed services.
Which solution will meet these requirements with the LEAST amount of refactoring?

A. Create a custom Amazon Machine Image (AMI) that contains the Airflow application and the code that the company needs to migrate. Use the custom AMI to deploy Amazon EC2 instances.
Update the network connections to interact with the newly deployed EC2 instances.
B. Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow.
C. Setup AWS Outposts in the AWS Region that is nearest to the location where the company uses Airflow. Migrate the servers into Outposts hosted Amazon EC2 instances. Update the pipelines to interact with the Outposts hosted EC2 instances instead of the on-premises pipelines.
D. Convert the pipelines to AWS Step Functions workflows. Recreate the data quality checks in SQL as Python based AWS Lambda functions.

Answer: B

Explanation:
Amazon MWAA is a managed service for running Apache Airflow. It allows migrating existing Airflow configurations with minimal changes. Data quality checks can continue to be implemented as SQL tasks in Airflow, similar to the current setup.

NEW QUESTION # 125
Data engineer designed the data pipelines using Snowpipe to load data files into Snowflake tables, what will happen in case few files with same name but modified data are queued for reloading?

A. Data will be reloaded as files are modified & its associated metadata also changed. But Snowflake handle implicitly deduplication.
B. eTAG is changed for Files even they are having same name, so data will be duplicated in SnowFlake tables.
C. Snowpipe uses file loading metadata associated with each table object, so no metadata available to prevent duplication.
D. Snowpipe uses file loading metadata associated with each pipe object to prevent reload-ing the same files (and duplicating data) in a table.

Answer: D

Explanation:
Explanation
Snowflake uses file loading metadata to prevent reloading the same files (and duplicating data) in a table.
Snowpipe prevents loading files with the same name even if they were later modified (i.e. have a different eTag).
The file loading metadata is associated with the pipe object rather than the table. As a result:
Staged files with the same name as files that were already loaded are ignored, even if they have been modified, e.g. if new rows were added or errors in the file were corrected.
Truncating the table using the TRUNCATE TABLE command does not delete the Snowpipe file loading metadata.

NEW QUESTION # 126
......

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