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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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 (Q304-Q309):

NEW QUESTION # 304
A healthcare company stores patient records in an on-premises MySQL database. The company creates an application to access the MySQL database. The company must enforce security protocols to protect the patient records. The company currently rotates database credentials every 30 days to minimize the risk of unauthorized access.
The company wants a solution that does require the company to modify the application code for each credential rotation.
Which solution will meet this requirement with the LEAST operational overhead?

Answer: B

Explanation:
AWS Secrets Manager is purpose-built to store sensitive credentials and rotate them automatically without requiring the application code to be updated for each password change.
The application retrieves the current secret dynamically through API calls, so credential rotation happens transparently while minimizing operational overhead.


NEW QUESTION # 305
A company creates a new non-production application that runs on an Amazon EC2 instance. The application needs to communicate with an Amazon RDS database instance using Java Database Connectivity (JDBC). The EC2 instances and the RDS database instance are in the same subnet.
Which solution will meet this requirement?

Answer: C

Explanation:
Connectivity from Amazon EC2 to an Amazon RDS DB instance in the same subnet or VPC is controlled by VPC security groups. Updating the database security group to allow inbound connections from the EC2 instances enables the application to connect to the database by using JDBC. IAM roles do not control direct JDBC network access, and the RDS Data API is not used through a JDBC connection string.
Reference:
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Overview.RDSSecurityGroups.html
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_VPC.Scenarios.html


NEW QUESTION # 306
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?

Answer: A

Explanation:
https://aws.amazon.com/blogs/big-data/orchestrate-an-etl-pipeline-using-aws-glue-workflows- triggers-and-crawlers-with-custom-classifiers/
https://aws.amazon.com/blogs/big-data/extracting-multidimensional-data-from-microsoft-sql- server-analysis-services-using-aws-glue/


NEW QUESTION # 307
A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files.
The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline.
The company needs to improve the performance of the second pipeline.
Which solution will meet this requirement MOST cost-effectively?

Answer: B

Explanation:
Using the AWS Glue DynamicFrame grouping option (for example, groupFiles=True with an appropriate groupSize) combines many small input files into larger partitions at read time. This reduces the per-file overhead of task initialization and metadata operations, yielding much faster ETL runs without the added cost of more or bigger workers.


NEW QUESTION # 308
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution.
The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
Which solution will meet these requirements with the LEAST operational effort?

Answer: D

Explanation:
Amazon Athena federated query allows querying data from multiple data sources, including Amazon Redshift, Teradata, and Google BigQuery, using their federated query connectors. This solution offers a serverless approach, reducing the operational overhead of managing infrastructure while allowing SQL-based transformations across all data sources. Once the data is read and joined, Athena can write the results back to Amazon S3 in the Iceberg table format with a Merge operation.
This approach minimizes the operational effort as Athena manages the complexity of connecting to different databases through its connectors, and you can perform the necessary transformations and data joins using familiar SQL.
While AWS Glue is a powerful ETL tool, it requires more operational effort to manage complex transformations across multiple systems, and managing native transforms across different engines (Redshift, Teradata, BigQuery) in Glue can introduce additional complexity.
Amazon EMR with PySpark can handle the task, but it requires more operational effort to manage and maintain the EMR cluster. Writing and maintaining PySpark code can also be more complex compared to using SQL in Athena.
Appflow is primarily designed for simple data movement between SaaS applications and AWS services, but it does not provide the complex transformation and joining capabilities needed for this scenario. Using Athena after Appflow for joins adds unnecessary complexity compared to directly using federated queries in Athena.


NEW QUESTION # 309
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