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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Related Certifications:	SnowPro Advanced: Architect SnowPro Advanced: Data Scientist SnowPro Advanced: Security Engineer SnowPro Advanced: Data Analyst SnowPro Core SnowPro Advanced: Administrator
Real Exam Qty:	65
Exam Duration:	115 minutes
Available Languages:	English, Japanese
Exam Price:	$375 USD
Certificate Validity Period:	2 years
Passing Score:	750 / 1000
Exam Format:	Multiple select, Multiple choice
Recommended Training:	SnowPro Advanced: Data Engineer Exam Study Guide Snowflake University Training Courses
Exam Registration:	Pearson VUE Registration Snowflake Certification Portal
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	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	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	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
A finance company receives data from third-party data providers and stores the data as objects in an Amazon S3 bucket.
The company ran an AWS Glue crawler on the objects to create a data catalog. The AWS Glue crawler created multiple tables. However, the company expected that the crawler would create only one table.
The company needs a solution that will ensure the AVS Glue crawler creates only one table.
Which combination of solutions will meet this requirement? (Choose two.)

A. Ensure that the schema is the same for each object. Do not enforce consistency for the file format and compression type of each object.
B. Ensure that the structure of the prefix for each S3 object name is consistent.
C. Ensure that the object format and schema are the same for each object. Do not enforce consistency for the compression type of each object.
D. Ensure that all S3 object names follow a similar pattern.
E. Ensure that the object format, compression type, and schema are the same for each object.

Answer: B,E

NEW QUESTION # 56
A company needs to store semi-structured transactional data for an application in a database.
The database must be serverless. The application writes the data infrequently, but it reads the data frequently. The application must retrieve the data within milliseconds. Which solution will meet these requirements with the LEAST operational overhead?

A. Store the data in an Amazon S3 Standard bucket. Enable S3 Transfer Acceleration.
B. Store the data in an Amazon S3 Apache Iceberg table. Enable S3 Transfer Acceleration.
C. Store the data in an Amazon DynamoDB table. Configure a DynamoDB Accelerator cache.
D. Store the data in an Amazon RDS for MySQL cluster. Configure RDS Optimized Reads for the cluster.

Answer: C

Explanation:
Amazon DynamoDB is a fully managed serverless database that is designed for millisecond response times and works well for semi-structured application data. Because the application reads frequently and writes infrequently, adding DynamoDB Accelerator (DAX) provides an in- memory cache that further reduces read latency with minimal operational overhead.

NEW QUESTION # 57
A company builds a new data pipeline to process data for business intelligence reports. Users have noticed that data is missing from the reports.
A data engineer needs to add a data quality check for columns that contain null values and for referential integrity at a stage before the data is added to storage.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon SageMaker Data Wrangler to create a Data Quality and Insights report.
B. Use Amazon SageMaker Data Wrangler and a custom Python transform to create custom rules to check for null values and referential integrity.
C. Use AWS Glue ETL jobs to perform a SQL transform on the data to determine whether requested column contain null values. Use a second SQL transform to check referential integrity.
D. Use AWS Glue ETL jobs to perform a data quality evaluation transform on the data. Use an IsComplete rule on the requested columns. Use a ReferentialItegrity rule for each join.

Answer: D

Explanation:
AWS Glue's built-in data quality evaluation transform lets you declaratively apply DQDL rules, like IsComplete for null checks and ReferentialIntegrity for joins, directly in your ETL job. This requires minimal custom code and no separate reporting or profiling infrastructure, giving you pre-load validations with the least operational overhead.

NEW QUESTION # 58
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

A. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.
B. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
C. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
D. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.

Answer: D

Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api-monitoring-events.html

NEW QUESTION # 59
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?

A. Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.
B. Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.
C. Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.
D. Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.

Answer: A

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 # 60
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