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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 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
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
A data engineer needs to validate the quality of files that are uploaded to an Amazon S3 bucket every day. The files are in CSV and JSON formats and schema variations exist. The data engineer needs a repeatable process to monitor data quality metrics such as null values, format inconsistencies, and outliers. The process must provide reusable rule-based profiling and scale across multiple datasets with minimal manual effort or custom code.
Which solution will meet these requirements?

Answer: A

Explanation:
AWS Glue DataBrew is designed for low-code data profiling and data quality validation across datasets stored in Amazon S3. It can generate profile statistics such as null values and outliers, and it supports reusable data quality rules and repeatable jobs, which matches the requirement for a scalable, low-effort process across CSV and JSON datasets with schema variation. Manual dashboard review alone would not provide the repeatable rule-based validation the question requires.
Reference:
https://docs.aws.amazon.com/databrew/latest/dg/profile.data-quality-rules.html
https://docs.aws.amazon.com/databrew/latest/dg/getting-started.02.html


NEW QUESTION # 54
A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team. The compliance team must have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

Answer: A,D


NEW QUESTION # 55
A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format:
s3://bucket/prefix/year=2023/month=01/day=01.
A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.
Which solution will meet these requirements with the LEAST latency?

Answer: B

Explanation:
Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call.
This approach ensures that the Data Catalog is updated as soon as new data is written to S3, providing the least latency in reflecting new partitions.


NEW QUESTION # 56
Charles, A Lead Data engineer, with ACCOUNTADMIN role wants to configure the time travel for one of the Schema's object. He setup the MIN_DATA_RETENTION_TIME_IN_DAYS pa-rameter with Value 79 at account level but he figured out that DA-TA_RETENTION_TIME_IN_DAYS is already set with value 81 at account level. What would be the effective minimum data retention period for an object?

Answer: B

Explanation:
Explanation
A user with the ACCOUNTADMIN role can also set the MIN_DATA_RETENTION_TIME_IN_DAYS at the account level. This parameter setting enforc-es a minimum data retention period for databases, schemas, and tables. Setting MIN_DATA_RETENTION_TIME_IN_DAYS does not alter or replace the DA-TA_RETENTION_TIME_IN_DAYS parameter value. It may, however, change the effective data retention period for objects. When MIN_DATA_RETENTION_TIME_IN_DAYS is set at the ac-count level, the data retention period for an object is determined by MAX(DATA_RETENTION_TIME_IN_DAYS, MIN_DATA_RETENTION_TIME_IN_DAYS).


NEW QUESTION # 57
A retail company needs to implement a solution to capture data updates from multiple Amazon Aurora MySQL databases. The company needs to make the updates available for analytics in near real time. The solution must be serverless and require minimal maintenance. Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Aurora zero-ETL integration with Amazon Redshift Serverless is the most direct fit because it delivers near real-time data availability for analytics with a fully managed approach and minimal maintenance. It avoids building and operating custom CDC pipelines, streaming infrastructure, or connector frameworks, which gives it the least operational overhead for multiple Aurora MySQL databases.


NEW QUESTION # 58
......

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