100% Pass 2026 Snowflake DEA-C01 Perfect Reliable Test Vce

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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
  • 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 5
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q212-Q217):

NEW QUESTION # 212
A company uses an Amazon Redshift cluster to manage data, including vendor sales data. The company wants to store a copy of the vendor data in an Amazon S3 bucket.
A data engineer sets up an AWS Glue job to upload the data to the S3 bucket data on a schedule. The data engineer set up a network connection to allow private traffic between Amazon Redshift and Amazon S3.
What is the next step required to meet this requirement?

Answer: A

Explanation:
Amazon Redshift needs an IAM role that grants permission to write to the target Amazon S3 bucket. After the private network path is in place, associating that role with the Redshift cluster enables the export operation to store the vendor data in S3.


NEW QUESTION # 213
A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies.
The sales table for the first company is named Table S1. The sales table for the second company is named Table S2. Table S1 contains 10 billion records. Table S2 contains 900 million records.
The query becomes slow after the modification. A data engineer must improve the query performance.
Which solutions will meet these requirements? (Choose two.)

Answer: B,E

Explanation:
Choosing KEY distribution on both tables with a high-cardinality join column colocates matching rows across nodes and avoids data skew, improving join performance.
Redshift Advisor provides automated, actionable recommendations (e.g., distribution and sort keys, stats) to further optimize the slow query with minimal effort.


NEW QUESTION # 214
A company uses AWS Glue ETL pipelines to process data. The company uses Amazon Athena to analyze data in an Amazon S3 bucket.
To better understand shipping timelines, the company decides to collect and store shipping and delivery dates in addition to order data. The company adds a data quality check to ensure that shipping date is greater than order date and that delivery date is greater than shipping date.
Orders that fail the quality check must be stored in a second S3 bucket.
Which solution will meet these requirements MOST cost-effectively?

Answer: C

Explanation:
AWS Glue Data Quality is the most cost-effective choice because it is built for ETL data validation and supports custom rules that compare multiple columns, such as ensuring shipping date is after order date and delivery date is after shipping date. It can also identify the specific records that fail validation so those failed orders can be written to a separate Amazon S3 bucket as part of the ETL flow.
Reference:
https://docs.aws.amazon.com/glue/latest/dg/glue-data-quality.html
https://docs.aws.amazon.com/glue/latest/dg/tutorial-data-quality.html
https://docs.aws.amazon.com/glue/latest/dg/dqdl-rule-types-CustomSql.html


NEW QUESTION # 215
A company stores customer data tables that include customer addresses in an AWS Lake Formation data lake. To comply with new regulations, the company must ensure that users cannot access data for customers who are in Canada.
The company needs a solution that will prevent user access to rows for customers who are in Canada.
Which solution will meet this requirement with the LEAST operational effort?

Answer: D


NEW QUESTION # 216
A Data Engineer is investigating a query that is taking a long time to return The Query Profile shows the following:

What step should the Engineer take to increase the query performance?

Answer: B

Explanation:
Explanation
The step that the Engineer should take to increase the query performance is to increase the size of the virtual warehouse. The Query Profile shows that most of the time was spent on local disk IO, which indicates that the query was reading a lot of data from disk rather than from cache. This could be due to a large amount of data being scanned or a low cache hit ratio. Increasing the size of the virtual warehouse will increase the amount of memory and cache available for the query, which could reduce the disk IO time and improve the query performance. The other options are not likely to increase the query performance significantly. Option A, adding additional virtual warehouses, will not help unless they are used in a multi-cluster warehouse configuration or for concurrent queries. Option C, rewriting the query using Common Table Expressions (CTEs), will not affect the amount of data scanned or cached by the query. Option D, changing the order of the joins and starting with smaller tables first, will not reduce the disk IO time unless it also reduces the amount of data scanned or cached by the query.


NEW QUESTION # 217
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