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Snowflake DEA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Engineer Certification Exam
Exam Number:DEA-C01
Available Languages:English
Related Certifications:SnowPro Core Certification
Exam Price:$375 USD
Real Exam Qty:Approximately 65 questions
Exam Format:Scenario-based questions, Multiple choice, Multiple select
Exam Duration:115 minutes
Passing Score:Not publicly disclosed (Snowflake uses scaled scoring)
Certificate Validity Period:2 years
Recommended Training:Snowflake University Training
SnowPro Advanced Data Engineer Exam Guide
Exam Registration:Snowflake Certification Portal
Sample Questions:Snowflake DEA-C01 Sample Questions
Exam Way:Online proctored or authorized testing center
Pre Condition:Recommended: SnowPro Core Certification or equivalent Snowflake experience
Official Syllabus URL:https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/

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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
  • 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
  • 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 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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q353-Q358):

NEW QUESTION # 353
A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real-time streaming data. The data in the Iceberg tables is in Apache Parquet format.
The company is experiencing slow query performance.
Which solutions will improve query performance? (Choose two.)

Answer: A,D

Explanation:
Compaction reduces many small Parquet files into larger ones, lowering file-listing and open/scan overhead for faster reads.
Copy-on-write favors read performance by materializing updates into rewritten data files, resulting in more contiguous, scan-efficient files for queries.


NEW QUESTION # 354
A data engineer is optimizing query performance in Amazon Athena notebooks that use Apache Spark to analyze large datasets that are stored in Amazon S3. The data is partitioned.
An AWS Glue crawler updates the partitions.
The data engineer wants to minimize the amount of data that is scanned to improve efficiency of Athena queries.
Which solution will meet these requirements?

Answer: C

Explanation:
By including predicates on your partition columns in each Athena query (for example, WHERE year = '2025' AND month = '05'), Athena prunes partitions at the optimizer level and reads only the matching S3 folders. This directly minimizes the data scanned and improves query efficiency with no additional infrastructure changes.


NEW QUESTION # 355
A company needs to implement a data mesh architecture in which domains for trading, risk, and compliance teams each have own their data. The teams need to share specific views with one another. The teams have over 1,000 tables across 50 databases in AWS Glue Data Catalog. All three teams use Amazon Athena to perform on-demand analysis. The teams use Amazon Redshift to generate complex reports. The compliance team must audit all data access. Access to personally identifiable information (PII) data must be restricted.
The company requires a scalable solution to meet the team requirements. The solution must provide the ability to perform analysis across team domains.
Which solution will meet these requirements?

Answer: B

Explanation:
AWS Lake Formation provides centralized, fine-grained governance (LF-Tags, column/row-level permissions) across domains, integrates with Athena and Redshift for cross-domain analytics, and supports comprehensive auditing via Lake Formation access logs and CloudTrail meeting scalability, PII restriction, and audit requirements.


NEW QUESTION # 356
Which output is provided by both theSYSTEM$CLUSTERING_DEPTHfunction and theSYSTEM$CLUSTERING_INFORMATIONfunction?

Answer: D

Explanation:
Explanation
The output that is provided by both the SYSTEM$CLUSTERING_DEPTH function and the SYSTEM$CLUSTERING_INFORMATION function is average_depth. This output indicates the average number of micro-partitions that contain data for a given column value or combinationof column values. The other outputs are not common to both functions. The notes output is only provided by the SYSTEM$CLUSTERING_INFORMATION function and it contains additional information or recommendations about the clustering status of the table. The average_overlaps output is only provided by the SYSTEM$CLUSTERING_DEPTH function and it indicates the average number of micro-partitions that overlap with other micro-partitions for a given column value or combination of column values. The total_partition_count output is only provided by the SYSTEM$CLUSTERING_INFORMATION function and it indicates the total number of micro-partitions in the table.


NEW QUESTION # 357
Which column provides information when the stream became stale or may become stale if not con-sumed?

Answer: C

Explanation:
Explanation
execute SHOW STREAMS command.
Column Name: STALE_AFTER
Timestamp when the stream became stale or may become stale if not consumed. The value is calcu-lated by adding the retention period for the source table (i.e. the larger of the DA-TA_RETENTION_TIME_IN_DAYS or MAX_DATA_EXTENSION_TIME_IN_DAYS parame-ter setting) to the last time the stream was read.


NEW QUESTION # 358
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