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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 Core SnowPro Advanced: Administrator SnowPro Advanced: Data Analyst SnowPro Advanced: Data Scientist SnowPro Advanced: Security Engineer
Available Languages:	English, Japanese
Passing Score:	750 / 1000
Exam Price:	$375 USD
Certificate Validity Period:	2 years
Exam Format:	Multiple select, Multiple choice
Real Exam Qty:	65
Exam Duration:	115 minutes
Recommended Training:	SnowPro Advanced: Data Engineer Exam Study Guide Snowflake University Training Courses
Exam Registration:	Snowflake Certification Portal Pearson VUE Registration
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 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 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	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	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 5	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q237-Q242):

NEW QUESTION # 237
A company needs a solution to automatically ingest data from an Amazon S3 bucket to an Amazon Redshift table every day. The company must obfuscate sensitive data in Amazon Redshift. Users must not be able to view the sensitive data as plaintext.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an AWS Glue job to read the data from the S3 bucket, detect and obfuscate sensitive data, and load the data into Amazon Redshift.
B. Enable Amazon Macie for the company's AWS account. Configure a sensitive data discovery job for the S3 bucket. Configure an AWS Glue job to load the data into Amazon Redshift.
C. Enable Amazon Macie for the company's AWS account. Configure an AWS Glue job to flag and obfuscate data columns based on the Macie analysis. Load the data into Amazon Redshift.
D. Configure an AWS Glue job to load the data into Amazon Redshift. Use Amazon Redshift to detect the sensitive data, create masking policies, and apply dynamic masking.

Answer: D

Explanation:
Schedule an AWS Glue job to load S3 data into Amazon Redshift, then use Redshift's built-in sensitive data discovery and dynamic data masking to identify sensitive columns and enforce masking at query time. This meets the daily ingest and non-plaintext visibility requirements with the least operational overhead, since masking is managed natively in Redshift without custom obfuscation pipelines or Macie-based preprocessing.

NEW QUESTION # 238
While working with Multi Cluster Warehouses, Select the incorrect understanding of Data Engineer about its usage?

A. Multi-cluster warehouses are designed specifically for handling queuing and perfor-mance issues related to large numbers of concurrent users and/or queries.
B. Multi-cluster warehouses generally improve query performance, particularly for larger, more complex queries.
C. When choosing the maximum number of clusters for a multi-cluster warehouse set its value as large as possible.
D. When choosing the minimum number of clusters for a multi-cluster warehouse keep the default value as
1.
E. Unless you have a specific requirement for running in Maximized mode, multi-cluster warehouses should be configured to run in Auto-scale mode, which enables Snowflake to automatically start and stop clusters as needed.

Answer: B

Explanation:
Explanation
Resizing a warehouse generally improves query performance, particularly for larger, more complex queries.
Multi-cluster warehouses are designed specifically for handling queuing and performance issues re-lated to large numbers of concurrent users and/or queries. In addition, multi-cluster warehouses can help automate this process if your number of users/queries tend to fluctuate.
Rest all are correct understanding.

NEW QUESTION # 239
A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.
A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places. The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).
Which solution will meet these requirements?

A. Define and create the calculated field in the analysis.
B. Define and create the calculated field in the visual.
C. Define and create the calculated field in the dataset.
D. Define and create the calculated field in the dashboard.

Answer: C

Explanation:
Defining and creating the calculated field in the dataset ensures that the exchange rate calculations are precomputed before being stored in SPICE (QuickSight's super-fast, parallel, in- memory calculation engine). This allows the calculation to be materialized and reused across different analyses and visuals, ensuring that the precision is maintained and the performance is optimized.
Creating the calculated field in the analysis would compute the result at the analysis level, not within the dataset. This approach doesn't guarantee precomputation within SPICE and may increase the calculation overhead for each analysis.
Defining the field in the visual means the calculation is performed dynamically every time the visual is rendered, which does not leverage SPICE's precomputation benefits and can result in slower performance.
Creating the calculated field at the dashboard level is similar to doing it at the visual level, meaning the calculations are not precomputed and stored in SPICE, leading to potential performance issues and less reuse of the calculation across different analyses or visuals.

NEW QUESTION # 240
Can the same column be specified in both a Dynamic data masking policy signature and a row ac-cess policy signature at the same time?

A. NO
B. YES

Answer: A

NEW QUESTION # 241
A company stores analysis results for thousands of daily customer service call transcripts in an Amazon DynamoDB table. An application generates business reports by using only the previous
7 working days of data. To reduce costs, the company wants to implement a solution that automatically removes data that is older than 7 working days.
Which solution will meet this requirement with the LEAST operational overhead?

A. Use AWS Glue jobs to implement a daily batch job to scan for and delete expired items from the DynamoDB table.
B. Use Amazon EventBridge to invoke an AWS Lambda function on a schedule to delete old records every day.
C. Enable TTL on the DynamoDB table. Add an expiration timestamp attribute to each item in the table.
D. Create a new DynamoDB table. Enable TTL on the new table. Migrate the data to the new table.
Update the application to use the new table.

Answer: C

Explanation:
DynamoDB TTL is the simplest native feature for automatically removing old items with minimal administration. By storing an expiration timestamp on each item and enabling TTL on the table, the company can let DynamoDB handle deletion of expired data automatically without building and maintaining scheduled jobs, Lambda code, or migration steps.

NEW QUESTION # 242
......

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