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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Passing Score:	Not publicly disclosed (Snowflake uses scaled scoring)
Certificate Validity Period:	2 years
Exam Duration:	115 minutes
Real Exam Qty:	Approximately 65 questions
Exam Format:	Multiple choice, Multiple select, Scenario-based questions
Available Languages:	English
Related Certifications:	SnowPro Core Certification
Exam Price:	$375 USD
Recommended Training:	SnowPro Advanced Data Engineer Exam Guide Snowflake University Training
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:

Topic	Details
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	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 3	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 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	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 (Q198-Q203):

NEW QUESTION # 198
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)

A. Use an S3 bucket that is in the same account that uses Athena to query the data.
B. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
C. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
D. Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
E. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.

Answer: C,E

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html

NEW QUESTION # 199
The following chart represents the performance of a virtual warehouse over time:

A DataEngineer notices that the warehouse is queueing queries The warehouse is size X-Smallthe minimum and maximum cluster counts are set to 1 the scaling policy is set to i and auto-suspend is set to 10 minutes.
How canthe performance be improved?

A. Change the cluster settings
B. Change the scaling policy to economy
C. Increase the size of the warehouse
D. Change auto-suspend to a longer time frame

Answer: C

Explanation:
Explanation
The performance can be improved by increasing the size of the warehouse. The chart shows that the warehouse is queueing queries, which means that there are more queries than the warehouse can handle at its current size. Increasing the size of the warehouse will increase its processing power and concurrency limit, which could reduce the queueing time and improve the performance. The other options are not likely to improve the performance significantly. Option A, changing the cluster settings, will not help unless the minimum and maximum cluster countsare increased to allow for multi-cluster scaling. Option C, changing the scaling policy to economy, will not help because it will reduce the responsiveness of the warehouse to scale up or down based on demand. Option D, changing auto-suspend to a longer time frame, will not help because it will only affect how long the warehouse stays idle before suspending itself.

NEW QUESTION # 200
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time. Which solution will meet these requirements?

A. Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
B. Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.
C. Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.
D. Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.

Answer: A

Explanation:
Amazon Athena Federated Query can run SQL across data in Amazon S3 and external sources by using data source connectors, and AWS provides connectors for Amazon DynamoDB, Amazon RDS, and Amazon Redshift. That lets the company perform a one-time cross-source join and analysis without first centralizing or copying the data, which minimizes unnecessary data movement and setup effort.
Reference:
https://docs.aws.amazon.com/athena/latest/ug/federated-queries.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-redshift.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-dynamodb.html

NEW QUESTION # 201
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?

A. Use AWS Glue extract, transform, and load (ETL) jobs with dynamic frames and the ResolveChoice class to align schemas and detect structural differences between files.
B. Use AWS Glue DataBrew to generate profiling dashboards for each dataset. Monitor quality metrics through manual review.
C. Use AWS Glue Studio to create a daily extract, transform, and load (ETL) pipeline that merges datasets and applies validation logic by using PySpark transformations.
D. Use AWS Glue DataBrew to configure profiling jobs and reusable recipe actions. Schedule the profiling jobs and reusable recipe actions to run against each dataset in Amazon S3.

Answer: D

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 # 202
Dominic, a Data Engineer wants to resume the pipe named stalepipe3 which got stale after 14 days. To do the same, he called the SYSTEM$PIPE_FORCE_RESUME function select sys-tem$pipe_force_resume('snowmydb.mysnowschema.stalepipe3','staleness_check_override'); Let's say If the pipe is resumed 16 days after it was paused, what will happened to the event notifi-cation that were received on the first and second days after the pipe was paused?

A. All the events get processed from day 1 if the PURGE properties in the PIPE object definition set to be FALSE initially.
B. Once the Pipe got stale, all the events got purged automatically & pipe needs to be rec-reated with modified properties.
C. Snowpipe generally skips any event notifications that were received on the first and second days after the pipe was paused.
D. Pipe maintains Metadata history of files for 64 days, so in this scenarios Snowpipe pro-cessed all the event notifications that were received for 16 days or so.

Answer: C

Explanation:
Explanation
When a pipe is paused, event messages received for the pipe enter a limited retention period. The period is 14 days by default. If a pipe is paused for longer than 14 days, it is considered stale.
To resume a stale pipe, a qualified role must call the SYSTEM$PIPE_FORCE_RESUME function and input the STALENESS_CHECK_OVERRIDE argument. This argument indicates an under-standing that the role is resuming a stale pipe.
For example, resume the stale stalepipe1 pipe in the mydb.myschema database and schema:
select sys-tem$pipe_force_resume('mydb.myschema.stalepipe3','staleness_check_override'); As an event notification received while a pipe is paused reaches the end of the limited retention pe-riod, Snowflake schedules it to be dropped from the internal metadata. If the pipe is later resumed, Snowpipe processes these older notifications on a best effort basis. Snowflake cannot guarantee that they are processed.
For example, if a pipe is resumed 15 days after it was paused, Snowpipe generally skips any event notifications that were received on the first day the pipe was paused (i.e. that are now more than 14 days old).
If the pipe is resumed 16 days after it was paused, Snowpipe generally skips any event notifications that were received on the first and second days after the pipe was paused. And so on.

NEW QUESTION # 203
......

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