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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:	750 / 1000
Exam Format:	Multiple choice, Multiple select
Related Certifications:	SnowPro Advanced: Administrator SnowPro Advanced: Data Scientist SnowPro Advanced: Security Engineer SnowPro Core SnowPro Advanced: Data Analyst SnowPro Advanced: Architect
Available Languages:	English, Japanese
Certificate Validity Period:	2 years
Exam Price:	$375 USD
Real Exam Qty:	65
Exam Duration:	115 minutes
Recommended Training:	SnowPro Advanced: Data Engineer Exam Study Guide Snowflake University Training Courses
Exam Registration:	Pearson VUE Registration Snowflake Certification Portal
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 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	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	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 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	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 (Q331-Q336):

NEW QUESTION # 331
A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu- east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.
Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?

A. Set up an AWS DMS replication instance in Account_A in eu-east-1.
B. Set up an AWS DMS replication instance in Account_B in eu-east-1.
C. Set up an AWS DMS replication instance in a new AWS account in eu-west-1.
D. Set up an AWS DMS replication instance in Account_B in eu-west-1.

Answer: B

Explanation:
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.Redshift.html#CHAP_Target.R edshift.Prerequisites

NEW QUESTION # 332
Data Engineer identified use case where he decided to use materialized view for query perfor-mance. Which one is not the limitation he must be aware of before using MVs in their use case?

A. A materialized view cannot include HAVING clauses OR ORDER BY clause.
B. Time Travel is not currently supported on materialized views.
C. Truncating a materialized view is not supported.
D. A materialized views cannot be created on Shared Data.
E. You cannot directly clone a materialized view by using the CREATE MATERIAL-IZED VIEW ...
CLONE... command.
F. A materialized view can query only a single table & Joins, including self-joins, are not supported.
G. A materialized views does not support clustering.
H. Context Functions like CURRENT_TIME or CURRENT_TIMESTAMP is not per-mitted.

Answer: A,G

Explanation:
Explanation
Defining a clustering key on a materialized view is supported and can increase performance in many situations. However, it also adds costs.
If you cluster both the materialized view(s) and the base table on which the materialized view(s) are defined, you can cluster the materialized view(s) on different columns from the columns used to cluster the base table.
You can create a materialized view on shared data.
Also You can use Snowflake's data sharing feature to share a materialized view.
Rest all are correct.

NEW QUESTION # 333
To support Time Travel, Which of the following SQL extensions/parameters/commands have been implemented?

A. AT | BEFORE clause which can be specified in the CREATE ... CLONE commands.
B. ONSET (time difference in seconds from the present time)
C. STATEMENT (identifier for statement, e.g. query ID)
D. OFFSET (time difference in seconds from the present time)
E. UNDROP command for tables, schemas, and databases.
F. STATEMENT_ID (identifier for statement, e.g. query ID)

Answer: A,C,D,E

NEW QUESTION # 334
A Data Engineer is working on a continuous data pipeline which receives data from Amazon Kinesis Firehose and loads the data into a staging table which will later be used in the data transformation process The average file size is 300-500 MB.
The Engineer needs to ensure that Snowpipe is performant while minimizing costs.
How can this be achieved?

A. Split the files before loading them andset the SIZE_LIMIT option to 250 MB.
B. Decrease the buffer size to trigger delivery of files sized between 100 to 250 MB in Kinesis Firehose
C. Increase the size of the virtual warehouse used by Snowpipe.
D. Change the file compression size and increase the frequency of the Snowpipe loads

Answer: A

Explanation:
Explanation
This option is the best way to ensure that Snowpipe is performant while minimizing costs. By splitting the files before loading them, the Data Engineer can reduce the size of each file and increase the parallelism of loading.
By setting the SIZE_LIMIT option to 250 MB, the Data Engineer can specify the maximum file size that can be loaded bySnowpipe, which can prevent performance degradation or errors due to large files. The other options are not optimal because:
Increasing the size of the virtual warehouse used by Snowpipe will increase the performance but also increase the costs, as larger warehouses consume more credits per hour.
Changing the file compression size and increasing the frequency of the Snowpipe loads will not have much impact on performance or costs, as Snowpipe already supports various compression formats and automatically loads files as soon as they are detected in the stage.
Decreasing the buffer size to trigger delivery of files sized between 100 to 250 MB in Kinesis Firehose will not affect Snowpipe performance or costs, as Snowpipe does not depend on Kinesis Firehose buffer size but rather on its own SIZE_LIMIT option.

NEW QUESTION # 335
A data engineer uses Amazon Kinesis Data Streams to ingest and process records that contain user behavior data from an application every day.
The data engineer notices that the data stream is experiencing throttling because hot shards receive much more data than other shards in the data stream.
How should the data engineer resolve the throttling issue?

A. Limit the number of records that are sent each second by the producer to match the capacity of the stream.
B. Use a random partition key to distribute the ingested records.
C. Increase the number of shards in the data stream. Distribute the records across the shards.
D. Decrease the size of the records that the producer sends to match the capacity of the stream.

Answer: C

Explanation:
Amazon Kinesis Data Streams distribute data across multiple shards, with each shard having its own capacity for read and write operations. Throttling occurs when one or more shards, referred to as "hot shards," receive significantly more data than they can handle. To resolve this, increasing the number of shards in the data stream and redistributing the records across the shards is the appropriate solution. This approach ensures that the workload is spread more evenly, thereby preventing throttling on individual shards.

NEW QUESTION # 336
......

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