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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Exam Price:	$375 USD
Available Languages:	Japanese, English
Exam Duration:	115 minutes
Certificate Validity Period:	2 years
Related Certifications:	SnowPro Advanced: Data Analyst SnowPro Advanced: Architect SnowPro Advanced: Data Scientist SnowPro Advanced: Security Engineer SnowPro Core SnowPro Advanced: Administrator
Passing Score:	750 / 1000
Real Exam Qty:	65
Exam Format:	Multiple select, Multiple choice
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	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 2	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 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	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 5	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q196-Q201):

NEW QUESTION # 196
The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first
30 days after the records are created. The company accesses records that are older than 30 days infrequently.
The company needs to cost-optimize its Amazon S3 storage.
Which solution will meet these requirements MOST cost-effectively?

A. Use S3 Standard-Infrequent Access (S3 Standard-IA) storage for all customer records.
B. Transition records to S3 Glacier Deep Archive storage after 30 days.
C. Apply a lifecycle policy to transition records to S3 Standard Infrequent-Access (S3 Standard-IA) storage after 30 days.
D. Use S3 Intelligent-Tiering storage.

Answer: C

Explanation:
S3 Standard-IA (Infrequent Access) is ideal for data that is accessed less frequently but requires immediate access when needed. Since the company needs immediate access to customer records for the first 30 days and infrequent access afterward, applying an S3 lifecycle policy to automatically transition the data to S3 Standard-IA after 30 days provides a cost-effective solution. This way, records remain easily accessible during the initial 30 days and are moved to a cheaper storage tier when they are accessed less often.
S3 Intelligent-Tiering automatically moves data between frequent and infrequent access tiers based on access patterns, but it incurs additional monitoring and automation charges. This option is typically more expensive compared to S3 Standard-IA if the access pattern is well understood (like in this case, where data is accessed frequently for the first 30 days and infrequently thereafter).
S3 Glacier Deep Archive is a low-cost storage option for archival data that is rarely accessed and requires hours for retrieval. It does not meet the requirement for immediate access to records after 30 days.
Storing all data in S3 Standard-IA is more expensive during the first 30 days when the data is accessed frequently. S3 Standard is more cost-effective for frequently accessed data during that period.

NEW QUESTION # 197
A company needs to optimize storage for an Amazon S3 bucket. Objects older than 1 year must be accessible within 5 hours. All versions of the objects must be retained and immutable for 7 years. All versions of the objects must use the write-once-read-many (WORM) model. Which solution will meet these requirements?

A. Configure S3 Versioning on the bucket and use the S3 Intelligent-Tiering storage class. Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Deep Archive. Configure the policy to deletes objects that are older than 7 years.
B. Configure S3 Object Lock on the bucket and use the S3 Intelligent-Tiering storage class.
Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Flexible Retrieval. Configure the policy to delete objects that are older than 7 years.
C. Configure S3 Versioning on the bucket and use the S3 Intelligent-Tiering storage class. Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Flexible Retrieval. Configure the policy to delete objects that are older than 7 years.
D. Configure S3 Object Lock on the bucket and use the S3 Intelligent-Tiering storage class.
Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Deep Archive. Configure the policy to delete objects that are older than 7 years.

Answer: B

Explanation:
S3 Object Lock enforces the write-once-read-many model and ensures that all object versions remain immutable for the required 7-year retention period. Transitioning objects older than 1 year to S3 Glacier Flexible Retrieval satisfies the requirement that data be accessible within 5 hours, as this storage class supports retrieval within minutes to a few hours. The lifecycle policy can expire objects after 7 years, aligning with the retention requirement once the Object Lock retention period has ended.

NEW QUESTION # 198
A data engineer wants to improve the performance of SQL queries in Amazon Athena that run against a sales data table.
The data engineer wants to understand the execution plan of a specific SQL statement. The data engineer also wants to see the computational cost of each operation in a SQL query.
Which statement does the data engineer need to run to meet these requirements?

A. EXPLAIN SELECT * FROM sales;
B. EXPLAIN ANALYZE FROM sales;
C. EXPLAIN FROM sales;
D. EXPLAIN ANALYZE SELECT * FROM sales;

Answer: D

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/athena-explain-statement.html

NEW QUESTION # 199
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?

A. MOVE city, state FROM cities_world TO cities_usa WHERE country='usa';
B. UPDATE cities_usa SET (city, state) = (SELECT city, state FROM cities_world WHERE country='usa');
C. INSERT INTO cities_usa SELECT city, state FROM cities_world WHERE country='usa';
D. INSERT INTO cities_usa (city,state) SELECT city, state FROM cities_world WHERE country='usa';

Answer: D

NEW QUESTION # 200
A Data Engineer is implementing a near real-time ingestionpipeline to toad data into Snowflake using the Snowflake Kafka connector. There will be three Kafka topics created.
......snowflake objects are created automatically when the Kafka connector starts? (Select THREE)

A. Tables
B. Pipes
C. internal stages
D. Materialized views
E. External stages
F. Tasks

Answer: A,B,C

Explanation:
Explanation
The Snowflake objects that are created automatically when the Kafka connector starts are tables, pipes, and internal stages. The Kafka connector will create one table, one pipe, and oneinternal stage for each Kafka topic that is configured in the connector properties. The table will store the data from the Kafka topic, the pipe will load the data from the stage to the table using COPY statements, and the internal stage will store the files that are produced by the Kafka connector using PUT commands. The other options are not Snowflake objects that are created automatically when the Kafka connector starts. Option B, tasks, are objects that can execute SQL statements on a schedule without requiring a warehouse. Option E, external stages, are objects that can reference locations outside of Snowflake, such as cloud storage services. Option F, materialized views, are objects that can store the precomputed results of a query and refresh them periodically.

NEW QUESTION # 201
......

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