DEA-C01 Reliable Exam Syllabus - DEA-C01 Exam Format

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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
Real Exam Qty:	65
Related Certifications:	SnowPro Advanced: Data Scientist SnowPro Advanced: Architect SnowPro Advanced: Administrator SnowPro Advanced: Security Engineer SnowPro Advanced: Data Analyst SnowPro Core
Passing Score:	750 / 1000
Exam Format:	Multiple select, Multiple choice
Available Languages:	Japanese, English
Certificate Validity Period:	2 years
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	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	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	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	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q264-Q269):

NEW QUESTION # 264
Robert, A Data Engineer, found that Pipe become stale as it was paused for longer than the limited retention period for event messages received for the pipe (14 days by default) & also the previous pipe owner transfers the ownership of this pipe to Robert role while the pipe was paused. How Robert in this case, Resume this stale pipe?

A. He can apply System function SYSTEM$PIPE_STALE_RESUME with ALTER PIPE statement.
B. ALTER PIPES ... RESUME statement will resume the pipe.
C. PIPE needs to recreate in this scenario, as pipe already past 14 days of period & stale.
D. select sys-tem$pipe_force_resume('mydb.myschema.stalepipe','staleness_check_override, ownership_transfer_check_override');
E. Robert can use SYSTEM$PIPE_FORCE_RESUME function to resume this stale pipe.

Answer: D

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.stalepipe1','staleness_check_override'); While the stale pipe was paused, if ownership of the pipe was transferred to another role, then re-suming the pipe requires the additional OWNERSHIP_TRANSFER_CHECK_OVERRIDE argu-ment. For example, resume the stale stalepipe2 pipe in the mydb.myschema database and schema, which transferred to a new role:
SELECT SYS-TEM$PIPE_FORCE_RESUME('mydb.myschema.stalepipe1','staleness_check_override, own-ership_transfer_check_override');

NEW QUESTION # 265
A company has a data lake in Amazon S3. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run.
A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?

A. Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.
B. Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.
C. Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.
D. Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.

Answer: C

Explanation:
Evaluate Data Quality Transform in AWS Glue is a built-in feature that allows the application of Data Quality Definition Language (DQDL) to define and apply data quality rules directly within AWS Glue ETL jobs. This option offers the least implementation effort because it leverages a native AWS Glue feature, allowing data engineers to easily add and enforce data quality checks without needing custom code or external libraries.
While SQL can implement data quality rules, this approach requires manually writing and managing SQL queries within each job, leading to higher implementation effort compared to using built-in Glue functionality.
PyDeequ is an external library that requires custom coding and additional integration effort.
Although it is effective, it demands more implementation effort than using AWS Glue's built-in features.
Great Expectations is another external data quality library. While it is robust, it also involves custom coding and higher maintenance effort compared to AWS Glue's native data quality evaluation tools.

NEW QUESTION # 266
A company stores customer records in Amazon S3. The company must not delete or modify the customer record data for 7 years after each record is created. The root user also must not have the ability to delete or modify the data.
A data engineer wants to use S3 Object Lock to secure the data.
Which solution will meet these requirements?

A. Enable compliance mode on the S3 bucket. Use a default retention period of 7 years.
B. Set the retention period for individual objects in the S3 bucket to 7 years.
C. Place a legal hold on individual objects in the S3 bucket. Set the retention period to 7 years.
D. Enable governance mode on the S3 bucket. Use a default retention period of 7 years.

Answer: A

Explanation:
Compliance Mode is the stricter mode of S3 Object Lock. When enabled, it ensures that no one, not even the root user, can modify or delete the object until the retention period expires. In this case, to meet the requirement of preventing any deletion or modification of the data for 7 years, compliance mode is the correct choice.
Governance Mode allows some users with special permissions (such as the root user) to override the lock and delete or modify the data, which does not meet the strict requirement that even the root user should not have this ability.
Legal Hold is used to prevent deletion of objects without specifying a time-based retention period, but it can be removed by users with sufficient permissions, and the root user might still be able to delete the data.
Retention Period for Individual Objects is possible, but enabling compliance mode provides a more robust solution by applying the same 7-year retention policy bucket-wide, ensuring that no modifications or deletions can occur during that period.

NEW QUESTION # 267
What kind of Snowflake integration is required when defining an external function in Snowflake?

A. HTTP integration
B. Notification integration
C. API integration
D. Security integration

Answer: C

Explanation:
Explanation
An API integration is required when defining an external function in Snowflake. An API integration is a Snowflake object that defines how Snowflake communicates with an externalservice via HTTPS requests and responses. An API integration specifies parameters such as URL, authentication method, encryption settings, request headers, and timeout values. An API integration is used to create an external function object that invokes the external service from within SQL queries.

NEW QUESTION # 268
Which of the following System keeps the following characteristics?
a. It will keep in it all the raw data.
b. Generally, the users of it is data scientists and data developers.
c. Flat architecture
d. Highly agile

A. Data Mart
B. Data Warehouse
C. Data Hub
D. Data Lake

Answer: D

NEW QUESTION # 269
......

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