DEA-C01 Latest Dumps Sheet | DEA-C01 Exam Topics

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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	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 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	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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q229-Q234):

NEW QUESTION # 229
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable formation (PII) about customers within the data. The company's privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules.
The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake.
Which solution will meet these requirements with the LEAST operational overhead?

A. Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.
B. Implement regex patterns to extract PII information from fields during extract transform, and load (ETL) operations into the data lake.
C. Manually review the data for custom PII categories.
D. Develop custom Python scripts to detect the custom PII categories. Call the scripts from DataBrew.

Answer: A

Explanation:
AWS Glue DataBrew allows users to create custom data quality rules, including rules for detecting non-standard PII categories. By implementing custom rules, the data engineer can automate the detection of PII (including the custom categories) across multiple datasets without needing to manually review or develop custom scripts. This provides a solution with the least operational overhead, as it leverages the existing DataBrew framework and automation capabilities.
Manually reviewing the data is highly labor-intensive, error-prone, and not scalable, especially across multiple datasets.
While possible, writing custom Python scripts adds operational overhead, as it requires developing, maintaining, and integrating scripts with DataBrew.
This increases complexity compared to implementing custom rules directly within DataBrew.
While regex can be useful, incorporating it directly into ETL processes adds operational complexity and doesn't leverage the capabilities of DataBrew, which is already designed for data cleaning and quality validation.

NEW QUESTION # 230
A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.
The company wants to achieve optimal throughput from each device that runs the gaming app.
Additionally, the company wants to develop an application to process data streams. The stream- processing application must have dedicated throughput for each internal consumer.
Which solution will meet these requirements?

A. Configure the mobile app to call the PutRecordBatch API operation to send data to Amazon Kinesis Data Firehose. Submit an AWS Support case to turn on dedicated throughput for the company's AWS account. Allow each internal consumer to access the stream.
B. Configure the mobile app to use the Amazon Kinesis Producer Library (KPL) to send data to Amazon Kinesis Data Firehose. Use the enhanced fan-out feature with a stream for each internal consumer.
C. Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Host the stream-processing application for each internal consumer on Amazon EC2 instances. Configure auto scaling for the EC2 instances.
D. Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature with a stream for each internal consumer.

Answer: D

NEW QUESTION # 231
A data engineer maintains a materialized view that is based on an Amazon Redshift database.
The view has a column named load_date that stores the date when each row was loaded.
The data engineer needs to reclaim database storage space by deleting all the rows from the materialized view.
Which command will reclaim the MOST database storage space?

A. DELETE FROM materialized_view_name where 1=1
B. DELETE FROM materialized_view_name where load_date<=current_date
C. VACUUM table_name where load_date<=current_date materializedview
D. TRUNCATE materialized_view_name

Answer: D

NEW QUESTION # 232
Which Function would Data engineer used to recursively resume all tasks in Chain of Tasks rather than resuming each task individually (using ALTER TASK ... RESUME)?

A. SYSTEM$TASK_RECURSIVE_ENABLE
B. SYSTEM$TASK_DEPENDENTS_ENABLE
C. SYSTEM$TASK_DEPENDENTS_RESUME
D. SYSTEM$TASK_DEPENDENTS

Answer: B

Explanation:
Explanation
To recursively resume all tasks in a DAG(A Directed Acyclic Graph (DAG) is a series of tasks com-posed of a single root task and additional tasks, organized by their dependencies.), query the SYS-TEM$TASK_DEPENDENTS_ENABLE function rather than resuming each task individually (us-ing ALTER TASK ... RESUME).

NEW QUESTION # 233
Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)

A. session.builder()
B. session.read.json{)
C. session.sql()
D. session,table()
E. DataFraas.writeO
F. session.jdbc_connection()

Answer: B,C,D

Explanation:
Explanation
The methods that can be used to create a DataFrame object in Snowpark are session.read.json(), session.table(), and session.sql(). These methods can create a DataFrame from different sources, such as JSON files, Snowflake tables, or SQL queries. The other options are not methods that can create a DataFrame object in Snowpark. Option A, session.jdbc_connection(), is a method that can create a JDBC connection object to connect to a database. Option D, DataFrame.write(), is a method that can write a DataFrame to a destination, such as a file or a table. Option E, session.builder(), is a method that can create a SessionBuilder object to configure and build a Snowpark session.

NEW QUESTION # 234
......

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