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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 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	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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q181-Q186):

NEW QUESTION # 181
A Data Engineer is trying to load the following rows from a CSV file into a table in Snowflake with the following structure:

....engineer is using the following COPY INTO statement:

However, the following error is received.

Which file format option should be used to resolve the error and successfully load all the data into the table?

A. FIELD_DELIMITER = ","
B. ERROR_ON_COLUMN_COUKT_MISMATCH = FALSE
C. FIELD OPTIONALLY ENCLOSED BY = " "
D. ESC&PE_UNENGLO9ED_FIELD = '\\'

Answer: C

Explanation:
Explanation
The file format option that should be used to resolve the error and successfully load all the data into the table is FIELD_OPTIONALLY_ENCLOSED_BY = '"'. This option specifies that fields in the file may be enclosed by double quotes, which allows for fields that contain commas or newlines within them. For example, in row 3 of the file, there is a field that contains a comma within double quotes: "Smith Jr., John". Without specifying this option, Snowflake will treat this field as two separate fields and cause an error due to column count mismatch. By specifying this option, Snowflake will treat this field as one field and load it correctly into the table.

NEW QUESTION # 182
A company wants to use machine learning (ML) to perform analytics on data that is in an Amazon S3 data lake. The company has two data transformation requirements that will give consumers within the company the ability to create reports.
The company must perform daily transformations on 300 GB of data that is in a variety format that must arrive in Amazon S3 at a scheduled time. The company must perform one-time transformations of terabytes of archived data that is in the S3 data lake. The company uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Directed Acyclic Graphs (DAGs) to orchestrate processing.
Which combination of tasks should the company schedule in the Amazon MWAA DAGs to meet these requirements MOST cost-effectively? (Choose two.)

A. For daily incoming data, use Amazon Athena to scan and identify the schema.
B. For daily incoming data, use AWS Glue crawlers to scan and identify the schema.
C. For archived data, use Amazon SageMaker to perform data transformations.
D. For daily and archived data, use Amazon EMR to perform data transformations.
E. For daily incoming data, use Amazon Redshift to perform transformations.

Answer: B,D

NEW QUESTION # 183
Snowflake web interface can be used to create users with no passwords or remove passwords from existing users?

A. FALSE
B. TRUE

Answer: A

Explanation:
Explanation
You cannot use the Snowflake web interface to create users with no passwords or remove pass-words from existing users. You must use CREATE USER or ALTER USER command instead.
Also If you create a user with no password (or alter an existing user and remove their password), this effectively disables Snowflake authentication for the user. Without a password in Snowflake, a user cannot log in using Snowflake authentication and must use federated authentication instead.

NEW QUESTION # 184
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?

A. Establish WebSocket connections to Amazon Redshift.
B. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.
C. Use the Amazon Redshift Data API.
D. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.

Answer: C

Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api.html

NEW QUESTION # 185
How Data Engineer can do Monitoring of Files which are Staged Internally during Continuous data pipelines loading process? [Select all that apply]

A. She can use the DATA_VALIDATE function to validate the data files She have loaded and can retrieve any errors encountered during the load.
B. She can use the DATA_LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.
C. Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days.
D. She Can Monitor the files using Metadata maintained by Snowflake i.e. file-name,last_modified date etc.
E. She can Monitor the status of each COPY INTO <table> command on the History tab page of the classic web interface.

Answer: C,D,E

Explanation:
Explanation
Monitoring Files Staged Internally
Snowflake maintains detailed metadata for each file uploaded into internal stage (for users, tables, and stages), including:
File name
File size (compressed, if compression was specified during upload)
LAST_MODIFIED date, i.e. the timestamp when the data file was initially staged or when it was last modified, whichever is later In addition, Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days. The metadata can be used to monitor and manage the loading process, including de-leting files after upload completes:
Use the LIST command to view the status of data files that have been staged.
Monitor the status of each COPY INTO <table> command on the History tab page of the classic web interface.
Use the VALIDATE function to validate the data files you've loaded and retrieve any errors en-countered during the load.
Use the LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.

NEW QUESTION # 186
......

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