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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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 (Q51-Q56):

NEW QUESTION # 51
A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII.
Which solution will meet this requirement with the LEAST operational effort?

A. Ingest the dataset into Amazon DynamoDB. Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake.
B. Use the Detect PII transform in AWS Glue Studio to identify the PII. Create a rule in AWS Glue Data Quality to obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
C. Use the Detect PII transform in AWS Glue Studio to identify the PII. Obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
D. Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PII. Use an AWS SDK to obfuscate the PII. Set the S3 data lake as the target for the delivery stream.

Answer: B

NEW QUESTION # 52
A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data.
Which solution will meet these requirements MOST cost-effectively?

A. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
B. Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.
C. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
D. Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.

Answer: A

NEW QUESTION # 53
Data Engineer, ran the below clustering depth analysis function:
select system$clustering_depth('TPCH_CUSTOMERS', '(C1, C6)', 'C9 = 30'); on TPCH_CUSTOMERS table, will return which of the following?

A. An error: this function does not accept lists of columns as a third parameter.
B. An error: this function does not accept predicates ('C9 = 30') as parameter.
C. Calculate the clustering depth for a table using the clustering key defined for the table.
D. Calculate the clustering depth for a table using mentioned columns in the table.

Answer: D

NEW QUESTION # 54
Mohan, Data engineer works with ALTUSO Company, wants to programmatically check the status of the query. He needs query id to identify each query executed by Snowflake & using Snowflake Connector for Python to execute a query, how he will be able to meet this requirements. Select the best options you will suggest?

A. He can access the query ID through the sfqid attribute in the Cursor object.
1.cur = con.cursor()
2.cur.execute("SELECT * FROM snowtable")
3.print(cur.sfqid)
B. Using python connector, snowflake does not support queryID retrieval for both syn-chronous & asynchronous query.
C. When he used the Snowflake Connector for Python to execute a query, he can access the query ID through the pyqueryid attribute in the Cursor object.
D. He needs to query history views to get the queryID as best practices.

Answer: A

Explanation:
Explanation
Retrieving the Snowflake Query ID
A query ID identifies each query executed by Snowflake. When you use the Snowflake Connector for Python to execute a query, you can access the query ID through the sfqid attribute in the Cursor object:
1.# Retrieving a Snowflake Query ID
2.cur = con.cursor()
3.cur.execute("SELECT * FROM testtable")
4.print(cur.sfqid)

NEW QUESTION # 55
What are Invalid rules applicable when using stored procedure contains transaction?

A. If a transaction is started inside a stored procedure and is still active when the stored procedure finishes, then an error occurs, and the transaction is rolled back.
B. All Rules are Applicable.
C. You cannot start a transaction before calling the stored procedure, then complete the transaction inside the stored procedure.
D. You cannot start a transaction inside the stored procedure, then complete the transac-tion after returning from the procedure.
E. A transaction can be inside a stored procedure, or a stored procedure can be inside a transaction.
F. A transaction inside a stored procedure can include a call to another stored procedure that contains a transaction.

Answer: B

NEW QUESTION # 56
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