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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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 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 (Q116-Q121):

NEW QUESTION # 116
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Which solution will meet this requirement with the LEAST coding effort?

A. Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.
B. Use AWS Glue DataBrew to read the files. Use the NEST_TO_ARRAY transformation to create the new column.
C. Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.
D. Use AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the new column.

Answer: D

NEW QUESTION # 117
A company receives marketing campaign data from a vendor. The company ingests the data into an Amazon S3 bucket every 40 to 60 minutes. The data is in CSV format. File sizes are between
100 KB and 300 KB.
A data engineer needs to set-up an extract, transform, and load (ETL) pipeline to upload the content of each file to Amazon Redshift.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Redshift Spectrum to query the S3 bucket. Configure an AWS Glue Crawler for the S3 bucket to update metadata in an AWS Glue Data Catalog.
B. Creates an AWS Database Migration Service (AWS DMS) task. Specify an appropriate data schema to migrate. Specify the appropriate type of migration to use.
C. Create an Amazon Data Firehose stream. Configure the stream to use an AWS Lambda function as a source to pull data from the S3 bucket. Set Amazon Redshift as the destination.
D. Create an AWS Lambda function that connects to Amazon Redshift and runs a COPY command.
Use Amazon EventBridge to invoke the Lambda function based on an Amazon S3 upload trigger.

Answer: D

Explanation:
Configuring your S3 bucket to emit "Object Created" events into EventBridge and using a rule to invoke a lightweight Lambda function lets you automatically run a Redshift COPY for each new CSV. This serverless, event-driven pattern requires no continuously running infrastructure and has minimal operational overhead.

NEW QUESTION # 118
A data engineer finished testing an Amazon Redshift stored procedure that processes and inserts data into a table that is not mission critical. The engineer wants to automatically run the stored procedure on a daily basis.
Which solution will meet this requirement in the MOST cost-effective way?

A. Create an AWS Lambda function to schedule a cron job to run the stored procedure.
B. Use query editor v2 to run the stored procedure on a schedule.
C. Schedule and run the stored procedure by using the Amazon Redshift Data API in an Amazon EC2 Spot Instance.
D. Schedule an AWS Glue Python shell job to run the stored procedure.

Answer: B

Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/query-editor-v2-schedule-query.html

NEW QUESTION # 119
Data Engineer is performing below steps in sequence while working on Stream s1 created on table t1.
Step 1: Begin transaction.
Step 2: Query stream s1 on table t1.
Step 3: Update rows in table t1.
Step 4: Query stream s1.
Step 5: Commit transaction.
Step 6: Begin transaction.
Step 7: Query stream s1.
Mark the Incorrect Operational statements:

A. For Step 5, If the stream was consumed in DML statements within the transaction, the stream position advances to the transaction start time.
B. For Step 7, Results do include table changes committed by Transaction 1.
C. For Step 4, Returns the CDC data records by streams with updated rows happened in the Step 3 because Streams works in Repeated committed mode in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
D. if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the be-ginning time of Transaction 2 and would not see any changes committed by Transac-tion 1.
E. For Step 2, The stream returns the change data capture records between the current position to the Transaction 1 start time. If the stream is used in a DML statement, the stream is then locked to avoid changes by concurrent transactions.

Answer: C

Explanation:
Explanation
Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream. This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise, it stays at the same position.
Within Transaction 1, all queries to stream s1 see the same set of records. DML changes to table t1 are recorded to the stream only when the transaction is committed.
In Transaction 2, queries to the stream see the changes recorded to the table in Transaction 1. Note that if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the beginning time of Transaction 2 and would not see any changes committed by Transaction 1.

NEW QUESTION # 120
You can execute zero, one, or more transactions inside a stored procedure?

A. TRUE
B. FALSE

Answer: A

NEW QUESTION # 121
......

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