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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	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 3	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 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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
What kind of Snowflake integration is required when defining an external function in Snowflake?

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

Answer: D

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 # 30
A data engineer is building a new data pipeline that stores metadata in an Amazon DynamoDB table. The data engineer must ensure that all items that are older than a specified age are removed from the DynamoDB table daily.
Which solution will meet this requirement with the LEAST configuration effort?

A. Create an Amazon EventBridge rule that uses a daily cron expression to trigger an AWS Lambda function to delete items that are older than the specified age.
B. Create a DynamoDB stream that has an AWS Lambda function that reacts to data modifications.Configure the Lambda function to delete items that are older than the specified age.
C. Enable DynamoDB TTL on the DynamoDB table. Adjust the application source code to set the TTL attribute appropriately.
D. Add a lifecycle configuration to the DynamoDB table that deletes items that are older than the specified age.

Answer: C

Explanation:
DynamoDB Time to Live (TTL) is a fully managed feature that automatically expires and deletes items based on a timestamp attribute you define. By enabling TTL and setting the TTL attribute in the application code, items older than the specified age are removed without the need for custom scheduling or Lambda functions, offering the least configuration effort.

NEW QUESTION # 31
Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)

A. DataFraas.writeO
B. session.read.json{)
C. session.sql()
D. session,table()
E. session.builder()
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 # 32
Select the correct usage statements with regards to SQL UDF?

A. The body of a UDF cannot contain DDL statements or any DML statement other than SELECT.
B. You can include only one query expression.
C. When using a query expression in a SQL UDF, do not include a semicolon within the UDF body to terminate the query expression.
D. All of above are correct.
E. Scalar functions (UDFs) have a limit of 500 input arguments.

Answer: D

NEW QUESTION # 33
A company needs to build an extract, transform, and load (ETL) pipeline that has separate stages for batch data ingestion, transformation, and storage. The pipeline must store the transformed data in an Amazon S3 bucket. Each stage must automatically retry failures. The pipeline must provide visibility into the success or failure of individual stages.
Which solution will meet these requirements with the LEAST operational overhead?

A. Build an Amazon EventBridge based pipeline that invokes AWS Lambda functions to perform each stage.
B. Chain AWS Glue jobs that perform each stage together by using job triggers. Set the MaxRetries field to 0.
C. Deploy AWS Step Functions workflows to orchestrate AWS Lambda functions that ingest data.
Use AWS Glue jobs to transform the data and store the data in the S3 bucket.
D. Schedule Apache Airflow directed acyclic graphs (DAGs) on Amazon Manages Workflows tor Apache Airflow (Amazon MWAA) to orchestrate pipeline steps. Use Amazon Simple Queue Service (Amazon SQS) to ingest data. Use AWS Glue jobs to transform data and store the data in the S3 bucket.

Answer: C

Explanation:
AWS Step Functions provides managed orchestration with per-step retries, error handling, and execution history. Orchestrating Lambda for ingestion and AWS Glue jobs for transformation to S3 delivers a serverless, low-maintenance pipeline with built-in visibility into each stage's success or failure.

NEW QUESTION # 34
......

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