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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	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	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 (Q150-Q155):

NEW QUESTION # 150
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 Monitor the files using Metadata maintained by Snowflake i.e. file-name,last_modified date etc.
B. She can Monitor the status of each COPY INTO <table> command on the History tab page of the classic web interface.
C. She can use the DATA_LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.
D. Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days.
E. She can use the DATA_VALIDATE function to validate the data files She have loaded and can retrieve any errors encountered during the load.

Answer: A,B,D

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 # 151
What kind of Snowflake integration is required when defining an external function in Snowflake?

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

Answer: C

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 # 152
A company needs to implement a workflow to process transactions. Each transaction goes through multiple levels of validation. Each validation level depends on the preceding validation level.
The workflow must either process or reject each transaction within 24-hours. The workflow must run for less than 24 hours total.
Which solution will meet these requirements with the LEAST operational cost?

A. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to implement the workflow.
B. Create an express workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
C. Use AWS Lambda functions to implement the workflow. Use Amazon EventBridge to invoke the validation steps.
D. Create a standard workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.

Answer: D

Explanation:
Standard Step Functions support long-running, sequential workflows and the Wait for Callback pattern without charging for idle wait time, minimizing cost while ensuring completion within 24 hours. Express would bill for long waits, Lambda+EventBridge adds complexity and limits, and MWAA has higher operational overhead and cost.

NEW QUESTION # 153
A company uses an Amazon S3 Standard bucket to maintain a self-managed transactional data lake that uses Apache Iceberg tables. The data lake ingests data both in real time and in batches.
Users report slow performance for real-time tables. A data engineer reviews the real-time tables and notices that the tables are made up of many small data files The data engineer must improve the performance of the real-time tables.
Which solution will meet this requirement?

A. Archive historic snapshots.
B. Delete S3 objects that are not linked from the Iceberg table.
C. Apply compaction.
D. Expire historic snapshots.

Answer: C

Explanation:
Compaction merges many small data files into fewer, larger files, which reduces file count and metadata overhead in Apache Iceberg tables, directly improving query performance for real-time workloads.

NEW QUESTION # 154
A data engineer is building a serverless. multi-step extract, transform, and load (ETL) pipeline.
The pipeline extracts data from an Amazon S3 data lake and transforms the data by using AWS Glue ETL jobs. The pipeline then loads the results into an Amazon Redshift database. The data engineer needs to orchestrate the serverless ETL workflow. Which solutions will meet these requirements? (Choose two.)

A. Implement the workflow by using AWS Step Functions. Configure Step Functions to coordinate the AWS Glue ETL jobs and handle error conditions with automatic retries.
B. Use AWS Glue workflows to create a graph of the ETL tasks that visually represents the dependencies between jobs and the job triggers.
C. Build an orchestration solution by using AWS CodePipeline to coordinate the ETL pipeline and infrastructure changes based on the dependencies.
D. Provision an always on Amazon EC2 instance. Create a cron job that invokes the AWS Glue ETL jobs in sequence based on a predefined schedule
E. Use Amazon EventBridge rules to invoke the AWS Glue ETL jobs based on S3 object creation events. Configure the rules to chain the AWS Glue ETL jobs in sequence and handle complex job dependencies.

Answer: A,B

Explanation:
AWS Step Functions is a serverless orchestration service that can coordinate multi-step ETL workflows, invoke AWS Glue jobs, manage dependencies, and provide built-in retry and error- handling logic. This makes it a strong fit for orchestrating a serverless ETL pipeline end to end.
AWS Glue workflows are specifically designed to orchestrate AWS Glue jobs, crawlers, and triggers in a dependency-based ETL graph. They provide a native way to represent and manage task order and workflow execution for serverless Glue-based pipelines.

NEW QUESTION # 155
......

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