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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	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 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 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 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 (Q141-Q146):

NEW QUESTION # 141
A media company uses software as a service (SaaS) applications to gather data by using third- party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?

A. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
B. AWS Glue Data Catalog
C. Amazon Kinesis
D. Amazon AppFlow

Answer: D

Explanation:
https://d1.awsstatic.com/solutions/guidance/architecture-diagrams/integrating-third-party-saas- data-using-amazon-appflow.pdf Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between Software as a Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks. It can store the raw data pulled from SaaS applications in Amazon S3, and integrates with AWS Glue Data Catalog to catalog and store metadata.

NEW QUESTION # 142
A company stores raw clickstream data in an Amazon S3 bucket. The company needs a solution to process the data every day by using complex PySpark transformations that rely on custom internal libraries. After the data is transformed, the company must store the data in Amazon Redshift for analytics. The solution must be highly scalable to handle large data workloads. Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon EMR to run PySpark jobs. Use bootstrap actions to install the custom libraries
B. Use AWS Glue Studio to build and schedule PySpark jobs. Configure an AWS Glue data connection that includes the custom libraries.
C. Use Amazon SageMaker Processing jobs to run PySpark code that uses native SageMaker libraries.
D. Use Amazon EC2 Auto Scaling groups with a custom AMI that contains the custom libraries to run a PySpark application.

Answer: B

Explanation:
AWS Glue is a fully managed, highly scalable serverless service for running PySpark ETL jobs, and it supports adding custom Python libraries to Glue jobs from Amazon S3 by using job parameters such as --additional-python-modules and --extra-py-files. AWS Glue can also write to Amazon Redshift, making it the best fit for daily large-scale PySpark transformations with the least operational overhead compared with managing EC2 instances or EMR clusters.
Reference:
https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-python-libraries.html
https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-connect-redshift- home.html

NEW QUESTION # 143
A company runs an extract, transform, and load (ETL) job in AWS Glue. The job processes personally identifiable information (PII) data and writes logs to an Amazon CloudWatch Logs log group. A data engineer needs to mask PII data in the CloudWatch logs group. Which solution will meet these requirements?

A. Configure a data protection policy. Attach the policy to the CloudWatch log group.
B. Attach an AWS Glue security configuration to the ETL job.
C. Call AWS Glue sensitive data detection APIs in the ETL job.
D. Run an Amazon Macie sensitive data discovery job.

Answer: A

Explanation:
A CloudWatch Logs data protection policy is specifically designed to detect, audit, and mask sensitive data such as PII in log events that are ingested into a log group. Attaching the policy directly to the CloudWatch log group meets the requirement to mask PII in the logs without changing the ETL logic itself.
Reference:
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/mask-sensitive-log-data.html
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/cloudwatch-logs-data-protection- policies.html

NEW QUESTION # 144
When processing large datasets using distributed computing frameworks, uneven distribution of data can lead to processing delays. What is this phenomenon commonly known as?

A. Data fragmentation
B. Data shuffling
C. Data partitioning
D. Data skew

Answer: D

NEW QUESTION # 145
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real- time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Choose two.)

A. Use Amazon Redshift ML to generate inventory recommendations.
B. Use Amazon Redshift as a file storage system to archive old inventory management reports.
C. Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.
D. Use SQL to invoke a remote SageMaker endpoint for prediction.
E. Use Amazon Redshift ML to schedule regular data exports for offline model training.

Answer: A,D

Explanation:
Amazon Redshift ML integrates machine learning (ML) directly into the Redshift environment, allowing you to build and use ML models with SQL commands. By leveraging Redshift ML, the company can make real-time inventory recommendations based on historical and current data directly within Redshift.
Redshift can invoke external services, such as a SageMaker real-time endpoint, using SQL queries. This allows the company to send real-time data from Redshift to SageMaker and receive predictions (e.g., inventory forecasting) in real time, meeting the need for real-time predictions.

NEW QUESTION # 146
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