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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	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 3	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 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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q354-Q359):

NEW QUESTION # 354
A company runs a data platform on AWS. The data platform uses AWS Glue to provide a data catalog and to perform processing. The company notices quality issues in the data.
The company needs to implement data quality validations. The validations must include rules for known issues. The validations must have the ability to automatically detect unexpected data quality issues.
Which solution will meet these requirements with the LEAST operation overhead?

A. Use AWS Glue jobs to implement data quality rules that use open source data quality frameworks.
B. Use AWS Glue jobs to implement data quality validations that use SQL statements.
C. Use AWS Glue jobs to implement AWS Glue Data Quality validations that include anomaly detection.
D. Use AWS Glue DataBrew to profile the data. Configure data quality rules based on the data quality results from the profiling.

Answer: C

Explanation:
AWS Glue Data Quality provides native, managed data quality rules with built-in anomaly detection, allowing validation of known issues while automatically identifying unexpected data quality problems with minimal operational effort.

NEW QUESTION # 355
A media company wants to use Amazon OpenSearch Service to analyze rea-time data about popular musical artists and songs. The company expects to ingest millions of new data events every day. The new data events will arrive through an Amazon Kinesis data stream. The company must transform the data and then ingest the data into the OpenSearch Service domain.
Which method should the company use to ingest the data with the LEAST operational overhead?

A. Use the Kinesis Client Library (KCL) to transform the data and deliver the transformed data to OpenSearch Service.
B. Use an AWS Lambda function to call the Amazon Kinesis Agent to transform the data and deliver the transformed data OpenSearch Service.
C. Use Amazon Kinesis Data Firehose and an AWS Lambda function to transform the data and deliver the transformed data to OpenSearch Service.
D. Use a Logstash pipeline that has prebuilt filters to transform the data and deliver the transformed data to OpenSearch Service.

Answer: C

Explanation:
Amazon Kinesis Data Firehose is a fully managed service that reliably loads streaming data into data lakes, data stores and analytics services like OpenSearch Service. It can automatically scale to match the throughput of your data and requires no ongoing administration.

NEW QUESTION # 356
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. Create an express workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
B. Create a standard 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. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to implement the workflow.

Answer: B

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 # 357
In Which Data Modelling Technique, Data Engineer generally refer the terms Hubs & Satellites?

A. Star Schema
B. Data Hub
C. Data Vault
D. Snowflake Schema

Answer: C

Explanation:
Explanation
In Data Vault modelling, Hubs are entities of interest to the business.
They contain just a distinct list of business keys and metadata about when each key was first loaded and from where.
In Data Vault modelling, Satellites connect to Hubs or Links. They are Point in Time: so we can ask and answer the question, "what did we know when?" Satellites contain data about their parent Hub or Link and metadata about when the data was load-ed, from where, and a business effectivity date.

NEW QUESTION # 358
Select the incorrect statements regarding Clustering depth?

A. It helps Monitoring the clustering "health" of a large table, particularly over time as DML is performed on the table.
B. The clustering depth for a populated table measures the average depth (1 or greater) of the overlapping micro-partitions for specified columns in a table. The smaller the aver-age depth, the better clustered the table is with regards to the specified columns.
C. Clustering depth can be used for determining whether a large table would benefit from explicitly defining a clustering key.
D. A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 1.
(Correct)

Answer: D

Explanation:
Explanation
A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 0.

NEW QUESTION # 359
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