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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	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 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	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 (Q296-Q301):

NEW QUESTION # 296
A company stores employee data in Amazon Resdshift. A table names Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key.
Which queries will MOST increase the speed of query by using a compound sort key of the table?
(Choose two.)

A. Select *from Employee where Region ID='North America' and Role ID=50;
B. Select *from Employee where Department ID=20 and Region ID='North America';
C. Select *from Employee where Role ID=50;
D. Select *from Employee where Region ID='North America' and Department ID=20;
E. Select *from Employee where Region ID='North America';

Answer: A,D

Explanation:
To maximize the speed of queries by using the compound sort key (Region ID, Department ID, and Role ID) in the Employee table in Amazon Redshift, the queries should align with the order of the columns in the sort key.

NEW QUESTION # 297
A data engineer is writing a query to join two tables in Amazon Athena. The data engineer needs to choose the correct join order for the tables to optimize query performance. Which solution will meet these requirements?

A. Use table statistics to automatically determine the join order.
B. Specify the smaller table on the left side of the join and the larger table on the right side of the join.
C. Use AWS Glue to pre-process the tables before performing the join.
D. Specify the larger table on the left side of the join and the smaller table on the right side of the join.

Answer: B

Explanation:
Amazon Athena (based on Presto/Trino) performs joins more efficiently when the smaller table is placed on the left side because it is used as the build side of the join and can be loaded into memory. This minimizes data shuffling and improves performance when joining with a larger table on the right side.

NEW QUESTION # 298
A Data Engineer needs to know the details regarding the micro-partition layout for a table named invoice using a built-in function.
Which query will provide this information?

A. CALL SYSTEM$CLUSTERING_INFORMATION ('Invoice');
B. CALL $CLUSTERINS_INFORMATION('Invoice');
C. SELECT SYSTEM$CLUSTERING_INTFORMATICII ('Invoice' ) ;
D. SELECT $CLUSTERXNG_INFQRMATION ('Invoice')'

Answer: C

Explanation:
Explanation
The query that will provide information about the micro-partition layout for a table named invoice using a built-in function is SELECT SYSTEM$CLUSTERING_INFORMATION('Invoice');. The SYSTEM$CLUSTERING_INFORMATION function returns information about the clustering status of a table, such as the clustering key, the clustering depth, the clustering ratio, the partition count, etc. The function takes one argument: the table name in a qualified or unqualified form. In this case, the table name is Invoice and it is unqualified, which means that it will use the current database and schema as the context. The other options are incorrect because they do not use a valid built-in function for providing information about the micro-partition layout for a table. Option B is incorrect because it uses $CLUSTERING_INFORMATION instead of SYSTEM$CLUSTERING_INFORMATION, which is not a valid function name. Option C is incorrect because it uses CALL instead of SELECT, which is not a valid way to invoke a table function.
Option D is incorrect because it uses CALL instead of SELECT and $CLUSTERING_INFORMATION instead of SYSTEM$CLUSTERING_INFORMATION, which are both invalid.

NEW QUESTION # 299
A company needs to implement a data mesh architecture in which domains for trading, risk, and compliance teams each have own their data. The teams need to share specific views with one another. The teams have over 1,000 tables across 50 databases in AWS Glue Data Catalog. All three teams use Amazon Athena to perform on-demand analysis. The teams use Amazon Redshift to generate complex reports. The compliance team must audit all data access. Access to personally identifiable information (PII) data must be restricted.
The company requires a scalable solution to meet the team requirements. The solution must provide the ability to perform analysis across team domains.
Which solution will meet these requirements?

A. Use AWS Glue Data Catalog views to perform analysis. Use AWS CloudTrail logs to audit data access. Use AWS Lake Formation to manage access permissions. Use security definer views to mask PII.
B. Use AWS Lake Formation to set up cross-domain access to tables. Set up fine-grained access controls.
C. Create materialized views and enable Amazon Redshift datashares for each domain. Configure cross-domain access policies.
D. Create views in Athena for on-demand analysis. Use the Athena views in Amazon Redshift to perform cross-domain analytics. Use AWS CloudTrail to audit data access. Use AWS Lake Formation to establish fine-grained access control.

Answer: B

Explanation:
AWS Lake Formation provides centralized, fine-grained governance (LF-Tags, column/row-level permissions) across domains, integrates with Athena and Redshift for cross-domain analytics, and supports comprehensive auditing via Lake Formation access logs and CloudTrail meeting scalability, PII restriction, and audit requirements.

NEW QUESTION # 300
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 schedule regular data exports for offline model training.
B. Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.
C. Use Amazon Redshift as a file storage system to archive old inventory management reports.
D. Use SQL to invoke a remote SageMaker endpoint for prediction.
E. Use Amazon Redshift ML to generate inventory recommendations.

Answer: D,E

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 # 301
......

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