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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	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 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 (Q212-Q217):

NEW QUESTION # 212
A retail company stores transactions, store locations, and customer information tables in four reserved ra3.4xlarge Amazon Redshift cluster nodes. All three tables use even table distribution.
The company updates the store location table only once or twice every few years.
A data engineer notices that Redshift queues are slowing down because the whole store location table is constantly being broadcast to all four compute nodes for most queries. The data engineer wants to speed up the query performance by minimizing the broadcasting of the store location table.
Which solution will meet these requirements in the MOST cost-effective way?

A. Change the distribution style of the store location table to KEY distribution based on the column that has the highest dimension.
B. Upgrade the Redshift reserved node to a larger instance size in the same instance family.
C. Change the distribution style of the store location table from EVEN distribution to ALL distribution.
D. Add a join column named store_id into the sort key for all the tables.

Answer: C

Explanation:
Changing the distribution style of the store location table to ALL distribution (A) is the most cost- effective solution. It directly addresses the issue of broadcasting by ensuring the entire table is available on each node, significantly improving join performance without incurring substantial additional costs.

NEW QUESTION # 213
A research company stores data in an Amazon Redshift cluster. The company needs to share data between departments and maintain regulatory compliance. The company needs a solution that gives researchers access to only the records from their own departments and does not create multiple dataset copies. The solution must also ensure that personally identifiable information (PII) is protected from unauthorized access.
Which solution will meet these requirements?

A. Create separate schemas for each department with appropriate views that filter data. Grant each department access to only their respective schema.
B. Use row-level security policies with multi-condition SQL predicates. Attach the security policies to the data tables. Grant each department's role access to the appropriate policies.
C. Implement row-level security policies with basic SQL filters based on department. Attach the security policies to the data tables. Grant EXPLAIN RLS permission to authorized researchers.
D. Create a datashare in Amazon Redshift for each department. Use cross-Region data sharing to distribute copies of the entire dataset to each department's Amazon Redshift cluster.

Answer: B

Explanation:
Row-level security policies allow fine-grained access control directly on tables without duplicating data. Using multi-condition SQL predicates enables filtering by department while also enforcing conditions to protect sensitive data such as PII. Assigning these policies to roles ensures each department can only access its permitted records while maintaining a single dataset.

NEW QUESTION # 214
A company stores 10 to 15 TB of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a one-time query engine.
The company wants to transform the data to optimize query runtime and storage costs.
Which file format and compression solution will meet these requirements for Athena queries?

A. Apache Parquet format compressed with Snappy
B. JSON format compressed with bzip2
C. Apache Avro format compressed with LZO
D. .csv format compressed with zip

Answer: A

Explanation:
Parquet provides efficient columnar storage, enabling Athena to read only the necessary data for queries, which reduces scan times and speeds up query performance.
Snappy compression offers a good balance between compression speed and efficiency, reducing storage costs without significantly impacting query times.

NEW QUESTION # 215
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 DataBrew to profile the data. Configure data quality rules based on the data quality results from the profiling.
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 jobs to implement data quality rules that use open source data quality frameworks.

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 # 216
A company is using Apache Hive on Amazon EMR for data warehousing services. Multiple AWS accounts will access data. Recently when one of the nodes was terminated, metastore information for the table was lost. Project team is looking for an efficient option to store the metastore information persistently on external storage.
Which storage service can be used for this purpose?

A. Use the Amazon S3 as the external metastore for Apache Hive.
B. Use the Amazon Aurora as the external metastore for Apache Hive.
C. Use the Amazon RDS as the external metastore for Apache Hive.
D. Use the AWS Glue Data Catalog as the external metastore for Apache Hive.

Answer: D

Explanation:
Metastore consists of a description of the table and the underlying data on which it is built, which includes the partition names, data types etc. Hive stores the metastore information in the MySQL database on the primary node file system. When the primary node is terminated, this metastore information stored in ephemeral storage is lost. To store metastore information persistently, there are two options in AWS, Use AWS Glue Data Catalog Use Amazon RDS or Amazon Aurora.
When a metastore is shared between multiple AWS accounts, using AWS Glue Data Catalog for persistent storage of metastore is a better option.
Reference:
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-metastore-external-hive.html

NEW QUESTION # 217
......

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