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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q185-Q190):

NEW QUESTION # 185
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 AWS Glue Data Quality validations that include anomaly detection.
C. Use AWS Glue jobs to implement data quality validations that use SQL statements.
D. Use AWS Glue jobs to implement data quality rules that use open source data quality frameworks.

Answer: B

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 # 186
A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.
The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.
Which solution will MOST reduce the data processing time?

A. Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.
B. Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.
C. Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.
D. Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

Answer: C

NEW QUESTION # 187
Mark the correct statements about Cache?

A. Warehouse cache is dropped when the warehouse is suspended, which may result in slower initial performance for some queries after the warehouse is resumed.
B. Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
C. The size of the warehouse cache is determined by the compute resources in the ware-house.
D. For persisted query results of all sizes, the cache expires after 24 hours.
E. Materialized views are more flexible than, but typically slower than, cached results.

Answer: A,B,C,D,E

Explanation:
Explanation
How Does Warehouse Caching Impact Queries?
Each warehouse, when running, maintains a cache of table data accessed as queries are processed by the warehouse. This enables improved performance for subsequent queries if they are able to read from the cache instead of from the table(s) in the query. The size of the cache is determined by the compute resources in the warehouse (i.e. the larger the warehouse and, therefore, more compute re-sources in the warehouse), the larger the cache.
This cache is dropped when the warehouse is suspended, which may result in slower initial perfor-mance for some queries after the warehouse is resumed. As the resumed warehouse runs and pro-cesses more queries, the cache is rebuilt, and queries that are able to take advantage of the cache will experience improved performance.
Keep this in mind when deciding whether to suspend a warehouse or leave it running. In other words, consider the trade-off between saving credits by suspending a warehouse versus maintaining the cache of data from previous queries to help with performance.
Using Persisted Query Results
When a query is executed, the result is persisted (i.e. cached) for a period of time. At the end of the time period, the result is purged from the system.
Snowflake uses persisted query results to avoid re-generating results when nothing has changed (i.e. "retrieval optimization"). In addition, you can use persisted query results to post-process the results (e.g. layering a new query on top of the results already calculated).
For persisted query results of all sizes, the cache expires after 24 hours.
Both materialized views and cached query results provide query performance benefits:
Materialized views are more flexible than, but typically slower than, cached results.
Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
Regular views do not cache data, and therefore cannot improve performance by caching.

NEW QUESTION # 188
A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?

A. Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.
B. Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.
C. Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an IAM policy that uses the tags to apply appropriate permissions to the workgroup.
D. Create an IAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.

Answer: C

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/user-created-workgroups.html

NEW QUESTION # 189
A data engineer at a large company needs to create centralized datasets that are optimized for Amazon Redshift performance. The company has multiple downstream teams that use their own AWS accounts and dedicated Amazon Redshift clusters with RA3 nodes. All downstream teams need access to the centralized datasets. Which solution will provide immediate access to the datasets and maintain the current Amazon Redshift performance?

A. Set up an AWS DataSync job that automatically syncs the data between the Amazon Redshift producer clusters and the consumer clusters.
B. Set up Amazon Redshift data sharing between the Amazon Redshift producer clusters and the consumer clusters to provide access to the centralized datasets.
C. Create a daily extract, transform, and load (ETL) job to unload the data to an Amazon S3 staging area. Instruct the teams to copy the data into their Amazon Redshift clusters.
D. Copy the datasets to an Amazon S3 bucket by using the UNLOAD command. Register the table definitions in a dedicated AWS Glue Data Catalog schema. Share the schema with the other AWS accounts by using AWS Lake Formation. Use Amazon Redshift Spectrum to access the data.

Answer: B

Explanation:
Amazon Redshift data sharing allows multiple Redshift clusters (including across AWS accounts) to access centralized datasets in real time without copying or moving data. This provides immediate access while maintaining performance because queries run directly on the producer cluster's managed storage, eliminating duplication and ETL overhead.

NEW QUESTION # 190
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