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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 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	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 (Q362-Q367):

NEW QUESTION # 362
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Write a custom extract, transform, and load (ETL) job in Python. Use the DataFrame.drop_duplicates() function by importing the Pandas library to perform data deduplication.
B. Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
C. Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.
D. Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

Answer: C

NEW QUESTION # 363
A company is building a dashboard for thousands of Analysts. The dashboard presents the results of a few summary queries on tables that are regularly updated. The query conditions vary by tope according to what data each Analyst needs Responsiveness of the dashboard queries is a top priority, and the data cache should be preserved.
How should the Data Engineer configure the compute resources to support this dashboard?

A. Create a size XL virtual warehouse to support all the dashboard queries Monitor query runtimes to determine whether the virtual warehouse should be resized.
B. Assign queries to a multi-cluster virtual warehouse with economy auto-scaling Allow the system to automatically start and stop clusters according to demand.
C. Create a virtual warehouse for every 250 Analysts Monitor to determine how many of these virtual warehouses are being utilized at capacity.
D. Assign all queries to a multi-cluster virtual warehouse set to maximized mode Monitor to determine the smallest suitable number of clusters.

Answer: D

Explanation:
Explanation
This option is the best way to configure the compute resources to support this dashboard. By assigning all queries to a multi-cluster virtual warehouse set to maximized mode, the Data Engineer can ensure that there is enough compute capacity to handle thousands of concurrent queries from different analysts. A multi-cluster virtual warehouse can scale up or down by adding or removing clusters based on the load. A maximized scaling policy ensures that there is always at least one cluster running and that new clusters are added as soon as possible whenneeded. By monitoring the utilization and performance of the virtual warehouse, the Data Engineer can determine the smallest suitable number of clusters that can meet the responsiveness requirement and minimize costs.

NEW QUESTION # 364
A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.
The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.
B. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.
C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.
D. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.

Answer: B

NEW QUESTION # 365
A global finance company needs to implement near real-time cross-Region synchronization of trading data between trading centers in the us-east-1 Region, the eu-west-2 Region, and the ap- northeast-1 Region. The company must ensure that data is encrypted in transit. The solution must ensure data ordering and consistency and must support cross-Region disaster recovery.
The solution must provide data latency of less than 500 milliseconds. Which solution will meet these requirements with the LEAST operational effort?

A. Use Amazon Kinesis Data Streams to receive trading data from each AWS Region. Use Amazon Data Firehose to replicate data between Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in each Region. Configure AWS Key Management Service (AWS KMS) encryption and IAM roles to manage access.
B. Deploy Apache Kafka Mirror Maker 2.0 in each AWS Region. Set up custom replication policies to handle cross-Region data synchronization. Configure the SSL security protocol.
C. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) Replicator to establish fully interconnected replication relationships between MSK clusters in the three AWS Regions. Enable TLS encryption and IAM authentication. Set up cross-Region backup configurations.
D. Deploy Apache Kafka Connect in each AWS Region. Use custom-developed connectors to set up cross-Region data replication. Configure the SSL security protocol.

Answer: C

Explanation:
Amazon MSK Replicator is a fully managed service for replicating data across MSK clusters in different AWS Regions, which makes it the lowest-effort option for near real-time cross-Region synchronization and disaster recovery. It supports secure encrypted connections, preserves Kafka replication behavior needed for ordered streaming data, and is designed for multi-Region resilience without requiring the company to operate custom replication infrastructure.
Reference:
https://docs.aws.amazon.com/msk/latest/developerguide/msk-replicator.html
https://docs.aws.amazon.com/msk/latest/developerguide/msk-replicator-how-it-works.html

NEW QUESTION # 366
A Data Engineer enables a result cache at the session level with the following command:
ALTER SESSION SET USE CACHED RESULT = TRUE;
The Engineer then runs the following select query twice without delay:

The underlying table does not change between executions
What are the results of both runs?

A. The first and second run returned differentresults because the query uses *instead of an explicit column list
B. The first and second run returned different results because the query is evaluated each time it is run.
C. The first and second run returned the same results because sample is deterministic
D. The first and second run returned the same results, because the specific SEEDvalue was provided.

Answer: D

Explanation:
Explanation
The result cache is enabled at the session level, which means that repeated queries will return cached results if there is no change in the underlying data or session parameters. However, in this case, the result cache is not relevant because the query uses a specific SEED value for sampling, which makes it deterministic. Therefore, both runs will return the same results regardless of caching.

NEW QUESTION # 367
......

The Snowflake DEA-C01 Certification is a valuable credential in the modern world. The Snowflake DEA-C01 certification exam offers a great opportunity for beginners and experienced professionals to validate their skills and knowledge level. With the one certification SnowPro Advanced: Data Engineer Certification Exam exam you can upgrade your expertise and knowledge.

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