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| 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.
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| 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.
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| 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.
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| 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.
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| Topic 5 | - 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.
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>> New Snowflake DEA-C01 Test Questions <<
DEA-C01 Exam Preparation: SnowPro Advanced: Data Engineer Certification Exam & DEA-C01 Best Questions
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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q48-Q53):
NEW QUESTION # 48
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)
- A. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
- B. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.
- C. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
- D. Use an S3 bucket that is in the same account that uses Athena to query the data.
- E. Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
Answer: B,C
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html
NEW QUESTION # 49
A company processes 500 GB of audience and advertising data daily, storing CSV files in Amazon S3 with schemas registered in AWS Glue Data Catalog. They need to convert these files to Apache Parquet format and store them in an S3 bucket.
The solution requires a long-running workflow with 15 GiB memory capacity to process the data concurrently, followed by a correlation process that begins only after the first two processes complete.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use AWS Step Functions to orchestrate a workflow that uses multiple AWS Lambda functions.Ensure that the third process starts after the first two processes have finished.
- B. Use Amazon EMR to run each process in the workflow. Create an Amazon Simple Queue Service (Amazon SQS) queue to handle messages that indicate the completion of the first two processes. Configure an AWS Lambda function to process the SQS queue by running the third process.
- C. Use AWS Glue workflows to run the first two processes in parallel. Ensure that the third process starts after the first two processes have finished.
- D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the workflow by using AWS Glue. Configure AWS Glue to begin the third process after the first two processes have finished.
Answer: C
Explanation:
AWS Glue workflows natively orchestrate multiple Glue jobs in parallel and support dependency triggers so the third job starts only after the first two finish. Glue jobs provide the required ~15 GiB memory capacity and easily convert CSV to Parquet using the Glue Data Catalog, delivering a fully managed, low-operations solution.
NEW QUESTION # 50
Snowflake supports using key pair authentication for enhanced authentication security as an alterna-tive to basic authentication (i.e. username and password). Select the list of SnowFlake Clients sup-port the same?
[Select All that Apply]
- A. SnowFlake Connector for Spark
- B. SnowCD
- C. Node.js
- D. SnowSQL
- E. Go Driver
Answer: A,C,D,E
NEW QUESTION # 51
A company adds new data to a large CSV file in an Amazon S3 bucket every day. The file contains company sales data from the previous 5 years. The file currently includes more than
5,000 rows. The CSV file structure is shown below with sample data:

The company needs to use Amazon Athena to run queries on the CSV file to fetch data from a specific time period.
Which solution will meet this requirement MOST cost-effectively?
- A. Create an Amazon RDS database. Create a table named SALES that matches the schema of the CSV file. Create an index on the SALE_DATE column. Create an AWS Lambda function to load the CSV data into the RDS database. Use S3 Event Notifications to invoke the Lambda function.
- B. Use prefixes to partition the data in the S3 bucket. Use the SALE_DATE column to create a partition for each day. Catalog the data in AWS Glue and ensure that the partitions are added.
Update the Athena queries to use the new partitions. - C. Launch an Amazon EMR cluster. Specify AWS Glue Data Catalog as the default Apache Hive metastore. Use Presto in Trino to run queries on the data.
- D. Write an Apache Spark script to convert the CSV data to JSON format. Create an AWS Glue job to run the script every day. Catalog the JSON data in AWS Glue. Run the Athena queries on the JSON data.
Answer: B
Explanation:
Partitioning the S3 data by SALE_DATE (daily prefixes) lets Athena prune non-matching partitions so it scans only the requested time range, minimizing bytes scanned and cost while working directly on the existing CSVs. Cataloging the partitions in AWS Glue enables efficient querying with minimal operational overhead.
NEW QUESTION # 52
A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.
The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.
Which solution will meet these requirements MOST cost-effectively?
- A. Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company's data catalog.
- B. Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.
- C. Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company's data catalog.
- D. Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.
Answer: B
Explanation:
https://aws.amazon.com/blogs/big-data/migrate-and-deploy-your-apache-hive-metastore-on- amazon-emr/ Migrating the Hive metastore into Amazon EMR and using AWS Glue Data Catalog as an external catalog provides a balance between leveraging the scalable and managed services of AWS (like EMR and Glue Data Catalog) and ensuring a smooth transition from the on-premises setup. This approach leverages the serverless nature of AWS Glue Data Catalog, minimizing operational overhead and potentially reducing costs compared to managing database servers.
NEW QUESTION # 53
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The SnowPro Advanced: Data Engineer Certification Exam (DEA-C01) practice test questions are customizable which means that the customers can customize the time and DEA-C01 exam questions types according to their needs. These Snowflake DEA-C01 Practice Tests are based on real based examination scenarios which help the students practice under real DEA-C01 exam questions pressure and learn to control it.
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