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| Topic | Details |
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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 | - 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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| 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 | - 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 (Q314-Q319):
NEW QUESTION # 314
You have been tasked with migrating an on-premises MySQL database to Amazon Aurora PostgreSQL using AWS Database Migration Service (DMS). The stakeholder emphasizes that the source database must remain fully operational during the migration process.
Which of the following statements about DMS is accurate with respect to this scenario?
- A. AWS DMS can convert the MySQL database schema directly to PostgreSQL without any manual intervention.
- B. AWS DMS requires the source MySQL database to be version 5.7 or higher for migrating to Amazon Aurora PostgreSQL.
- C. AWS DMS supports both full-load and continuous replication, allowing the source MySQL database to remain operational during migration.
- D. AWS DMS only supports full-load migrations, which would require downtime for the source database.
- E. When using DMS, the target Amazon Aurora PostgreSQL instance cannot be accessed or queried until the migration is complete.
Answer: C
NEW QUESTION # 315
What is a characteristic of the use of external tokenization?
- A. External tokenization allows (he preservation of analytical values after de-identification
- B. Secure data sharing can be used with external tokenization
- C. Pre-loading of unmasked data is supported with external tokenization
- D. External tokenization cannot be used with database replication
Answer: A
Explanation:
Explanation
External tokenization is a feature in Snowflake that allows users to replace sensitive data values with tokens that are generated and managed by an external service. External tokenization allows the preservation of analytical values after de-identification, such as preserving the format, length, or range of the original values.
This way, users can perform analytics on the tokenized data without compromising the security or privacy of the sensitive data.
NEW QUESTION # 316
A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company's marketing, claims, and analytics teams need to be able to access the customer data.
The marketing team should have access to obfuscated claim information but should have full access to customer contact information. The claims team should have access to customer information for each claim that the team processes. The analytics team should have access only to obfuscated PII data.
Which solution will enforce these data access requirements with the LEAST administrative overhead?
- A. Create a separate Redshift cluster for each team. Load only the required data for each team.
Restrict access to clusters based on the teams. - B. Move the customer data to an Amazon S3 bucket. Use AWS Lake Formation to create a data lake. Use fine-grained security capabilities to grant each team appropriate permissions to access the data.
- C. Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.
- D. Create a separate Amazon Redshift database role for each team. Define masking policies that apply for each team separately. Attach appropriate masking policies to each team role.
Answer: D
NEW QUESTION # 317
You're working as a data engineer for a financial services company processing real-time stock market data feeds. This data includes stock prices, trading volumes, and market news. You need a robust infrastructure to capture, process, and analyze this real-time data stream to make informed investment decisions. Downtime or data loss in your streaming pipeline can have significant consequences. Which option ensures high availability and fault tolerance with minimal operational overhead?
- A. Use Amazon MSK to create a managed Kafka cluster.
- B. Set up a Kafka cluster on EC2 instances across multiple availability zones.
- C. Implement a custom Kafka cluster on ECS with Fargate.
- D. Use AWS Lambda to process streaming data and write it to an S3 bucket.
Answer: A
Explanation:
Amazon MSK (Managed Streaming for Apache Kafka) provides a fully managed Kafka service that ensures high availability and fault tolerance with minimal operational overhead.
NEW QUESTION # 318
A company needs a solution to automatically ingest data from an Amazon S3 bucket to an Amazon Redshift table every day. The company must obfuscate sensitive data in Amazon Redshift. Users must not be able to view the sensitive data as plaintext.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Enable Amazon Macie for the company's AWS account. Configure a sensitive data discovery job for the S3 bucket. Configure an AWS Glue job to load the data into Amazon Redshift.
- B. Configure an AWS Glue job to load the data into Amazon Redshift. Use Amazon Redshift to detect the sensitive data, create masking policies, and apply dynamic masking.
- C. Enable Amazon Macie for the company's AWS account. Configure an AWS Glue job to flag and obfuscate data columns based on the Macie analysis. Load the data into Amazon Redshift.
- D. Configure an AWS Glue job to read the data from the S3 bucket, detect and obfuscate sensitive data, and load the data into Amazon Redshift.
Answer: B
Explanation:
Schedule an AWS Glue job to load S3 data into Amazon Redshift, then use Redshift's built-in sensitive data discovery and dynamic data masking to identify sensitive columns and enforce masking at query time. This meets the daily ingest and non-plaintext visibility requirements with the least operational overhead, since masking is managed natively in Redshift without custom obfuscation pipelines or Macie-based preprocessing.
NEW QUESTION # 319
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