Valid DEA-C01 Exam Format | DEA-C01 Exam Syllabus

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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 | - 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 3 | - 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 4 | - 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 5 | - 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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DEA-C01 Exam Syllabus & DEA-C01 Guaranteed Success
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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q234-Q239):
NEW QUESTION # 234
A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?
- A. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company's access policies.
- B. Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.
- C. Load the data into Amazon Redshift. Create a view for each country. Create separate IAM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.
- D. Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.
Answer: A
Explanation:
https://docs.aws.amazon.com/lake-formation/latest/dg/register-data-lake.html
https://docs.aws.amazon.com/lake-formation/latest/dg/registration-role.html
NEW QUESTION # 235
A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create AWS Lambda functions to evaluate data quality rules. Use AWS Step Functions to orchestrate a workflow that publishes notifications when the data fails to meet data quality rules.
- B. Create an AWS Lambda function to check data quality and to raise exceptions in the code. Run the function when data is added to the S3 bucket. Create an Amazon CloudWatch alarm for exceptions in the code.
- C. Create an Amazon EMR provisioned cluster. Add a Python open source data quality package to the EMR cluster. Use the Python package to write code for data quality rules and to copy the data from the S3 bucket to the EMR cluster. Copy the data from the S3 bucket to the EMR cluster. Run the data quality rules.
- D. Create an AWS Glue DataBrew project for the data in the S3 bucket. Create a ruleset for the data quality rules. Create a profile job to run the data quality rules. Use Amazon EventBridge to run the profile job when data is added to the S3 bucket.
Answer: D
Explanation:
AWS Glue DataBrew is a fully managed, serverless service with built-in data quality rulesets and profiling jobs that run directly on S3 data. Triggering the profile job via EventBridge on object creation applies rules with minimal code and management overhead.
NEW QUESTION # 236
Select the correct usage statements with regards to SQL UDF?
- A. Scalar functions (UDFs) have a limit of 500 input arguments.
- B. You can include only one query expression.
- C. The body of a UDF cannot contain DDL statements or any DML statement other than SELECT.
- D. When using a query expression in a SQL UDF, do not include a semicolon within the UDF body to terminate the query expression.
- E. All of above are correct.
Answer: E
NEW QUESTION # 237
A data engineer is designing a log table for an application that requires continuous ingestion. The application must provide dependable API-based access to specific records from other applications. The application must handle more than 4,000 concurrent write operations and 6,500 read operations every second. Which solution will meet these requirements?
- A. Create an Amazon DynamoDB table that has provisioned capacity to meet the application's capacity needs. Use the DynamoDB table to perform all read and write operations by using DynamoDB APIs.
- B. Create an Amazon Redshift table with the EVEN distribution style. Use the Amazon Redshift Java Database Connectivity (JDBC) connector to establish a database connection. Use the database connection to perform all read and write operations.
- C. Create an Amazon Redshift table with the KEY distribution style. Use the Amazon Redshift Data API to perform all read and write operations.
- D. Store the log files in an Amazon S3 Standard bucket. Register the schema in AWS Glue Data Catalog. Create an external Redshift table that points to the AWS Glue schema. Use the table to perform Amazon Redshift Spectrum read operations.
Answer: A
Explanation:
Amazon DynamoDB is the best fit because it is designed for dependable API-based access with very high request rates and can be provisioned to support the required thousands of concurrent write and read operations per second. It also supports direct item retrieval and query operations through DynamoDB APIs, which matches the requirement for other applications to access specific records reliably. Amazon Redshift is designed for analytics workloads rather than high- throughput transactional API reads and writes.
Reference:
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/read-write- operations.html
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Query.html
NEW QUESTION # 238
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Choose two.)
- A. There was a change in the number of shards, record processors, or both.
- B. The producer experienced network-related timeouts.
- C. The AggregationEnabled configuration property was set to true.
- D. The stream's value for the IteratorAgeMilliseconds metric was too high.
- E. The max_records configuration property was set to a number that was too high.
Answer: A,B
NEW QUESTION # 239
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