DEA-C01 Reliable Study Questions | DEA-C01 Actualtest

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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.
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
  • 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 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
  • 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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q117-Q122):

NEW QUESTION # 117
A retail company stores data from a product lifecycle management (PLM) application in an on- premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on- premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?

Answer: B

Explanation:
https://aws.amazon.com/ko/blogs/apn/change-data-capture-from-on-premises-sql-server-to- amazon-redshift-target/


NEW QUESTION # 118
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.
The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job.
Which rule will meet these requirements?

Answer: A

Explanation:
The ReferentialIntegrity rule checks that every (city, state) pair in the primary dataset has a matching (ref_city, ref_state) pair in the reference dataset, and setting the threshold to 1.0 enforces a 100% match rate. This directly validates exact correspondence of those columns without extra overhead.


NEW QUESTION # 119
A company runs an extract, transform, and load (ETL) job in AWS Glue. The job processes personally identifiable information (PII) data and writes logs to an Amazon CloudWatch Logs log group. A data engineer needs to mask PII data in the CloudWatch logs group. Which solution will meet these requirements?

Answer: B

Explanation:
A CloudWatch Logs data protection policy is specifically designed to detect, audit, and mask sensitive data such as PII in log events that are ingested into a log group. Attaching the policy directly to the CloudWatch log group meets the requirement to mask PII in the logs without changing the ETL logic itself.
Reference:
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/mask-sensitive-log-data.html
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/cloudwatch-logs-data-protection- policies.html


NEW QUESTION # 120
Given the table sales which has a clustering key of column CLOSED_DATE which table function will return the average clustering depth for the SALES_REPRESENTATIVEcolumn for the North American region?

Answer: C

Explanation:
Explanation
The table function SYSTEM$CLUSTERING_DEPTH returns the average clustering depth for a specified column or set of columns in a table. The function takes two arguments: the table name and the column name(s). In this case, the table name is sales and the column name is SALES_REPRESENTATIVE. The function also supports a WHERE clause to filter the rows for which the clustering depth is calculated. In this case, the WHERE clause is REGION = 'North America'. Therefore, the function call in Option B will return the desired result.


NEW QUESTION # 121
A company is uploading log files from on-premises servers to an Amazon S3 bucket. The company needs to validate that the logs from the on-premises server are the same as the logs that are stored in the S3 bucket. Which solution will meet this requirement?

Answer: A

Explanation:
Computing a checksum during upload and storing it with the object provides an integrity value that can be compared later to confirm the uploaded object matches the original log file. Using an SDK-managed CRC32 checksum gives a straightforward, automated validation approach without additional processing infrastructure.


NEW QUESTION # 122
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