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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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.
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
  • 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 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 (Q156-Q161):

NEW QUESTION # 156
A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AWS Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Daily.csv in a second S3 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.
A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?

Answer: B

Explanation:
AWS Glue Data Quality rules can be integrated directly into the AWS Glue ETL pipeline to automatically check for missing or invalid data in the daily file. You can use Data Quality Definition Language (DQDL) to define rules that verify the completeness and validity of the data before the pipeline writes the output. This solution ensures that the previous day's file is only overwritten if the new data is complete and valid, minimizing manual intervention and operational effort.
While Lambda could be used, it requires additional development and integration outside of the existing Glue ETL pipeline. This adds complexity and operational overhead.
The "Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field" approach assumes that filling missing values with the most common ones is acceptable, but it might not always be the correct solution for all cases. The requirement specifies that the file should be validated and replaced only if it's complete and valid.
Using Athena to drop missing rows doesn't fully address the problem, as it could result in loss of data and still requires manual intervention to validate the file before copying it. Additionally, it introduces unnecessary steps compared to using built-in AWS Glue Data Quality capabilities.


NEW QUESTION # 157
A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?

Answer: B

Explanation:
AWS CloudTrail provides detailed logging of AWS API calls, including object-level events for Amazon S3. You can configure CloudTrail to track data events for the S3 bucket, which will log each access to the objects in the bucket, including the username of the IAM entity (user or role) that accessed the data. By forwarding these CloudTrail events to Amazon CloudWatch, you can set up alarms to trigger notifications when policy violations are detected, and include the violating user's identity.
AWS Config is primarily designed for monitoring resource configurations, not for tracking real- time access events like data access violations. It does not directly provide information about which user accessed the S3 objects.
CloudWatch metrics can monitor S3 storage or request metrics, but it does not provide detailed logging of object-level access, including the username of the violator. You need CloudTrail to get detailed event information.
While S3 server access logs can track access to objects, they lack real-time processing and do not provide the same level of detail as CloudTrail.
Additionally, processing access logs with CloudWatch metric filters adds more complexity compared to using CloudTrail, which is designed for this use case.


NEW QUESTION # 158
Which use case would be BEST suited for the search optimization service?

Answer: C

Explanation:
Explanation
The use case that would be best suited for the search optimization service is business users who need fast response times using highly selective filters. The search optimization service is a feature that enables faster queries on tables with high cardinality columns by creating inverted indexes on those columns. High cardinality columns are columns that have a large number of distinct values, such as customer IDs, product SKUs, or email addresses. Queries that use highly selective filters on high cardinality columns can benefit from the search optimization service because they can quickly locate the relevant rows without scanning the entire table. The other options are not best suited for the search optimization service. Option A is incorrect because analysts who need to perform aggregates over high cardinality columns will not benefit from the search optimization service, as they will still need to scan all the rows that match the filter criteria. Option C is incorrect because data scientists who seek specific JOIN statements with large volumes of data will not benefit from the search optimization service, as they will still need to perform join operations that may involve shuffling or sorting data across nodes. Option D is incorrect because data engineers who create clustered tables with frequent reads against clustering keys will not benefit from the search optimization service, as they already have an efficient way to organize and access data based on clustering keys.


NEW QUESTION # 159
A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.
The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.
Which combination of steps will meet this requirement with the LEAST operational overhead?
(Choose two.)

Answer: B,D


NEW QUESTION # 160
Which stages support external tables?

Answer: B

Explanation:
Explanation
External stages only from any region, and any cloud provider support external tables. External tables are virtual tables that can query data from files stored in external stages without loading them into Snowflake tables. External stages are references to locations outside of Snowflake, such as Amazon S3 buckets, Azure Blob Storage containers, or Google Cloud Storage buckets. External stages can be created from any region and any cloud provider, as long as they have a valid URL and credentials. The other options are incorrect because internal stages do notsupport external tables. Internal stages are locations within Snowflake that can store files for loading or unloading data. Internal stages can be user stages, table stages, or named stages.


NEW QUESTION # 161
......

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