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Snowflake DEA-C01 Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 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.
Thema 3	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.
Thema 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.
Thema 5	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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DEA-C01 Prüfungsinformationen, DEA-C01 Zertifikatsfragen

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Snowflake SnowPro Advanced: Data Engineer Certification Exam DEA-C01 Prüfungsfragen mit Lösungen (Q172-Q177):

172. Frage
If the data retention period for a table is less than 90 days, and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale?

A. FALSE
B. TRUE

Antwort: A

Begründung:
Explanation
If the data retention period for a table is less than 14 days, and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale. The period is extended to the stream's offset, up to a maximum of 14 days by default, regardless of the Snowflake edition for your account. The maximum number of days for which Snowflake can extend the data retention period is determined by the MAX_DATA_EXTENSION_TIME_IN_DAYS parameter value. When the stream is consumed, the extended data retention period is reduced to the default period for the table.

173. Frage
A Data Engineer is investigating a query that is taking a long time to return The Query Profile shows the following:

What step should the Engineer take to increase the query performance?

A. Add additional virtual warehouses.
B. Change the order of the joins and start with smaller tables first
C. Rewrite the query using Common Table Expressions (CTEs)
D. increasethe size of the virtual warehouse.

Antwort: D

Begründung:
Explanation
The step that the Engineer should take to increase the query performance is to increase the size of the virtual warehouse. The Query Profile shows that most of the time was spent on local disk IO, which indicates that the query was reading a lot of data from disk rather than from cache. This could be due to a large amount of data being scanned or a low cache hit ratio. Increasing the size of the virtual warehouse will increase the amount of memory and cache available for the query, which could reduce the disk IO time and improve the query performance. The other options are not likely to increase the query performance significantly. Option A, adding additional virtual warehouses, will not help unless they are used in a multi-cluster warehouse configuration or for concurrent queries. Option C, rewriting the query using Common Table Expressions (CTEs), will not affect the amount of data scanned or cached by the query. Option D, changing the order of the joins and starting with smaller tables first, will not reduce the disk IO time unless it also reduces the amount of data scanned or cached by the query.

174. Frage
A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.
Which solution will meet these requirements with the LEAST effort?

A. Use scripts to scan data elements and to assign data classifications based on the format of the data.
B. Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.
C. Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog.
Use data stewards to inspect the data and update the data catalog with the data format.
D. Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.

Antwort: B

Begründung:
AWS Glue crawlers can automatically scan data in Amazon S3 buckets and Amazon RDS databases to build a data catalog. Glue crawlers also have classifiers that can automatically detect the format of the data (such as CSV, JSON, Parquet, etc.) and store this information as metadata in the Data Catalog. This solution automates the process of cataloging and format recognition, meeting the requirement with the least effort.
The "Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format." option requires manual inspection and updating of the data catalog by data stewards, which adds significant effort and is unnecessary since Glue crawlers can automatically detect the format.
Amazon Macie is primarily used for identifying sensitive data (e.g., PII), not for building a comprehensive data catalog or identifying data formats. It doesn't meet the requirement of cataloging storage format metadata.
Writing custom scripts to scan and classify data based on format is much more labor-intensive compared to using an automated Glue crawler, which handles this task with much less effort.

175. Frage
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

A. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
B. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
C. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.
D. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.

Antwort: C

Begründung:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api-monitoring-events.html

176. Frage
A global bank is using Amazon S3 to store all financial transactions for its customers. Data is stored in multiple S3 buckets. The Security Team wants to perform a one-time assessment to determine if any credit card data is stored in one of Amazon's S3 buckets. The team will be using Amazon Macie to make this discovery.
How sensitive data can be discovered?

A. Perform sensitive data discovery jobs with custom data identifiers.
B. Perform automated sensitive data discovery with custom data identifiers.
C. Perform sensitive data discovery jobs with managed data identifiers.
D. Perform automated sensitive data discovery with managed data identifiers.

Antwort: C

Begründung:
Amazon Macie can be used to automatically discover, log, and report sensitive data in Amazon S3 buckets. It can be discovered in any of the two ways, Automated Sensitive Data Discovery Job: This job can be used to get broader visibility of sensitive data stored in Amazon S3 buckets. It evaluates S3 buckets daily and identifies data.
Sensitive Data Discovery Job: It can be used to provide a deeper analysis of sensitive data from specific S3 buckets.
For discovering data in any of the above two options, further fine-tuning of the data discovery can be done in the following ways, Managed Data Identifiers: They use built-in criteria and techniques to identify known sensitive data, such as credit card and passport numbers.
Custom Data Identifier: This method uses custom data identifiers, such as regular expressions (regex), to identify any pattern or character matching the sensitive data.
Access List: This can be used to ignore specific names or characters that should not be considered sensitive data.
Reference:
https://docs.aws.amazon.com/macie/latest/user/data-classification.html

177. Frage
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