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DEA-C01 Ressourcen Prüfung - DEA-C01 Prüfungsguide & DEA-C01 Beste Fragen

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DEA-C01 Fragen Beantworten - DEA-C01 Examengine

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Snowflake DEA-C01 Prüfungsplan:

Thema
Einzelheiten

Thema 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.

Thema 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.

Thema 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.

Thema 4

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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam DEA-C01 Prüfungsfragen mit Lösungen (Q73-Q78):

73. Frage
Robert, A Data Engineer, found that Pipe become stale as it was paused for longer than the limited retention period for event messages received for the pipe (14 days by default) & also the previous pipe owner transfers the ownership of this pipe to Robert role while the pipe was paused. How Robert in this case, Resume this stale pipe?

A. He can apply System function SYSTEM$PIPE_STALE_RESUME with ALTER PIPE statement.
B. select sys-tem$pipe_force_resume('mydb.myschema.stalepipe','staleness_check_override, ownership_transfer_check_override');
C. ALTER PIPES ... RESUME statement will resume the pipe.
D. Robert can use SYSTEM$PIPE_FORCE_RESUME function to resume this stale pipe.
E. PIPE needs to recreate in this scenario, as pipe already past 14 days of period & stale.

Antwort: B

Begründung:
Explanation
When a pipe is paused, event messages received for the pipe enter a limited retention period. The period is 14 days by default. If a pipe is paused for longer than 14 days, it is considered stale.
To resume a stale pipe, a qualified role must call the SYSTEM$PIPE_FORCE_RESUME function and input the STALENESS_CHECK_OVERRIDE argument. This argument indicates an under-standing that the role is resuming a stale pipe.
For example, resume the stale stalepipe1 pipe in the mydb.myschema database and schema:
SELECT SYS-TEM$PIPE_FORCE_RESUME('mydb.myschema.stalepipe1','staleness_check_override'); While the stale pipe was paused, if ownership of the pipe was transferred to another role, then re-suming the pipe requires the additional OWNERSHIP_TRANSFER_CHECK_OVERRIDE argu-ment. For example, resume the stale stalepipe2 pipe in the mydb.myschema database and schema, which transferred to a new role:
SELECT SYS-TEM$PIPE_FORCE_RESUME('mydb.myschema.stalepipe1','staleness_check_override, own-ership_transfer_check_override');

74. Frage
A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?

A. Use an AWS Glue workflow to refresh the materialized views.
B. Use Apache Airflow to refresh the materialized views.
C. Use the query editor v2 in Amazon Redshift to refresh the materialized views.
D. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.

Antwort: D

Begründung:
AWS Lambda allows running code in response to triggers without needing to provision or manage servers. However, creating a UDF within Amazon Redshift to call a Lambda function for this purpose involves writing custom code and managing permissions between Lambda and Redshift.

75. Frage
A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB.
The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)

A. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
B. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
C. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes.
Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
D. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.
E. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

Antwort: C,E

76. Frage
A company is building a new application that ingests CSV files into Amazon Redshift. The company has developed the frontend for the application.
The files are stored in an Amazon S3 bucket. Files are no larger than 5 MB.
A data engineer is developing the extract, transform, and load (ETL) pipeline for the CSV files.
The data engineer configured a Redshift cluster and an AWS Lambda function that copies the data out of the files into the Redshift cluster.
Which additional steps should the data engineer perform to meet these requirements?

A. Configure the $3 bucket to send S3 event notifications to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the Lambda function to process the queue.
B. Configure an Amazon EventBridge rule that matches S3 new object created events. Set an Amazon Simple Queue Service (Amazon SQS) queue as the target of the rule. Configure the Lambda function to process the queue.
C. Configure AWS Database Migration Service (AWS DMS) to stream new S3 objects to a data stream in Amazon Kinesis Data Streams. Set the Lambda function as the target of the data stream.
D. Configure the bucket to send S3 event notifications to Amazon EventBridge. Configure an EventBridge rule that matches S3 new object created events. Set the Lambda function as the target.

Antwort: D

Begründung:
By sending S3 "Object Created" events to EventBridge and matching those events with a rule that invokes your Lambda function, you trigger your ETL whenever a new CSV lands in S3, without extra polling or queue management. This direct, event-driven pattern keeps operational overhead to a minimum.

77. 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 a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
B. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail 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 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.

Antwort: C

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

78. Frage
......

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