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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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	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 4	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 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 (Q363-Q368):

NEW QUESTION # 363
While creating even Secure UDF, snowflake recommended to use randomized identifiers (e.g. gen-erated by UUID_STRING) instead of sequence-generated values?

A. FALSE
B. TRUE
(Correct)

Answer: B

NEW QUESTION # 364
A company adds new data to a large CSV file in an Amazon S3 bucket every day. The file contains company sales data from the previous 5 years. The file currently includes more than
5,000 rows. The CSV file structure is shown below with sample data:

The company needs to use Amazon Athena to run queries on the CSV file to fetch data from a specific time period.
Which solution will meet this requirement MOST cost-effectively?

A. Use prefixes to partition the data in the S3 bucket. Use the SALE_DATE column to create a partition for each day. Catalog the data in AWS Glue and ensure that the partitions are added.
Update the Athena queries to use the new partitions.
B. Create an Amazon RDS database. Create a table named SALES that matches the schema of the CSV file. Create an index on the SALE_DATE column. Create an AWS Lambda function to load the CSV data into the RDS database. Use S3 Event Notifications to invoke the Lambda function.
C. Write an Apache Spark script to convert the CSV data to JSON format. Create an AWS Glue job to run the script every day. Catalog the JSON data in AWS Glue. Run the Athena queries on the JSON data.
D. Launch an Amazon EMR cluster. Specify AWS Glue Data Catalog as the default Apache Hive metastore. Use Presto in Trino to run queries on the data.

Answer: A

Explanation:
Partitioning the S3 data by SALE_DATE (daily prefixes) lets Athena prune non-matching partitions so it scans only the requested time range, minimizing bytes scanned and cost while working directly on the existing CSVs. Cataloging the partitions in AWS Glue enables efficient querying with minimal operational overhead.

NEW QUESTION # 365
A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team. The compliance team must have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

A. Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.
B. Create an Amazon Redshift managed VPC endpoint in the marketing team's account. Grant the marketing team access to the views.
C. Create an Amazon Redshift data share that includes the tables that need to be shared.
D. Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.
E. Create views of the tables that need to be shared. Include only the required columns.

Answer: C,D

NEW QUESTION # 366
A data engineer needs to run a data transformation job whenever a user adds a file to an Amazon S3 bucket. The job will run for less than 1 minute. The job must send the output through an email message to the data engineer. The data engineer expects users to add one file every hour of the day.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Create a small Amazon EC2 instance that polls the S3 bucket for new files. Run transformation code on a schedule to generate the output. Use operating system commands to send email messages.
B. Create an AWS Lambda function to transform the data. Use Amazon S3 Event Notifications to invoke the Lambda function when a new object is created. Publish the output to an Amazon Simple Notification Service (Amazon SNS) topic. Subscribe the data engineer's email account to the topic.
C. Deploy an Amazon EMR cluster. Use EMR File System (EMRFS) to access the files in the S3 bucket. Run transformation code on a schedule to generate the output to a second S3 bucket.Create an Amazon Simple Notification Service (Amazon SNS) topic. Configure Amazon S3 Event Notifications to notify the topic when a new object is created.
D. Run an Amazon Elastic Container Service (Amazon ECS) task to poll the S3 bucket for new files.
Run transformation code on a schedule to generate the output. Use operating system commands to send email messages.

Answer: B

Explanation:
An S3 event notification on ObjectCreated can directly invoke a Lambda function as soon as a new file arrives. Lambda can perform your <1 minute transformation and then publish the result to an SNS topic. With the data engineer subscribed to that topic by email, the output is delivered automatically
- no servers to manage, no polling, and minimal ongoing operations.

NEW QUESTION # 367
A company plans to provision a log delivery stream within a VPC. The company configured the VPC flow logs to publish to Amazon CloudWatch Logs. The company needs to send the flow logs to Splunk in near real time for further analysis.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an Amazon Kinesis Data Streams data stream to use Splunk as the destination. Create an AWS Lambda function to send the flow logs from CloudWatch Logs to the data stream.
B. Create an Amazon Kinesis Data Firehose delivery stream to use Splunk as the destination. Create a CloudWatch Logs subscription filter to send log events to the delivery stream.
C. Create an Amazon Kinesis Data Firehose delivery stream to use Splunk as the destination. Create an AWS Lambda function to send the flow logs from CloudWatch Logs to the delivery stream.
D. Configure an Amazon Kinesis Data Streams data stream to use Splunk as the destination. Create a CloudWatch Logs subscription filter to send log events to the data stream.

Answer: B

Explanation:
Kinesis Data Firehose has built-in support for Splunk as a destination, making the integration straightforward. Using a CloudWatch Logs subscription filter directly to Firehose simplifies the data flow, eliminating the need for additional Lambda functions or custom integrations.

NEW QUESTION # 368
......

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