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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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.
Topic 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q182-Q187):

NEW QUESTION # 182
Clones can be cloned, with no limitations on the number or iterations of clones that can be created (e.g. you can create a clone of a clone of a clone, and so on), which results in a n-level hierarchy of cloned objects, each with their own portion of shared and independent data storage?

A. FALSE
B. TRUE

Answer: B

NEW QUESTION # 183
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an Amazon EventBridge rule to match AWS Glue job failure events. Define an Amazon CloudWatch metric based on the EventBridge rule. Set up a CloudWatch alarm based on the metric to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
B. Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
C. Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to target an AWS Lambda function that reads the logs and sends notifications to an Amazon Simple Notification Service (Amazon SNS) topic if AWS Glue job failure logs are present.
D. Create an Amazon EventBridge rule to match AWS Glue job failure events. Configure the rule to target an AWS Lambda function to process events. Configure the function to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.

Answer: A

Explanation:
By creating an EventBridge rule that matches Glue job state-change events where the state is FAILED, you can emit a custom CloudWatch metric without writing any code. Then you define a CloudWatch alarm on that metric to notify an SNS topic upon breach. This approach requires no Lambda functions or log parsing (just the rule, the metric, and the alarm) minimizing operational overhead.

NEW QUESTION # 184
A company stores analysis results for thousands of daily customer service call transcripts in an Amazon DynamoDB table. An application generates business reports by using only the previous
7 working days of data. To reduce costs, the company wants to implement a solution that automatically removes data that is older than 7 working days.
Which solution will meet this requirement with the LEAST operational overhead?

A. Create a new DynamoDB table. Enable TTL on the new table. Migrate the data to the new table.
Update the application to use the new table.
B. Use Amazon EventBridge to invoke an AWS Lambda function on a schedule to delete old records every day.
C. Enable TTL on the DynamoDB table. Add an expiration timestamp attribute to each item in the table.
D. Use AWS Glue jobs to implement a daily batch job to scan for and delete expired items from the DynamoDB table.

Answer: C

Explanation:
DynamoDB TTL is the simplest native feature for automatically removing old items with minimal administration. By storing an expiration timestamp on each item and enabling TTL on the table, the company can let DynamoDB handle deletion of expired data automatically without building and maintaining scheduled jobs, Lambda code, or migration steps.

NEW QUESTION # 185
UDTFs also called a table function, returns zero, one, or multiple rows for each input row?

A. YES
B. NO

Answer: A

Explanation:
Explanation
UDFs may be scalar or tabular.
A scalar function returns one output row for each input row. The returned row consists of a single column/value.
A tabular function, also called a table function, returns zero, one, or multiple rows for each input row. A tabular UDF is defined by specifying a return clause that contains the TABLE keyword and specifies the names and data types of the columns in the table results. Tabular UDFs are often called UDTFs (user-defined table functions) or table UDFs.

NEW QUESTION # 186
A company stores time-series data that is collected from streaming services in an Amazon S3 bucket. The company must ensure that only workloads that are deployed within the company's VPC can access the data. Which solution will meet this requirement?

A. Use a network ACL on the VPC subnets to allow only specific resources to access the S3 bucket.
B. Define an IAM policy that denies access to all users unless the request originates from within the company's VPC.
C. Create an S3 bucket policy that uses a condition to allow access only to traffic that originates from the company's VPC.
D. Apply a security group to the S3 bucket that allows connections only from the company's VPC CIDR block.

Answer: C

Explanation:
An S3 bucket policy can restrict access to requests that come through a specific VPC endpoint by using a condition on the VPC endpoint (or VPC) context, ensuring only workloads running inside the company's VPC (using that endpoint) can access the bucket.

NEW QUESTION # 187
......

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