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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	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	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	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 (Q277-Q282):

NEW QUESTION # 277
Which column provides information when the stream became stale or may become stale if not con-sumed?

A. STREAM_STALE_PERIOD
B. STALE_AFTER
C. STALE_STREAM_PERIOD
D. STALE_PERIOD_AFTER

Answer: B

Explanation:
Explanation
execute SHOW STREAMS command.
Column Name: STALE_AFTER
Timestamp when the stream became stale or may become stale if not consumed. The value is calcu-lated by adding the retention period for the source table (i.e. the larger of the DA-TA_RETENTION_TIME_IN_DAYS or MAX_DATA_EXTENSION_TIME_IN_DAYS parame-ter setting) to the last time the stream was read.

NEW QUESTION # 278
Snowflake supports using key pair authentication for enhanced authentication security as an alterna-tive to basic authentication (i.e. username and password). Select the list of SnowFlake Clients sup-port the same?
[Select All that Apply]

A. SnowSQL
B. Go Driver
C. Node.js
D. SnowFlake Connector for Spark
E. SnowCD

Answer: A,B,C,D

NEW QUESTION # 279
A company has a data lake in Amazon S3. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run.
A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?

A. Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.
B. Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.
C. Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.
D. Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.

Answer: D

Explanation:
Evaluate Data Quality Transform in AWS Glue is a built-in feature that allows the application of Data Quality Definition Language (DQDL) to define and apply data quality rules directly within AWS Glue ETL jobs. This option offers the least implementation effort because it leverages a native AWS Glue feature, allowing data engineers to easily add and enforce data quality checks without needing custom code or external libraries.
While SQL can implement data quality rules, this approach requires manually writing and managing SQL queries within each job, leading to higher implementation effort compared to using built-in Glue functionality.
PyDeequ is an external library that requires custom coding and additional integration effort.
Although it is effective, it demands more implementation effort than using AWS Glue's built-in features.
Great Expectations is another external data quality library. While it is robust, it also involves custom coding and higher maintenance effort compared to AWS Glue's native data quality evaluation tools.

NEW QUESTION # 280
A company that operates globally must follow regulations that require data from an AWS Region to be accessible only within that Region.
A data engineer is creating a data pipeline that will create resources in the Region where the data engineer works. The data pipeline should have access to data only from the Region where the data engineer works. The pipeline uses Active Directory as an identity and authentication system.
The pipeline uses a custom identity broker application to verify that employees are signed in to Active Directory and to obtain temporary credentials by using the AssumeRole API operation.
Which solution will meet the locality requirements with the LEAST administrative effort?

A. Create an IAM group for each Region. Include the required IAM policies for each IAM group. Add users to each IAM group so that when users log in by obtaining the temporary credentials, the users will receive the appropriate access based on the IAM group.
B. Create individual IAM policies that allow users to create resources in a specific Region. Assign the policies to each data engineer. Allow users to assume the individually assigned role when the users log in to AWS.
C. Create an IAM role for data engineers in each Region separately. Instruct each data engineer to obtain temporary credentials by assuming the appropriate Region specific IAM role.
D. Create an IAM role that has permissions to create resources. Create a policy for each Region that ensures users can create resources only in that Region. Pass the policy as the session policy when employees obtain the temporary credentials.

Answer: D

Explanation:
Using a single IAM role with region-scoped session policies (using the aws:RequestedRegion condition) applied at AssumeRole time lets the identity broker grant temporary credentials limited to the engineer's Region. This enforces Regional access while avoiding per-Region roles or per- user policies, minimizing administration.

NEW QUESTION # 281
A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an AWS Glue DataBrew project for the data in the S3 bucket. Create a ruleset for the data quality rules. Create a profile job to run the data quality rules. Use Amazon EventBridge to run the profile job when data is added to the S3 bucket.
B. Create an Amazon EMR provisioned cluster. Add a Python open source data quality package to the EMR cluster. Use the Python package to write code for data quality rules and to copy the data from the S3 bucket to the EMR cluster. Copy the data from the S3 bucket to the EMR cluster. Run the data quality rules.
C. Create an AWS Lambda function to check data quality and to raise exceptions in the code. Run the function when data is added to the S3 bucket. Create an Amazon CloudWatch alarm for exceptions in the code.
D. Create AWS Lambda functions to evaluate data quality rules. Use AWS Step Functions to orchestrate a workflow that publishes notifications when the data fails to meet data quality rules.

Answer: A

Explanation:
AWS Glue DataBrew is a fully managed, serverless service with built-in data quality rulesets and profiling jobs that run directly on S3 data. Triggering the profile job via EventBridge on object creation applies rules with minimal code and management overhead.

NEW QUESTION # 282
......

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