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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	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 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q175-Q180):

NEW QUESTION # 175
A company stores server logs in an Amazon S3 bucket. The company needs to keep the logs for
1 year. The logs are not required after 1 year.
A data engineer needs a solution to automatically delete logs that are older than 1 year.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an AWS Step Functions state machine to delete the logs after 1 year.
B. Define an S3 Lifecycle configuration to delete the logs after 1 year.
C. Create an AWS Lambda function to delete the logs after 1 year.
D. Schedule a cron job on an Amazon EC2 instance to delete the logs after 1 year.

Answer: B

Explanation:
An S3 Lifecycle configuration is the best choice for this requirement because it allows the company to define a rule that will automatically delete objects in an S3 bucket after a specified period, in this case, 1 year. This solution provides the least operational overhead because it is a built-in feature of Amazon S3, requires no additional infrastructure or management, and is designed specifically for managing object lifecycles.

NEW QUESTION # 176
A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.
The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS CloudFormation templates to automatically process the analytics workload.
B. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.
C. Use the AWS CLI to automatically process the analytics workload.
D. Use Amazon Redshift Serverless to automatically process the analytics workload.

Answer: D

Explanation:
Use Amazon Redshift Serverless. This option allows the data engineer to focus on the analytics processes themselves without worrying about cluster provisioning, scaling, or management. It provides an on-demand, serverless solution that can handle variable workloads and is cost- effective for intermittent and irregular processing needs like those described.

NEW QUESTION # 177
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
B. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).
C. Use API calls to access and integrate third-party datasets from AWS Data Exchange.
D. Use API calls to access and integrate third-party datasets from AWS DataSync.

Answer: D

Explanation:
Data Exchange is the AWS official third-party datasets repository:
https://aws.amazon.com/data-exchange

NEW QUESTION # 178
A company uses Amazon S3 and AWS Glue Data Catalog to manage a data lake that contains contact information for customers. The company uses PySpark and AWS Glue jobs with a DynamicFrame to run a workflow that processes data within the data lake.
A data engineer notices that the workflow is generating errors as a result of how customer postal codes are stored in the data lake. Some postal codes include unnecessary numbers or invalid characters.
The data engineer needs a solution to address the errors and correct the postal codes in the data lake.

A. Create a schema definition for PySpark that matches the format the processing workflow requires for postal codes. Pass the schema to the DynamicFrame during processing.
B. Set the DynamicFrame additional_options parameter `useS3ListImplementation' to True.
C. Configure the column.push_down_predicate setting and the catalogPartitionPredicate settings for the postal code column in the DynamicFrame.
D. Use AWS Glue workflow properties to allow job state sharing. Configure the AWS Glue jobs to read values from the postal code column by using the properties from a previously successful run of the jobs.

Answer: A

Explanation:
By defining and enforcing a strict schema in PySpark that specifies the exact format you expect for postal codes (for example, a string of five digits), Spark will automatically flag or coerce any values that don't match. Passing that schema into your Glue DynamicFrame ensures that malformed postal codes are identified and can be corrected or filtered out as part of your ETL logic, eliminating the downstream errors.

NEW QUESTION # 179
A company needs to build an extract, transform, and load (ETL) pipeline that has separate stages for batch data ingestion, transformation, and storage. The pipeline must store the transformed data in an Amazon S3 bucket. Each stage must automatically retry failures. The pipeline must provide visibility into the success or failure of individual stages.
Which solution will meet these requirements with the LEAST operational overhead?

A. Chain AWS Glue jobs that perform each stage together by using job triggers. Set the MaxRetries field to 0.
B. Build an Amazon EventBridge based pipeline that invokes AWS Lambda functions to perform each stage.
C. Schedule Apache Airflow directed acyclic graphs (DAGs) on Amazon Manages Workflows tor Apache Airflow (Amazon MWAA) to orchestrate pipeline steps. Use Amazon Simple Queue Service (Amazon SQS) to ingest data. Use AWS Glue jobs to transform data and store the data in the S3 bucket.
D. Deploy AWS Step Functions workflows to orchestrate AWS Lambda functions that ingest data.
Use AWS Glue jobs to transform the data and store the data in the S3 bucket.

Answer: D

Explanation:
AWS Step Functions provides managed orchestration with per-step retries, error handling, and execution history. Orchestrating Lambda for ingestion and AWS Glue jobs for transformation to S3 delivers a serverless, low-maintenance pipeline with built-in visibility into each stage's success or failure.

NEW QUESTION # 180
......

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