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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 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 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	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	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.

Reference: https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/

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Snowflake SnowPro Advanced: Data Engineer Certification Sample Questions:

Question #1

A data engineer needs to use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket. When the data engineer connects to the QuickSight dashboard, the data engineer receives an error message that indicates insufficient permissions.
Which factors could cause to the permissions-related errors? (Choose two.)

A. There is no IAM role assigned to QuickSight.
B. There is no connection between QuickSight and Athena.
C. The Athena tables are not cataloged.
D. QuickSight does not have access to the S3 bucket.
E. QuickSight does not have access to decrypt S3 data.

Reveal Solution Discussion 0

Correct Answer: D,E 🗳️

Question #2

A large e-commerce company manages a single S3 bucket containing product images, customer data, and marketing materials. Different departments within the company require access to specific data sets, but granting access to the entire bucket for each department poses a security risk.
What AWS service can be implemented to provide granular access control and simplify permission management for various departments while using a single S3 bucket?

A. S3 Object Ownership
B. S3 Access Pointsright
C. Bucket ACLs
D. IAM User Groups with bucket policies

Reveal Solution Discussion 0

Correct Answer: B 🗳️

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Question #3

A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.
The company wants to achieve optimal throughput from each device that runs the gaming app.
Additionally, the company wants to develop an application to process data streams. The stream- processing application must have dedicated throughput for each internal consumer.
Which solution will meet these requirements?

A. Configure the mobile app to call the PutRecordBatch API operation to send data to Amazon Kinesis Data Firehose. Submit an AWS Support case to turn on dedicated throughput for the company's AWS account. Allow each internal consumer to access the stream.
B. Configure the mobile app to use the Amazon Kinesis Producer Library (KPL) to send data to Amazon Kinesis Data Firehose. Use the enhanced fan-out feature with a stream for each internal consumer.
C. Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Host the stream-processing application for each internal consumer on Amazon EC2 instances. Configure auto scaling for the EC2 instances.
D. Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature with a stream for each internal consumer.

Reveal Solution Discussion 0

Correct Answer: D 🗳️

Question #4

A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data. The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis.
The company needs to optimize the data format for analytical queries.
Which solutions will meet these requirements with the SHORTEST query times? (Choose two.)

A. Use Avro format. Use AWS Glue Data Catalog to track schema changes.
B. Use ORC format. Store schema definitions in separate files in Amazon S3.
C. Use Apache Parquet format. Use an external Amazon DynamoDB table to track schema changes.
D. Use Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.
E. Use ORC format. Use AWS Glue Data Catalog to track schema changes.

Reveal Solution Discussion 0

Correct Answer: D,E 🗳️

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Question #5

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. 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.
B. 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.
C. 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.
D. 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.

Reveal Solution Discussion 0

Correct Answer: C 🗳️

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