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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q107-Q112):

NEW QUESTION # 107
A stock broking firm is working on a new application that stores and updates stock market data in Amazon DynamoDB. Different applications will track these changes in real-time. The team is looking for a tool to capture this changing data, which should be available for processing for up to one year. Streaming data should be cleansed, transformed, and stored in Amazon S3 data lakes.
Which service can be considered?

Answer: D

Explanation:
Amazon DynamoDB offers two streaming models for change data capture,
Kinesis Data Streams for DynamoDB: Data retention with this model is 1 year. It can process stream records using AWS Lambda, Amazon Managed Service for Apache Flink, Kinesis data firehose, or AWS Glue streaming ETL. AWS Glue Streaming ETL can be used to cleanse, and transform streaming data and store it in either Amazon S3 data lakes or JDBC data stores.
DynamoDB Streams: This model retains data for 24 hours. It can process stream records using AWS Lambda and the DynamoDB Streams Kinesis adapter.
Reference:
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/streamsmain.html


NEW QUESTION # 108
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-inventory-athena-query.html S3 Select allows you to retrieve a subset of data from an object stored in S3 using simple SQL expressions. It is capable of working directly with objects in Parquet format.


NEW QUESTION # 109
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?

Answer: C

Explanation:
Converting the CSV files into Apache Parquet during your Glue ETL jobs dramatically reduces both storage size (because Parquet is a compressed, columnar format) and query cost (because analytics engines only scan the columns you need). This change requires no new infrastructure and pays off immediately for your daily analysis workloads.


NEW QUESTION # 110
A retail company stores order information in an Amazon Aurora table named Orders. The company needs to create operational reports from the Orders table with minimal latency. The Orders table contains billions of rows, and over 100,000 transactions can occur each second.
A marketing team needs to join the Orders data with an Amazon Redshift table named Campaigns in the marketing team's data warehouse. The operational Aurora database must not be affected.
Which solution will meet these requirements with the LEAST operational effort?

Answer: B

Explanation:
Aurora's zero-ETL integration with Redshift automatically and continuously streams data changes from the Orders table into a Redshift table with virtually no setup or maintenance. You can then define a materialized view in Redshift to join that replicated Orders table with Campaigns. This approach ensures minimal impact on the production Aurora workload and requires far less operational effort than building and managing your own ETL or replication pipelines.


NEW QUESTION # 111
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?

Answer: B


NEW QUESTION # 112
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