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Snowflake DEA-C01 Exam Overview:
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Snowflake DEA-C01 Exam Syllabus Topics:
| Topic | Details |
|---|
| Topic 1 | - 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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| Topic 2 | - 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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| 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.
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| 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.
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| 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.
|
Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q176-Q181):
NEW QUESTION # 176
A popular e-commerce platform experiences heavy traffic during peak hours, resulting in high latency for frequently accessed product listings. To improve performance and response times, the platform decides to implement caching for the most frequently queried product data. They aim to store the results of these queries in a highly available, low-latency cache to reduce database load and improve overall user experience.
Which AWS service would be most suitable for implementing the caching for the most frequent queries and storing their results as a cache?
- A. Use Amazon DynamoDB with DAX.
- B. Use Amazon Redshift.
- C. Use Amazon MemoryDB for Redis.
- D. Implement S3 Gateway endpoint and store results temporarily in S3.
Answer: C
Explanation:
Amazon MemoryDB for Redis is a fully managed Redis-compatible, in-memory database service that provides enhanced durability, replication, and performance. It is optimized for caching frequently accessed data and provides sub-millisecond latency, making it suitable for improving response times and reducing database load in high-traffic scenarios such as an e-commerce platform.
References:
https://aws.amazon.com/redshift/
https://aws.amazon.com/dynamodb/dax/
https://aws.amazon.com/s3/
https://aws.amazon.com/memorydb/
NEW QUESTION # 177
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?
- A. Replace sum(sales_amount) with count(*) for the aggregation.
- B. Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.
- C. Remove the GROUP BY clause.
- D. Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023.
Answer: B
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/select.html
NEW QUESTION # 178
Michael, a Data Engineer Running a Data query to achieve Union of Data sets coming from Multi-ple data sources, later he figured out that Data processing query is taking more time than expected. He started analyzing the Query performance using query profile interface. He discovered & realized that he used UNION when the UNION ALL semantics was sufficient.
Which Extra Data Processing Operator Michael figured out while doing query profile analysis in this case which helps him to identify this performance bottlenecks?
- A. Filter
- B. UNION ALL
- C. Join
- D. Aggregate
- E. Flatten
Answer: D
Explanation:
Explanation
In SQL, it is possible to combine two sets of data with either UNION or UNION ALL constructs. The difference between them is that UNION ALL simply concatenates inputs, while UNION does the same, but also performs duplicate elimination.
A common mistake is to use UNION when the UNION ALL semantics are sufficient. These que-ries show in Query Profile as a UnionAll operator with an extra Aggregate operator on top (which performs duplicate elimination).
To Know more about Data Processing Operators, please do refer:
https://docs.snowflake.com/en/user-guide/ui-query-profile#operator-types
NEW QUESTION # 179
Which Role that is dedicated to user and role management only?
- A. SECURITYADMIN
- B. USERADMIN
- C. ORGADMIN
- D. PUBLIC
- E. SYSADMIN
Answer: B
NEW QUESTION # 180
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications?
(Choose two.)
- A. Spark SQL
- B. Gremlin
- C. ANSI SQL
- D. SPARQL
- E. SQL
Answer: B,D
NEW QUESTION # 181
......
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