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Snowflake DEA-C01 Exam Overview:
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Snowflake DEA-C01 Exam Syllabus Topics:
| Topic | Details |
|---|
| Topic 1 | - 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.
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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 | - 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 (Q35-Q40):
NEW QUESTION # 35
Which query will show a list of the 20 most recent executions of a specified task kttask, that have been scheduled within the last hour that have ended or are stillrunning's.
Answer: B
NEW QUESTION # 36
A retail company stores point-of-sale transaction data in an Amazon RDS for MySQL database.
The company maintains historical sales analytics in Amazon Redshift. The company needs to create daily reports that combine the current day's transactions with historical sales patterns for trend analysis. The company requires a solution that provides near real-time insights while minimizing data transfer costs and maintenance overhead.
Which solution will meet these requirements?
- A. Use AWS Glue to create an extract, transform, and load (ETL) pipeline that runs every hour to copy incremental data from RDS for MySQL to Amazon Redshift. Generate reports.
- B. Configure AWS Database Migration Service (AWS DMS) to continuously replicate data from RDS for MySQL to Amazon Redshift. Use Redshift queries to create consolidated reports.
- C. Implement Amazon Redshift federated queries to directly access RDS for MySQL data and join it with existing Redshift tables in a single query.
- D. Export RDS for MySQL data to an Amazon S3 bucket on a regular schedule. Use the COPY command to load the data into Amazon Redshift staging tables. Join the data with historical data.
Answer: B
Explanation:
AWS DMS with ongoing replication (CDC) continuously streams new RDS for MySQL transactions into Amazon Redshift, providing near real-time freshness with minimal maintenance and transferring only changes instead of full batches. Reports can then run in Redshift joining current and historical data without hourly ETL jobs or S3 staging.
NEW QUESTION # 37
A company has an Amazon S3 based data lake. The data lake contains datasets that belong to multiple departments. The data lake ingests millions of customer records each day.
A data engineer needs to design an access and storage solution that allows departments to access only the subset of the company's dataset that each department requires. The solution must follow the principle of least privilege.
Which solution will meet these requirements with the LEAST operational effort?
- A. Set up AWS Lake Formation. Assign LF-Tags to AWS Glue Data Catalog resources. Enable Lake Formation tag-based access control (LF-TBAC).
- B. Deploy an Amazon RDS for PostgreSQL database that has the aws_s3 extension installed.Configure AWS Step Functions events to invoke an AWS Lambda function to sync the data lake with the database.
- C. Set up Amazon Redshift and Amazon Redshift Spectrum as the primary entry points for the data lake. Define an IAM role that Amazon Redshift can assume. Configure the IAM role to grant access to the data that is in Amazon S3.
- D. Define IAM policies and IAM roles for each department. Specify the S3 access paths from the data lake that each team can access.
Answer: A
Explanation:
AWS Lake Formation with LF-Tags and LF-TBAC is designed for fine-grained, scalable access control across shared data lake resources. It lets the company grant each department access only to the datasets it needs while following least privilege, and it reduces ongoing administrative work compared with managing separate IAM policies and S3 paths for every team.
NEW QUESTION # 38
An online retail company has an application that runs on Amazon EC2 instances that are in a VPC. The company wants to collect flow logs for the VPC and analyze network traffic.
Which solution will meet these requirements MOST cost-effectively?
- A. Publish flow logs to Amazon CloudWatch Logs. Use an Amazon OpenSearch Service cluster for analytics.
- B. Publish flow logs to Amazon S3 in text format. Use Amazon Athena for analytics.
- C. Publish flow logs to Amazon CloudWatch Logs. Use Amazon Athena for analytics.
- D. Publish flow logs to Amazon S3 in Apache Parquet format. Use Amazon Athena for analytics.
Answer: D
Explanation:
Publishing flow logs to Amazon S3 in Apache Parquet format and using Amazon Athena for analytics (D) is the most cost-effective solution. This approach minimizes storage costs due to the efficient compression of Parquet, and optimizes query performance and cost in Athena due to the reduced data size and optimized columnar storage.
NEW QUESTION # 39
A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real-time streaming data. The data in the Iceberg tables is in Apache Parquet format.
The company is experiencing slow query performance.
Which solutions will improve query performance? (Choose two.)
- A. Use AWS Glue Data Catalog to enable copy-on-write for the Iceberg tables.
- B. Use AWS Glue Data Catalog to automatically optimize indexes for the Iceberg tables.
- C. Use AWS Glue Data Catalog to generate column-level statistics for the Iceberg tables on a schedule.
- D. Use AWS Glue Data Catalog to generate views for the Iceberg tables.
- E. Use AWS Glue Data Catalog to automatically compact the Iceberg tables.
Answer: A,E
Explanation:
Compaction reduces many small Parquet files into larger ones, lowering file-listing and open/scan overhead for faster reads.
Copy-on-write favors read performance by materializing updates into rewritten data files, resulting in more contiguous, scan-efficient files for queries.
NEW QUESTION # 40
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