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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q200-Q205):

NEW QUESTION # 200
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys.
Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?

Answer: C

Explanation:
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.S3.html


NEW QUESTION # 201
A company has a data lake on AWS. The data lake ingests sources of data from business units.
The company uses Amazon Athena for queries. The storage layer is Amazon S3 with an AWS Glue Data Catalog as a metadata repository.
The company wants to make the data available to data scientists and business analysts.
However, the company first needs to manage fine-grained, column-level data access for Athena based on the user roles and responsibilities.
Which solution will meet these requirements?

Answer: D

Explanation:
Lake Formation supports fine-grained access control, including column-level permissions.


NEW QUESTION # 202
A company has an application that uses a microservice architecture. The company hosts the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.
The company wants to set up a robust monitoring system for the application. The company needs to analyze the logs from the EKS cluster and the application. The company needs to correlate the cluster's logs with the application's traces to identify points of failure in the whole application request flow.
Which combination of steps will meet these requirements with the LEAST development effort?
(Choose two.)

Answer: B,C

Explanation:
FluentBit is a lightweight, efficient tool to collect, process, and forward logs from the EKS cluster.
It integrates well with AWS services like CloudWatch or OpenSearch for storing and analyzing logs. OpenTelemetry is an open-source standard for collecting distributed traces, making it ideal for monitoring and analyzing application performance in a microservice architecture. Using these tools requires minimal development effort since they are widely adopted and have strong integrations with AWS.
Amazon OpenSearch (formerly Elasticsearch) is a fully managed service that makes it easy to store, search, and analyze log data, including traces from distributed systems. It is commonly used to correlate logs and traces because it provides robust querying capabilities and visualization features (via Kibana or OpenSearch Dashboards). This solution offers the necessary tools for log-trace correlation with minimal custom development effort.
While CloudWatch is suitable for log collection, Amazon Kinesis is more suited for real-time data streaming rather than collecting traces for correlation.
Kinesis also requires more configuration and development effort compared to OpenTelemetry for tracing.
Similar to Kinesis, Amazon MSK is primarily used for data streaming rather than trace collection.
It requires additional setup and custom development for correlation compared to the more straightforward integration provided by FluentBit and OpenTelemetry.
AWS Glue is primarily used for ETL (extract, transform, load) operations in a data lake. It is not designed for real-time log and trace correlation and would require significant development effort compared to the built-in capabilities of OpenSearch.


NEW QUESTION # 203
Can Masking policies be applied to virtual columns?

Answer: A


NEW QUESTION # 204
A company wants to use machine learning (ML) to perform analytics on data that is in an Amazon S3 data lake. The company has two data transformation requirements that will give consumers within the company the ability to create reports.
The company must perform daily transformations on 300 GB of data that is in a variety format that must arrive in Amazon S3 at a scheduled time. The company must perform one-time transformations of terabytes of archived data that is in the S3 data lake. The company uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Directed Acyclic Graphs (DAGs) to orchestrate processing.
Which combination of tasks should the company schedule in the Amazon MWAA DAGs to meet these requirements MOST cost-effectively? (Choose two.)

Answer: C,D


NEW QUESTION # 205
......

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