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Snowflake DEA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Engineer Certification Exam
Exam Number:DEA-C01
Passing Score:750 / 1000
Exam Price:$375 USD
Exam Duration:115 minutes
Exam Format:Multiple select, Multiple choice
Certificate Validity Period:2 years
Real Exam Qty:65
Available Languages:Japanese, English
Related Certifications:SnowPro Advanced: Data Analyst
SnowPro Advanced: Security Engineer
SnowPro Advanced: Data Scientist
SnowPro Advanced: Administrator
SnowPro Core
SnowPro Advanced: Architect
Recommended Training:Snowflake University Training Courses
SnowPro Advanced: Data Engineer Exam Study Guide
Exam Registration:Pearson VUE Registration
Snowflake Certification Portal
Sample Questions:Snowflake DEA-C01 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:Must hold active SnowPro Core Certification; recommended 2+ years hands-on data engineering experience
Official Syllabus URL:https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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.
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
  • 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
A data engineer uses AWS Lake Formation to manage access to data that is stored in an Amazon S3 bucket. The data engineer configures an AWS Glue crawler to discover data at a specific file location in the bucket, s3://examplepath. The crawler execution fails with the following error: "The S3 location: s3://examplepath is not registered." The data engineer needs to resolve the error.
Which solution will meet this requirement?

Answer: D

Explanation:
In Lake Formation, you must explicitly register each S3 path as a data location before any Glue crawler or user can access it. Registering s3:// examplepath in Lake Formation grants the crawler the metadata permissions it needs to crawl that location.


NEW QUESTION # 94
Elon, a Data Engineer, needs to Split Semi-structured Elements from the Source files and load them as an array into Separate Columns.
Source File:
1.+----------------------------------------------------------------------+
2.| $1 |
3.|----------------------------------------------------------------------|
4.| {"mac_address": {"host1": "197.128.1.1","host2": "197.168.0.1"}}, |
5.| {"mac_address": {"host1": "197.168.2.1","host2": "197.168.3.1"}} |
6.+----------------------------------------------------------------------+ Output: Splitting the Machine Address as below.
1.COL1 | COL2 |
2.|----------+----------|
3.| [ | [ |
4.| "197", | "197", |
5.| "128", | "168", |
6.| "1", | "0", |
7.| "1" | "1" |
8.| ] | ] |
9.| [ | [ |
10.| "197", | "197", |
11.| "168", | "168", |
12.| "2", | "3", |
13.| "1" | "1" |
14.| ] | ]
Which SnowFlake Function can Elon use to transform this semi structured data in the output for-mat?

Answer: C


NEW QUESTION # 95
A car sales company maintains data about cars that are listed for sale in an area. The company receives data about new car listings from vendors who upload the data daily as compressed files into Amazon S3. The compressed files are up to 5 KB in size. The company wants to see the most up-to- date listings as soon as the data is uploaded to Amazon S3.
A data engineer must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The data engineer must also provide the ability to perform one-time queries and analytical reporting. The query solution must be scalable.
Which solution will meet these requirements MOST cost-effectively?

Answer: B

Explanation:
AWS Glue is a fully managed ETL (Extract, Transform, Load) service that is cost-effective and scalable for processing incoming data. It can handle the compressed data files efficiently.
S3 Event Notifications and AWS Lambda provide a low-cost, serverless orchestration solution that automatically triggers workflows as soon as new files are uploaded to S3. This ensures real- time or near real-time processing of the data without the need to manage long-running services like EMR.
Amazon Athena is a serverless, scalable query service that allows users to run SQL queries on data directly in S3, which is ideal for one-time queries and analytical reporting without provisioning infrastructure.
Amazon QuickSight provides a cost-effective, fully managed service for visualizing the data, making it suitable for the dashboard requirements.
This solution is cost-effective because it minimizes the need for provisioned infrastructure and uses serverless and managed services like Lambda, Glue, Athena, and QuickSight, all of which automatically scale with demand.


NEW QUESTION # 96
A company processes a CSV file that contains millions of transaction records every day. The file is stored in Amazon S3. Each transaction must be validated before updating a database. The company needs a solution that will process the data in parallel. The solution must use error handling that stops the entire process if more than 15% of the records fail validation. Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
AWS Step Functions Distributed Map is designed to process large datasets from Amazon S3 in parallel with minimal infrastructure management. The ToleratedFailurePercentage setting directly supports the requirement to stop the overall workflow when more than 15% of records fail validation, which makes it the lowest-overhead solution for this use case.


NEW QUESTION # 97
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: A,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 # 98
......

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