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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Engineer Certification Exam |
| Exam Number: | DEA-C01 |
| Related Certifications: | SnowPro Core Certification |
| Exam Price: | $375 USD |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 115 minutes |
| Real Exam Qty: | Approximately 65 questions |
| Passing Score: | Not publicly disclosed (Snowflake uses scaled scoring) |
| Available Languages: | English |
| Exam Format: | Multiple choice, Multiple select, Scenario-based questions |
| Recommended Training: | SnowPro Advanced Data Engineer Exam Guide Snowflake University Training |
| Exam Registration: | Snowflake Certification Portal |
| Sample Questions: | Snowflake DEA-C01 Sample Questions |
| Exam Way: | Online proctored or authorized testing center |
| Pre Condition: | Recommended: SnowPro Core Certification or equivalent Snowflake experience |
| Official Syllabus URL: | https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/ |
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NEW QUESTION # 178
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 # 179
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB of sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
This solution meets the requirements with the least operational overhead by utilizing managed AWS services that simplify the data ingestion, transformation, and orchestration processes:
Amazon AppFlow is a fully managed integration service that allows data ingestion from Salesforce without custom connectors or manual ETL processes.
AWS Glue provides serverless data integration, making it suitable for extracting data from the MySQL database and transforming it as needed.
AWS Step Functions can then be used to orchestrate and automate the nightly process, minimizing the need for complex management.
NEW QUESTION # 180
What is a characteristic of the operations of streams in Snowflake?
Answer: D
Explanation:
Explanation
A stream is a Snowflake object that records the history of changes made to a table. A stream has an offset, which is a point in time that marks the beginning of the change records to be returned by the stream. Querying a stream returns all change records and table rows from the current offset to the current time. The offset is not automatically advanced by querying the stream, but it can be manually advanced by using the ALTER STREAM command. When a stream is used to update a target table, the offset is advanced to the current time only if the ON UPDATE clause is specified in the stream definition. Each committed transaction on the source table automatically puts a change record in the stream, but uncommitted transactions do not.
NEW QUESTION # 181
A company uses an Amazon S3 Standard bucket to maintain a self-managed transactional data lake that uses Apache Iceberg tables. The data lake ingests data both in real time and in batches.
Users report slow performance for real-time tables. A data engineer reviews the real-time tables and notices that the tables are made up of many small data files The data engineer must improve the performance of the real-time tables.
Which solution will meet this requirement?
Answer: B
Explanation:
Compaction merges many small data files into fewer, larger files, which reduces file count and metadata overhead in Apache Iceberg tables, directly improving query performance for real-time workloads.
NEW QUESTION # 182
A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data.
The company uses an Amazon Redshift cluster for a data warehouse.
An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster.
A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.
Which combination of steps will meet these requirements? (Choose two.)
Answer: D,E
Explanation:
Choice A ensures that live transactional data from PostgreSQL can be accessed directly within Redshift queries.
Choice C archives historical data in Amazon S3, reducing storage costs in Redshift while still making the data accessible via Redshift Spectrum.
NEW QUESTION # 183
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