Snowflake DEA-C01 Zertifizierungsprüfung & DEA-C01 PDF Demo

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Real Exam Qty:	Approximately 65 questions
Exam Price:	$375 USD
Related Certifications:	SnowPro Core Certification
Exam Format:	Multiple choice, Multiple select, Scenario-based questions
Exam Duration:	115 minutes
Available Languages:	English
Passing Score:	Not publicly disclosed (Snowflake uses scaled scoring)
Certificate Validity Period:	2 years
Recommended Training:	Snowflake University Training SnowPro Advanced Data Engineer Exam Guide
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/

>> Snowflake DEA-C01 Zertifizierungsprüfung <<

DEA-C01 PDF Demo, DEA-C01 Prüfungsvorbereitung

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Snowflake DEA-C01 Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 2	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.
Thema 3	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.
Thema 4	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.
Thema 5	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam DEA-C01 Prüfungsfragen mit Lösungen (Q300-Q305):

300. Frage
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The company runs the data stream in an account named Account A. The company wants to use an AWS Lambda function in an account named Account B to process the data from the data stream. The company creates a Lambda execution role in Account B that has permissions to access data from the data stream in Account A.
What additional step must the company take to meet this requirement?

A. Add a resource-based policy to the cross-account Lambda function to grant the data stream read access to the function.
B. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account B.
C. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account
D. Add a resource-based policy to the data stream to allow read access for the cross-account Lambda execution role.

Antwort: D

Begründung:
To enable cross-account Lambda processing of Kinesis Data Streams, the stream in Account A must explicitly allow the Lambda execution role from Account B. This is done by adding a resource-based policy on the Kinesis data stream to grant kinesis:SubscribeToShard and related read permissions to the cross-account role. Without this resource-based policy, the Lambda in Account B cannot consume the data.

301. Frage
A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies.
The sales table for the first company is named Table S1. The sales table for the second company is named Table S2. Table S1 contains 10 billion records. Table S2 contains 900 million records.
The query becomes slow after the modification. A data engineer must improve the query performance.
Which solutions will meet these requirements? (Choose two.)

A. Use Amazon Redshift Advisor to review and select optimizations to implement.
B. Use the KEY distribution style for both sales tables. Select a low cardinality column to use for the join.
C. Use the KEY distribution style for both sales tables. Select a high cardinality column to use for the join.
D. Use the Amazon Redshift query optimizer to review and select optimizations to implement.
E. Use the EVEN distribution style for Table S1. Use the ALL distribution style for Table S2.

Antwort: A,C

Begründung:
Choosing KEY distribution on both tables with a high-cardinality join column colocates matching rows across nodes and avoids data skew, improving join performance.
Redshift Advisor provides automated, actionable recommendations (e.g., distribution and sort keys, stats) to further optimize the slow query with minimal effort.

302. Frage
A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.
Which solution will meet this requirement with the LEAST operational effort?

A. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the event to Amazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.
B. Create a trail of management events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.
C. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the events to the logs S3 bucket.
D. Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

Antwort: D

Begründung:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/logging-with-S3.html

303. Frage
A large table with 200 columns contains two years of historical data. When queried. the table is filtered on a single day Below is the Query Profile:

Using a size 2XL virtual warehouse, this query look over an hour to complete What will improve the query performance the MOST?

A. Increase the number of clusters in the virtual warehouse
B. Implement the search optimization service on the table
C. increase the size of the virtual warehouse.
D. Add a date column as a cluster key on the table

Antwort: D

Begründung:
Explanation
Adding a date column as a cluster key on the table will improve the query performance by reducing the number of micro-partitions that need to be scanned. Since the table is filtered on a single day, clustering by date will make the query more selective and efficient.

304. Frage
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution.
The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
Which solution will meet these requirements with the LEAST operational effort?

A. Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.
B. Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.
C. Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.
D. Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.

Antwort: D

Begründung:
Amazon Athena federated query allows querying data from multiple data sources, including Amazon Redshift, Teradata, and Google BigQuery, using their federated query connectors. This solution offers a serverless approach, reducing the operational overhead of managing infrastructure while allowing SQL-based transformations across all data sources. Once the data is read and joined, Athena can write the results back to Amazon S3 in the Iceberg table format with a Merge operation.
This approach minimizes the operational effort as Athena manages the complexity of connecting to different databases through its connectors, and you can perform the necessary transformations and data joins using familiar SQL.
While AWS Glue is a powerful ETL tool, it requires more operational effort to manage complex transformations across multiple systems, and managing native transforms across different engines (Redshift, Teradata, BigQuery) in Glue can introduce additional complexity.
Amazon EMR with PySpark can handle the task, but it requires more operational effort to manage and maintain the EMR cluster. Writing and maintaining PySpark code can also be more complex compared to using SQL in Athena.
Appflow is primarily designed for simple data movement between SaaS applications and AWS services, but it does not provide the complex transformation and joining capabilities needed for this scenario. Using Athena after Appflow for joins adds unnecessary complexity compared to directly using federated queries in Athena.

305. Frage
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