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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.
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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q134-Q139):

NEW QUESTION # 134
A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.
The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Athena to load all the data into Amazon S3 in Apache Parquet format.
B. Use Amazon Redshift Serverless to load all the data into Amazon Redshift managed storage (RMS).
C. Use Amazon Redshift provisioned clusters to load all the data into Amazon Redshift managed storage (RMS).
D. Use Amazon Aurora PostgreSQL to load all the data into Aurora.

Answer: B

Explanation:
Amazon Redshift Serverless is ideal for this scenario as it provides a fully managed, serverless data warehouse solution with high performance for analytics queries. Key reasons why it meets the requirements with the least operational overhead:
Serverless Configuration: Redshift Serverless automatically scales capacity based on query demand, making it well-suited for unpredictable spikes in usage without requiring manual adjustments.
Managed Storage (RMS): Loading data into Amazon Redshift managed storage provides optimized storage for analytics and integrates with Amazon QuickSight for fast query performance in direct query mode.
Cost-Effective and Low Management: Since the data warehouse is only actively used for a few hours daily, Redshift Serverless provides a cost- effective option by scaling resources dynamically, which eliminates the need to manage and pay for idle infrastructure.

NEW QUESTION # 135
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real- time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Choose two.)

A. Use Amazon Redshift ML to generate inventory recommendations.
B. Use Amazon Redshift ML to schedule regular data exports for offline model training.
C. Use Amazon Redshift as a file storage system to archive old inventory management reports.
D. Use SQL to invoke a remote SageMaker endpoint for prediction.
E. Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.

Answer: A,D

Explanation:
Amazon Redshift ML integrates machine learning (ML) directly into the Redshift environment, allowing you to build and use ML models with SQL commands. By leveraging Redshift ML, the company can make real-time inventory recommendations based on historical and current data directly within Redshift.
Redshift can invoke external services, such as a SageMaker real-time endpoint, using SQL queries. This allows the company to send real-time data from Redshift to SageMaker and receive predictions (e.g., inventory forecasting) in real time, meeting the need for real-time predictions.

NEW QUESTION # 136
A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?

A. Change the data format from .csv to JSON format. Apply Snappy compression.
B. Change the data format from .csv to Apache Parquet. Apply Snappy compression.
C. Compress the .csv files by using gzip compression.
D. Compress the .csv files by using Snappy compression.

Answer: B

Explanation:
Apache Parquet is a columnar storage format optimized for analytical queries. It is highly efficient for query performance, especially when queries involve selecting specific columns, as it allows for column pruning and predicate pushdown optimizations.

NEW QUESTION # 137
A data engineer has implemented data quality rules in 1,000 AWS Glue Data Catalog tables.
Because of a recent change in business requirements, the data engineer must edit the data quality rules.
How should the data engineer meet this requirement with the LEAST operational overhead?

A. Create a pipeline in AWS Glue ETL to edit the rules for each of the 1,000 Data Catalog tables.
Use an AWS Lambda function to call the corresponding AWS Glue job for each Data Catalog table.
B. Create an Amazon EMR cluster. Run a pipeline on Amazon EMR that edits the rules for each Data Catalog table. Use an AWS Lambda function to run the EMR pipeline.
C. Create an AWS Lambda function that makes an API call to AWS Glue Data Quality to make the edits.
D. Use the AWS Management Console to edit the rules within the Data Catalog.

Answer: C

NEW QUESTION # 138
A table is loaded using Snowpipe and truncated afterwards Later, a Data Engineer finds that the table needs to be reloaded but the metadata of the pipe will not allow the same files to be loaded again.
How can this issue be solved using the LEAST amount of operational overhead?

A. Wait until the metadata expires and then reload the file using Snowpipe
B. Recreate the pipe by using the create or replace pipe command
C. Modify the file by adding a blank row to the bottom and re-stage the file
D. Set the FORCE=TRUE option in the Snowpipe COPY INTO command

Answer: D

Explanation:
Explanation
The FORCE=TRUE option in the Snowpipe COPY INTO command allows Snowpipe to load files that have already been loaded before, regardless of the metadata. This is the easiest way to reload the same files without modifying them or recreating the pipe.

NEW QUESTION # 139
......

In this way, the Snowflake DEA-C01 certified professionals can not only validate their skills and knowledge level but also put their careers on the right track. By doing this you can achieve your career objectives. To avail of all these benefits you need to pass the SnowPro Advanced: Data Engineer Certification Exam (DEA-C01) exam which is a difficult exam that demands firm commitment and complete Snowflake DEA-C01 exam questions preparation.

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