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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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.

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The next step to do is to take Snowflake DEA-C01. These DEA-C01 practice questions can help you measure your skill to see if it has already met the standard set by Snowflake DEA-C01. To optimize the effectiveness, We have made the DEA-C01 Practice Test using the same format as the SnowPro Advanced: Data Engineer Certification Exam exam. All Snowflake Exam Dumps questions appearing on the mock test are the ones we carefully predicted to appear on your upcoming exam.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
A retail company uses AWS Glue for extract, transform, and load (ETL) operations on a dataset that contains information about customer orders. The company wants to implement specific validation rules to ensure data accuracy and consistency.
Which solution will meet these requirements?

A. Create custom AWS Glue Data Quality rulesets to define specific data quality checks.
B. Use AWS Glue job bookmarks to track the data for accuracy and consistency.
C. Use the built-in AWS Glue Data Quality transforms for standard data quality validations.
D. Use AWS Glue Data Catalog to maintain a centralized data schema and metadata repository.

Answer: A

Explanation:
Custom AWS Glue Data Quality rulesets allow you to define precise data quality checks tailored to your specific needs, ensuring that the data meets the required standards of accuracy and consistency. This approach provides flexibility to implement a wide range of validation rules based on your business requirements.

NEW QUESTION # 65
A data engineer is using an AWS Glue crawler to catalog data that is in an Amazon S3 bucket.
The S3 bucket contains both .csv and json files. The data engineer configured the crawler to exclude the .json files from the catalog.
When the data engineer runs queries in Amazon Athena, the queries also process the excluded .json files. The data engineer wants to resolve this issue. The data engineer needs a solution that will not affect access requirements for the .csv files in the source S3 bucket.
Which solution will meet this requirement with the SHORTEST query times?

A. Use the Athena console to ensure the Athena queries also exclude the .json files.
B. Use S3 bucket policies to block access to the .json files.
C. Relocate the .json files to a different path within the S3 bucket.
D. Adjust the AWS Glue crawler settings to ensure that the AWS Glue crawler also excludes .json files.

Answer: C

Explanation:
Athena does not recognize exclude patterns that you specify an AWS Glue crawler. For example, if you have an Amazon S3 bucket that contains both .csv and .json files and you exclude the .json files from the crawler, Athena queries both groups of files. To avoid this, place the files that you want to exclude in a different location.
https://docs.aws.amazon.com/athena/latest/ug/troubleshooting-athena.html

NEW QUESTION # 66
For the most efficient and cost-effective Data load experience, Data Engineer needs to inconsider-ate which of the following considerations?

A. Amazon Kinesis Firehose can be convenient way to aggregate and batch data files which also allows defining both the desired file size, called the buffer size, and the wait interval after which a new file is sent, called the buffer interval.
B. When preparing your delimited text (CSV) files for loading, the number of columns in each row should be consistent.
C. Enabling the STRIP_OUTER_ARRAY file format option for the COPY INTO <ta-ble> command to remove the outer array structure and load the records into separate table rows.
D. Split larger files into a greater number of smaller files, maximize the processing over-head for each file.
(Correct)
E. if the "null" values in your files indicate missing values and have no other special mean-ing, Snowflake recommend setting the file format option STRIP_NULL_VALUES to TRUE when loading the semi-structured data file.

Answer: D

Explanation:
Explanation
Split larger files into a greater number of smaller files to distribute the load among the compute re-sources in an active warehouse. This would minimize the processing overhead rather than maximize it.
Rest is recommended Data loading considerations.

NEW QUESTION # 67
A data engineer must implement a data cataloging solution to track schema changes in an Amazon Redshift table.
Which solution will meet these requirements?

A. Use the AWS Schema Conversion Tool (AWS SCT) to log the table metadata to an Apache Hive metastore. Use Amazon EventBridge Scheduler to update the metastore every day.
B. Schedule an AWS Glue crawler to run every day on the table by using the Java Database Connectivity (JDBC) driver. Configure the crawler to update an AWS Glue Data Catalog.
C. Schedule an AWS Glue crawler to run every day on the table. Configure the crawler to update an Apache Hive metastore.
D. Use AWS DataSync to log the table metadata to an AWS Glue Data Catalog. Use an AWS Glue crawler to update the Data Catalog every day.

Answer: B

Explanation:
An AWS Glue crawler can connect to Amazon Redshift via JDBC, detect schema changes, and automatically update the AWS Glue Data Catalog on a schedule, providing the required ongoing schema tracking.

NEW QUESTION # 68
A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data sources.
The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.
B. Create a PySpark program in AWS Lambda to extract, transform, and load the data into the S3 bucket.
C. Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
D. Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.

Answer: C

NEW QUESTION # 69
......

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