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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 (Q33-Q38):

NEW QUESTION # 33
Which callback function is required within a JavaScript User-Defined Function (UDF) for it to execute successfully?

A. initialize ()
B. finalize ()
C. handler
D. processRow ()

Answer: D

Explanation:
Explanation
The processRow () callback function is required within a JavaScript UDF for it to execute successfully. This function defines how each row of input data is processed and what output is returned. The other callback functions are optional and can be used for initialization, finalization, or error handling.

NEW QUESTION # 34
A Data Engineer needs to know the details regarding the micro-partition layout for a table named invoice using a built-in function.
Which query will provide this information?

A. CALL SYSTEM$CLUSTERING_INFORMATION ('Invoice');
B. SELECT $CLUSTERXNG_INFQRMATION ('Invoice')'
C. CALL $CLUSTERINS_INFORMATION('Invoice');
D. SELECT SYSTEM$CLUSTERING_INTFORMATICII ('Invoice' ) ;

Answer: D

Explanation:
Explanation
The query that will provide information about the micro-partition layout for a table named invoice using a built-in function is SELECT SYSTEM$CLUSTERING_INFORMATION('Invoice');. The SYSTEM$CLUSTERING_INFORMATION function returns information about the clustering status of a table, such as the clustering key, the clustering depth, the clustering ratio, the partition count, etc. The function takes one argument: the table name in a qualified or unqualified form. In this case, the table name is Invoice and it is unqualified, which means that it will use the current database and schema as the context. The other options are incorrect because they do not use a valid built-in function for providing information about the micro-partition layout for a table. Option B is incorrect because it uses $CLUSTERING_INFORMATION instead of SYSTEM$CLUSTERING_INFORMATION, which is not a valid function name. Option C is incorrect because it uses CALL instead of SELECT, which is not a valid way to invoke a table function.
Option D is incorrect because it uses CALL instead of SELECT and $CLUSTERING_INFORMATION instead of SYSTEM$CLUSTERING_INFORMATION, which are both invalid.

NEW QUESTION # 35
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?

A. Use the AWS Glue Data Catalog.
B. Use Amazon EMR and Apache Ranger.
C. Use a Hive metastore on an EMR cluster.
D. Use a metastore on an Amazon RDS for MySQL DB instance.

Answer: A

Explanation:
https://aws.amazon.com/blogs/big-data/metadata-classification-lineage-and-discovery-using- apache-atlas-on-amazon-emr/

NEW QUESTION # 36
A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes.
A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake.
Which solution will capture the changed data MOST cost-effectively?

A. Ingest the data into Amazon RDS for MySQL. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.
B. Create an AWS Lambda function to identify the changes between the previous data and the current data. Configure the Lambda function to ingest the changes into the data lake.
C. Use an open source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data.
D. Ingest the data into an Amazon Aurora MySQL DB instance that runs Aurora Serverless. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.

Answer: C

NEW QUESTION # 37
A Data Engineer is trying to load the following rows from a CSV file into a table in Snowflake with the following structure:

....engineer is using the following COPY INTO statement:

However, the following error is received.

Which file format option should be used to resolve the error and successfully load all the data into the table?

A. ESC&PE_UNENGLO9ED_FIELD = ''
B. FIELD_DELIMITER = ","
C. FIELD OPTIONALLY ENCLOSED BY = " "
D. ERROR_ON_COLUMN_COUKT_MISMATCH = FALSE

Answer: C

Explanation:
Explanation
The file format option that should be used to resolve the error and successfully load all the data into the table is FIELD_OPTIONALLY_ENCLOSED_BY = '"'. This option specifies that fields in the file may be enclosed by double quotes, which allows for fields that contain commas or newlines within them. For example, in row 3 of the file, there is a field that contains a comma within double quotes: "Smith Jr., John". Without specifying this option, Snowflake will treat this field as two separate fields and cause an error due to column count mismatch. By specifying this option, Snowflake will treat this field as one field and load it correctly into the table.

NEW QUESTION # 38
......

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New DEA-C01 Exam Online & Examcollection DEA-C01 Questions Answers

Snowflake DEA-C01 Exam Syllabus Topics:

Free PDF 2025 Authoritative DEA-C01: New SnowPro Advanced: Data Engineer Certification Exam Exam Online

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q33-Q38):

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