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Snowflake DEA-C01 copyright copyright Topics:

Topic	Details
Topic 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.
Topic 2	Data Transformation: The SnowPro Advanced: Data Engineer copyright 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 3	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 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.
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.

Snowflake SnowPro Advanced: Data Engineer Certification copyright Sample Questions (Q113-Q118):

NEW QUESTION # 113
A company has an on-premises PostgreSQL database that contains customer data. The company wants to migrate the customer data to an Amazon Redshift data warehouse. The company has established a VPN connection between the on-premises database and AWS.
The on-premises database is continuously updated. The company must ensure that the data in Amazon Redshift is updated as quickly as possible.
Which solution will meet these requirements?

A. Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to perform a full load of the database to Amazon Redshift every night.
B. Use the pg_dump utility to generate a backup of the PostgreSQL database. Upload the backup to an Amazon S3 bucket. Use the COPY command to import the data into Amazon Redshift.
C. Use the pg_dump utility to generate a backup of the PostgreSQL database. Use the AWS Schema Conversion Tool (AWS SCT) to upload the backup to Amazon Redshift. Set up a cron job to perform a backup. Upload the backup to Amazon Redshift every night.
D. Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to use the change data capture (CDC) feature.

Answer: D

Explanation:
AWS DMS can perform an initial full load of your on-premises PostgreSQL data into Redshift and then continuously capture and apply changes (CDC) over the VPN. This approach keeps your Redshift tables up to date with minimal latency and operational overhead.

NEW QUESTION # 114
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys.
Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?

A. Create an AWS Glue connection to the Oracle database. Create an AWS Glue bookmark job to ingest the data incrementally and to write the data to Amazon S3 in Parquet format.
B. Create an Oracle database in Amazon RDS. Use AWS Database Migration Service (AWS DMS) to migrate the on-premises Oracle database to Amazon RDS. Configure triggers on the tables to invoke AWS Lambda functions to write changed records to Amazon S3 in Parquet format.
C. Create an Apache Sqoop job in Amazon EMR to read the data from the Oracle database.
Configure the Sqoop job to write the data to Amazon S3 in Parquet format.
D. Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.

Answer: D

Explanation:
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.S3.html

NEW QUESTION # 115
A data engineer is writing a query to join two tables in Amazon Athena. The data engineer needs to choose the correct join order for the tables to optimize query performance. Which solution will meet these requirements?

A. Use AWS Glue to pre-process the tables before performing the join.
B. Use table statistics to automatically determine the join order.
C. Specify the larger table on the left side of the join and the smaller table on the right side of the join.
D. Specify the smaller table on the left side of the join and the larger table on the right side of the join.

Answer: D

Explanation:
Amazon Athena (based on Presto/Trino) performs joins more efficiently when the smaller table is placed on the left side because it is used as the build side of the join and can be loaded into memory. This minimizes data shuffling and improves performance when joining with a larger table on the right side.

NEW QUESTION # 116
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?

A. UPDATE cities_usa SET (city, state) = (SELECT city, state FROM cities_world WHERE country='usa');
B. MOVE city, state FROM cities_world TO cities_usa WHERE country='usa';
C. INSERT INTO cities_usa (city,state) SELECT city, state FROM cities_world WHERE country='usa';
D. INSERT INTO cities_usa SELECT city, state FROM cities_world WHERE country='usa';

Answer: C

NEW QUESTION # 117
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?

A. Replace sum(sales_amount) with count(*) for the aggregation.
B. Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.
C. Remove the GROUP BY clause.
D. Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023.

Answer: B

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/select.html

NEW QUESTION # 118
......

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Reliable DEA-C01 Dumps Book | DEA-C01 Valid Test Bootcamp

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DEA-C01 Valid Test Bootcamp, Test Certification DEA-C01 Cost

Snowflake DEA-C01 copyright copyright Topics:

Snowflake SnowPro Advanced: Data Engineer Certification copyright Sample Questions (Q113-Q118):

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