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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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.
Topic 3	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 4	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 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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q206-Q211):

NEW QUESTION # 206
A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs.
Recently, customers reported that a query on one of the Athena tables did not return any data. A data engineer must resolve the issue.
Which combination of troubleshooting steps should the data engineer take? (Choose two.)

A. Confirm that Athena is pointing to the correct Amazon S3 location.
B. Delete and recreate the problematic Athena table.
C. Increase the query timeout duration.
D. Use the MSCK REPAIR TABLE command.
E. Restart Athena.

Answer: A,D

Explanation:
One common issue could be that the table's S3 location is incorrect or has changed. The data engineer should check that the Athena table is correctly referencing the S3 bucket or path where the CloudTrail logs are stored.
If new partitions have been added to the S3 bucket (such as logs for a new year), but these partitions haven't been reflected in the Athena table, running the MSCK REPAIR TABLE command will load new partitions into the table's metadata, ensuring that Athena can query the new data.
A query timeout would not cause data to be missing from the results. This option does not address the issue of missing data in the query results.
Athena is a serverless query service, so there is no option to "restart" Athena. This is not applicable.
While deleting and recreating the table could resolve the issue, it's generally not necessary and is more disruptive. The problem can often be resolved with less intrusive steps like checking the S3 location and repairing partitions.

NEW QUESTION # 207
A stock broking firm is working on a new application that stores and updates stock market data in Amazon DynamoDB. Different applications will track these changes in real-time. The team is looking for a tool to capture this changing data, which should be available for processing for up to one year. Streaming data should be cleansed, transformed, and stored in Amazon S3 data lakes.
Which service can be considered?

A. Use Kinesis Data Streams for DynamoDB as a streaming model. Process stream records with DynamoDB Streams Kinesis adapter.
B. Use Kinesis Data Streams for DynamoDB as a streaming model. Process stream records with AWS Glue streaming ETL.
C. Use DynamoDB Streams as a streaming model. Process stream records with AWS Data Firehose.
D. Use DynamoDB Streams as a streaming model. Process stream records with AWS Lambda.

Answer: B

Explanation:
Amazon DynamoDB offers two streaming models for change data capture,
Kinesis Data Streams for DynamoDB: Data retention with this model is 1 year. It can process stream records using AWS Lambda, Amazon Managed Service for Apache Flink, Kinesis data firehose, or AWS Glue streaming ETL. AWS Glue Streaming ETL can be used to cleanse, and transform streaming data and store it in either Amazon S3 data lakes or JDBC data stores.
DynamoDB Streams: This model retains data for 24 hours. It can process stream records using AWS Lambda and the DynamoDB Streams Kinesis adapter.
Reference:
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/streamsmain.html

NEW QUESTION # 208
A company has developed several AWS Glue extract, transform, and load (ETL) jobs to validate and transform data from Amazon S3. The ETL jobs load the data into Amazon RDS for MySQL in batches once every day. The ETL jobs use a DynamicFrame to read the S3 data.
The ETL jobs currently process all the data that is in the S3 bucket. However, the company wants the jobs to process only the daily incremental data.
Which solution will meet this requirement with the LEAST coding effort?

A. Enable job bookmarks for the ETL jobs to update the state after a run to keep track of previously processed data.
B. Configure the ETL jobs to delete processed objects from Amazon S3 after each run.
C. Create an ETL job that reads the S3 file status and logs the status in Amazon DynamoDB.
D. Enable job metrics for the ETL jobs to help keep track of processed objects in Amazon CloudWatch.

Answer: A

Explanation:
AWS Glue job bookmarks are designed to handle incremental data processing by automatically tracking the state.

NEW QUESTION # 209
A company adds new data to a large CSV file in an Amazon S3 bucket every day. The file contains company sales data from the previous 5 years. The file currently includes more than
5,000 rows. The CSV file structure is shown below with sample data:

The company needs to use Amazon Athena to run queries on the CSV file to fetch data from a specific time period.
Which solution will meet this requirement MOST cost-effectively?

A. Create an Amazon RDS database. Create a table named SALES that matches the schema of the CSV file. Create an index on the SALE_DATE column. Create an AWS Lambda function to load the CSV data into the RDS database. Use S3 Event Notifications to invoke the Lambda function.
B. Launch an Amazon EMR cluster. Specify AWS Glue Data Catalog as the default Apache Hive metastore. Use Presto in Trino to run queries on the data.
C. Write an Apache Spark script to convert the CSV data to JSON format. Create an AWS Glue job to run the script every day. Catalog the JSON data in AWS Glue. Run the Athena queries on the JSON data.
D. Use prefixes to partition the data in the S3 bucket. Use the SALE_DATE column to create a partition for each day. Catalog the data in AWS Glue and ensure that the partitions are added.
Update the Athena queries to use the new partitions.

Answer: D

Explanation:
Partitioning the S3 data by SALE_DATE (daily prefixes) lets Athena prune non-matching partitions so it scans only the requested time range, minimizing bytes scanned and cost while working directly on the existing CSVs. Cataloging the partitions in AWS Glue enables efficient querying with minimal operational overhead.

NEW QUESTION # 210
Streams cannot be created to query change data on which of the following objects? [Select All that Apply]

A. Standard tables, including shared tables.
B. Views, including secure views
C. External tables
D. Directory tables
E. Query Log Tables

Answer: E

Explanation:
Explanation
Streams supports all the listed objects except Query Log tables.

NEW QUESTION # 211
......

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