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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	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	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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q301-Q306):

NEW QUESTION # 301
As Data Engineer, you have requirement to Load set of New Product Files containing Product rele-vant information into the Snowflake internal tables, Later you analyzed that some of the Source files are already loaded in one of the historical batch & for that you have prechecked Metadata col-umn LAST_MODIFIED date for a staged data file & found out that LAST_MODIFIED date is older than 64 days for few files and the initial set of data was loaded into the table more than 64 days earlier, Which one is the best approach to Load Source data files with expired load metadata along with set of files whose metadata might be available to avoid data duplication?

A. Since the initial set of data for the table (i.e. the first batch after the table was created) was loaded, we can simply use the COPY INTO command to load all the product files with the known load status irrespective of their column LAST_MODIFIED date values.
B. Set the FORCE option to load all files, ignoring load metadata if it exists.
C. The COPY command cannot definitively determine whether a file has been loaded al-ready if the LAST_MODIFIED date is older than 64 days and the initial set of data was loaded into the table more than 64 days earlier (and if the file was loaded into the table, that also occurred more than 64 days earlier). In this case, to prevent accidental reload, the command skips the product files by default.
D. To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true.

Answer: D

Explanation:
Explanation
To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true. The copy option references load metadata, if available, to avoid data duplication, but also at-tempts to load files with expired load metadata.
Alternatively, set the FORCE option to load all files, ignoring load metadata if it exists. Note that this option reloads files, potentially duplicating data in a table.
Please refer the Example as mentioned in the link below:
https://docs.snowflake.com/en/user-guide/data-load-considerations-load.html#loading-older-files

NEW QUESTION # 302
A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use AWS Glue to store the data in Amazon RDS for querying.
B. Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.
C. Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.
D. Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.

Answer: A

NEW QUESTION # 303
Mark the incorrect statement when Data Engineer implement Automating Continuous Data Loading Using Cloud Messaging?

A. When a pipe is paused, event messages received for the pipe enter a limited retention period. The period is 14 days by default. If a pipe is paused for longer than 14 days, it is considered stale.
B. Notifications identify the cloud storage event and include a list of the file names. They do not include the actual data in the files.
C. Automated Snowpipe uses event notifications to determine when new files arrive in monitored cloud storage and are ready to load.
D. Triggering automated Snowpipe data loads using S3 event messages is supported by Snowflake accounts hosted on Cloud Platform like AWS, GCP or AZURE.

Answer: D

Explanation:
Explanation
Triggering automated Snowpipe data loads using S3 event messages is supported by Snowflake ac-counts hosted on Amazon Web Services (AWS) only.
Rest is correct statements.

NEW QUESTION # 304
Partition columns optimize query performance by pruning out the data files that do not need to be scanned (i.e.
partitioning the external table). Which pseudocolumn of External table evaluate as an expression that parses the path and/or filename information.

A. METADATA$COLUMNNAME
B. METADATA$ROW_NUMBER
C. METADATA$FILEPATH
D. METADATA$FILENAME

Answer: D

Explanation:
Explanation
METADATA$FILENAME
A pseudocolumn that identifies the name of each staged data file included in the external table, in-cluding its path in the stage.
An external table creator defines partition columns in a new external table as expressions that parse the path and/or filename information stored in the METADATA$FILENAME pseudocolumn. A partition consists of all data files that match the path and/or filename in the expression for the parti-tion column.

NEW QUESTION # 305
A company uses AWS Glue ETL pipelines to process data. The company uses Amazon Athena to analyze data in an Amazon S3 bucket.
To better understand shipping timelines, the company decides to collect and store shipping and delivery dates in addition to order data. The company adds a data quality check to ensure that shipping date is greater than order date and that delivery date is greater than shipping date.
Orders that fail the quality check must be stored in a second S3 bucket.
Which solution will meet these requirements MOST cost-effectively?

A. Use Athena to query all three date columns, and compare the columns.
B. Use AWS Glue Data Quality to create a custom rule that uses the three date columns.
C. Use the AWS Glue DataBrew DATEDIFF function to create two additional columns. Check the new columns.
D. Use an AWS Glue crawler to populate an AWS Glue Data Catalog. Use the three date columns to create a filter.

Answer: B

Explanation:
AWS Glue Data Quality is the most cost-effective choice because it is built for ETL data validation and supports custom rules that compare multiple columns, such as ensuring shipping date is after order date and delivery date is after shipping date. It can also identify the specific records that fail validation so those failed orders can be written to a separate Amazon S3 bucket as part of the ETL flow.
Reference:
https://docs.aws.amazon.com/glue/latest/dg/glue-data-quality.html
https://docs.aws.amazon.com/glue/latest/dg/tutorial-data-quality.html
https://docs.aws.amazon.com/glue/latest/dg/dqdl-rule-types-CustomSql.html

NEW QUESTION # 306
......

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