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Snowflake DEA-C02 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Engineering Fundamentals	- Data pipelines concepts and patterns - Snowflake architecture for data engineering
Topic 2: Data Transformation and Processing	- Streams and Tasks for ELT pipelines - Handling semi-structured data (JSON, Avro, Parquet) - SQL-based transformations in Snowflake
Topic 3: Security and Data Governance	- Data masking and encryption - Secure data sharing - Role-based access control (RBAC)
Topic 4: Performance and Optimization	- Clustering and partition strategies - Warehouse sizing and scaling - Query optimization techniques
Topic 5: Data Ingestion and Integration	- Snowpipe usage and automation - Staging data and loading mechanisms - Batch and streaming ingestion approaches

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q334-Q339):

NEW QUESTION # 334
You are tasked with loading a large CSV file (1 T B) into Snowflake. The file contains data for the past 5 years, partitioned by year in the filename (e.g., 'data 2019.csv', 'data 2020.csv', etc.). You need to minimize data loading time and ensure data quality. You have a Snowflake virtual warehouse 'XSMALL' and a stage 'my_stage'. Which of the following strategies would be MOST effective?

A. Create multiple named file formats each with a unique 'SKIP HEADER value matching the number of header rows in each file. Load using a single 'COPY' command referencing each file format specifically.
B. Increase the virtual warehouse size to 'LARGE, use a single 'COPY command to load all files with the ERROR = ABORT STATEMENT option. Create a file format with "SKIP HEADER = 1' and 'TRIM SPACE = TRUE.
C. Load each file individually using a separate 'COPY' command with 'VALIDATION MODE = RETURN ERRORS to check for data quality issues before loading the next file. Use the 'XSMALL' warehouse for all loads.
D. Increase the virtual warehouse size to 'LARGE, use a single 'COPY command to load all files with the ERROR = CONTINUE option. Implement data quality checks post-load using SQL queries.
E. Use Snowpipe with auto-ingest enabled. Ensure your cloud storage event notifications are properly configured. Create a file format with 'SKIP HEADER = 1' and 'TRIM SPACE = TRUE Leave the warehouse as 'XSMALL' to control costs.

Answer: D

Explanation:
Option B is the most effective. Increasing the warehouse size to 'LARGE allows for parallel processing and faster loading. ERROR = CONTINUE ensures that the load process doesn't halt on minor errors, and post-load data quality checks are more efficient. A allows validation during load which slows down the process significantly. C will halt the entire process upon encountering an error. D is not suitable because it will be throttled by the XSMALL warehouse, which is not good for initial data loading. E isn't realistic as files should have a standard headen

NEW QUESTION # 335
You are tasked with creating a Python script to load data from a CSV file stored in an AWS S3 bucket into a Snowflake table. You have the following requirements: 1. Use the 'COPY INTO' command for efficient data loading. 2. Handle potential schema evolution in the CSV file (e.g., new columns being added). 3. Automatically create the target table if it doesn't exist, inferring the schema from the CSV. Which combination of Snowflake Python connector functionalities and 'COPY INTO' options would best address these requirements, assuming you have an AWS IAM role configured for Snowflake access to S3?

A. Option C
B. Option A
C. Option D
D. Option B
E. Option E

Answer: A

Explanation:
Option C is the most effective solution. 'ON_ERROR = CONTINUE allows the 'COPY INTO' command to proceed even if it encounters errors (e.g., due to extra columns). = CASE INSENSITIVE' handles slight variations in column names between the CSV and the Snowflake table. Setting = TRUE in the Snowflake session (this is a hypothetical feature, Snowflake doesn't directly have AUTO_CREATE_TABLE in COPY INTO, so this tests knowledge of workarounds and understanding of COPY INTO capabilities). Other options have drawbacks. Option A doesn't directly use COPY INTO and is generally slower. Option B requires a predefined schema, negating the ability to handle schema evolution. Option D is used with in memory stream, rather s3. Option E involves manual schema inspection and construction, which is cumbersome and error-prone.

NEW QUESTION # 336
You have a Snowpark DataFrame 'df_products' with columns 'product id', 'category', and 'price'. You need to perform the following transformations in a single, optimized query using Snowpark Python: 1. Filter for products in the 'Electronics' or 'Clothing' categories. 2. Group the filtered data by category. 3. Calculate the average price for each category. 4. Rename the aggregated column to 'average_price'. Which of the following code snippets demonstrates the most efficient way to achieve this?

A. Option A
B. Option C
C. Option B
D. Option D
E. Option E

Answer: C

Explanation:
Option B is the most efficient and correct. It uses 'col()' from 'snowflake.snowpark.functions' to properly reference the 'category' and 'price' columns, uses 'isin()' for a more concise and efficient filtering of multiple category values, groups by the category using and calculates the average price with 'avg(col('price')).as_('average_price')'. Option A, C, and D are syntactically incorrect or less efficient ways to accomplish the same task within Snowpark. Option E is incorrect because it utilizes 'to_pandas()' which returns the result as a Pandas DataFrame rather than a Snowpark DataFrame, failing to adhere to the Snowpark environment. While Option D is very similar, it lacks the proper syntax for specifying column references with 'col('category')' in the groupBy and 'col('price')' in the avg function.

NEW QUESTION # 337
You are using the Snowflake Developer API to automate the creation and management of masking policies. You need to create a masking policy that masks an email address using SHA256 hashing. You also want to ensure that the policy can be applied to multiple tables and columns without modification. Assuming you have already established a connection to Snowflake using the Developer API, which of the following code snippets BEST demonstrates how to create and apply this masking policy using Python?

A. Option A
B. Option C
C. Option D
D. Option B
E. Option E

Answer: E

Explanation:
Option E is the best approach because: It uses 'CREATE OR REPLACE MASKING POLICY', which allows you to update the policy definition if it already exists. It defines the input parameter as 'ANY' and then casts it to 'VARCHAR within the SHA2 function. This makes the policy more versatile and applicable to columns with different data types (as long as they can be cast to VARCHAR). Using CREATE OR REPLACE MASKING POLICY ensure no error will be raised when the policy already exist. The 'val ANY part ensures that policy work for all type of data.

NEW QUESTION # 338
You are designing a Snowpipe pipeline to ingest data from an AWS SQS queue. The queue contains notifications about new files arriving in an S3 bucket. However, due to network issues, some notifications are delayed, causing Snowpipe to potentially miss files. Which of the following strategies, when combined, will BEST address the problem of delayed notifications and ensure data completeness?

A. Implement a Lambda function that triggers the 'SYSTEM$PIPE FORCE RESUME procedure after a certain delay.
B. Use 'VALIDATE()' function periodically to identify files that have not been loaded and trigger manual data loads for missing data.
C. Set 'MAX FILE_AGE to 'DEFAULT' and utilize the 'SYSTEM$PIPE FORCE RESUME' procedure in conjunction with a separate process that lists the S3 bucket and compares it to the files already loaded in Snowflake, loading any missing files.
D. Configure the SQS queue with a longer retention period and implement an event bridge rule with a retry policy to resend notifications.
E. Increase the 'MAX FILE AGE parameter in the Snowpipe definition and implement a periodic 'ALTER PIPE ... REFRESH' command.

Answer: C

Explanation:
Option E provides the most robust solution. Setting 'MAX FILE AGE to 'DEFAULT' ensures that Snowpipe considers all files, regardless of their age. 'SYSTEM$PIPE FORCE RESUME' can help in some cases. The key component is the supplemental process that actively compares the S3 bucket contents with the loaded data, identifying and loading any missing files due to delayed notifications. This approach guarantees data completeness even with delayed or missed SQS notifications. A,B,C,D doesn't guarantee data completeness. A alone can cause huge latency issue. B is not optimal. C addresses the SQS issue but not guaranteed to catch every case. D requires manual load intervention

NEW QUESTION # 339
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