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Snowflake DEA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Ingestion and Sourcing	20-25%	- Use Snowflake ingestion methods: Snowpipe, COPY INTO, External Functions - Ingest data from various sources: data lakes, APIs, on-premises systems, cloud storage - Handle different data formats: structured, semi-structured, unstructured - Design and implement continuous and batch ingestion pipelines
Data Pipeline Architecture and Design	15-20%	- Design scalable, reliable, and maintainable data pipelines - Integrate with external tools and platforms: orchestration, BI, ML - Apply design patterns for data engineering workloads - Build end-to-end near real-time streaming solutions
Data Sharing and Collaboration	5-10%	- Work with Snowflake Data Marketplace and external data providers - Implement secure data sharing and data exchanges - Design multi-tenant and cross-account data architectures
Data Governance, Security, and Compliance	10-15%	- Enforce data quality and governance standards - Apply data protection: encryption, masking, row-level security - Manage data lineage, cataloging, and compliance policies - Implement access control: RBAC, authentication, authorization
Performance Optimization and Compute Management	15-20%	- Optimize query performance: clustering, partitioning, materialized views - Use search optimization and query acceleration services - Manage virtual warehouses: sizing, scaling, multi-cluster, cost control - Monitor and tune workloads and resource utilization
Data Transformation and Processing	20-25%	- Transform and enrich data using SQL, JavaScript, Python, and Snowpark - Manage data quality, validation, and deduplication - Process semi-structured data: JSON, Avro, Parquet, ORC - Implement ELT/ETL workflows using Streams, Tasks, and Dynamic Tables

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q270-Q275):

NEW QUESTION # 270
You have a table 'EMPLOYEE with a column 'SALARY'. You need to mask the 'SALARY column for all roles EXCEPT for the PAYROLL ADMIN' role. You create the following masking policy:

You then apply this masking policy to the 'SALARY column: ALTER TABLE EMPLOYEE MODIFY COLUMN SALARY SET MASKING POLICY salary_mask; However, users who are not granted the 'PAYROLL _ADMIN' role are still seeing the actual salary values. What is the MOST likely reason for this and how can you resolve it?

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

Answer: B

Explanation:
The most common reason for masking policies not to function as expected is due to direct privileges on the underlying table bypassing the policy. Even with a masking policy in place, if a user has direct SELECT privilege on the table, they will see the unmasked data. Revoking the SELECT privilege on the table forces users to access data through roles where masking policies apply. Option C, UNMASK privilege could also be a cause, but Option A is more likely as it's a common oversight during setup.

NEW QUESTION # 271
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. 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: C

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 # 272
You are loading data from an S3 bucket into a Snowflake table using the COPY INTO command. The source data contains dates in various formats (e.g., 'YYYY-MM-DD', 'MM/DD/YYYY', 'DD-Mon-YYYY'). You want to ensure that all dates are loaded correctly and consistently into a DATE column in Snowflake. Which of the following COPY INTO options and commands is the MOST appropriate to handle this?

A. Use the 'DATE FORMAT option in the COPY INTO command with a single format string that covers all possible date formats.
B. Utilize the 'DATE' function with explicit format strings inside a Snowpipe transformation pipeline. This involves pattern matching using 'CASE WHEN' statements to identify date formats before converting to the DATE data type.
C. Use the 'ON_ERROR = 'SKIP FILE" option to skip files with invalid date formats.
D. Use the 'VALIDATE(O)' command before the COPY INTO command to identify files with invalid date formats and then process them separately.
E. Use the 'STRTOK TO DATE function within a SELECT statement in a Snowpipe transformation to dynamically parse the dates based on different patterns.

Answer: B

Explanation:
Option E is the most robust. It uses regular expressions for pattern matching to accurately determine the format of each date and then uses TO DATE with the correct format string. This ensures consistent conversion to Snowflake's DATE data type. While Option D aims to use STRTOK TO DATE, Snowflake doesn't offer dynamic date parsing during data loading, so each date pattern will be handled manually using CASE statement approach mentioned in the option E, making it more suitable for a varied array of patterns. Options A, B, and C are inadequate. A single 'DATE FORMAT won't handle multiple formats. B skips files, leading to data loss. C requires manual intervention and doesn't automate the loading process.

NEW QUESTION # 273
You are designing a Snowflake alert system for a data pipeline that loads data into a table named 'ORDERS'. You want to trigger an alert if the number of rows loaded per hour falls below a threshold, indicating a potential issue with the data source. You need to create an alert that is triggered based on the count of rows. Consider the code snippet below and the additional requirements. Assume that the table exists and the connection is successful.

A. Create a Snowflake task that runs every hour, executes a query to count the rows loaded in the past hour and triggers an alert using 'SYSTEM$SEND_EMAIC if the count is below the threshold. No need to create a Snowflake alert.
B. Create a Snowflake Stream on the 'ORDERS' table. Then create an Alert that triggers based on the metadata column, comparing it to the threshold value. This allows for real-time monitoring of data changes.
C. Use Snowflake's Resource Monitor feature and adjust the credit quota to trigger an alert if the credit usage exceeds the threshold for the virtual warehouse processing data pipeline, indirectly indicating that performance is degraded or data volume has changed significantly.
D. You cannot create alerts based on a rolling hourly window within Snowflake. Alerts can only be based on fixed time intervals.
E. Create a Snowflake Alert that executes a SQL query to count the number of rows loaded into the 'ORDERS table within the last hour. Configure the alert to trigger when the count is below the defined threshold. Use a Notification Integration to send alerts to a monitoring system.

Answer: E

Explanation:
Option D directly addresses the requirements by creating a Snowflake Alert that monitors the row count within the last hour and triggers when the threshold is breached. The alert can then be integrated with a notification system for timely alerts. Option A is incorrect as Snowflake supports alert conditions using rolling windows via SQL functions (e.g., "last_altereff). Option B describes a task-based solution, not using the Snowflake alert object directly. Option C is incorrect because streams track changes, not the total number of rows loaded within a period. Option E is an indirect method and does not precisely measure rows inserted; therefore, it does not satisfy the requirement of alerting based on the number of rows loaded.

NEW QUESTION # 274
You are tasked with ingesting data from an external stage into Snowflake. The data is in JSON format and compressed using GZIP. The JSON files contain nested arrays. You need to create a file format object that Snowflake can use to properly parse the dat a. Which of the following options represents the MOST efficient and correct file format definition to achieve this? Assume the stage is already created and accessible.

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

Answer: B

Explanation:
Option B is the most efficient and correct. The COMPRESSION = 'GZIP' parameter is necessary to handle the GZIP compression. 'STRIP OUTER ARRAY = TRUE is crucial when dealing with JSON files containing nested arrays or a single outer array, which is a common scenario for data coming from external sources. It allows Snowflake to correctly parse each element of the array as a separate row. Option A is incorrect because it doesn't address the potential outer array structure. Option C is incorrect because 'ENABLE OCTAL' is not relevant to the array structure issue. Option D is incorrect as it lacks the compression parameter. Option E is also suitable , but gzip is better performance than AUTO.

NEW QUESTION # 275
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