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Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q162-Q167):

162. Frage
How do row access policies and Dynamic Data Masking affect the creation and maintenance of reports and dashboards?

A. Both policies restrict data visibility for better security.
B. They offer unrestricted data visibility for all users.
C. Dynamic Data Masking doesn't influence dashboard creation.
D. Row access policies limit data visibility based on user privileges.

Antwort: D

Begründung:
Row access policies restrict data visibility based on user privileges, ensuring better security in creation and maintenance of reports and dashboards.

163. Frage
A Snowflake data analyst needs to create a secure view called "masked customer data' based on an existing table named 'customer data'. The requirement is to mask the 'email' column for all users except those with the "DATA ADMIN' role. Also, only users with the 'ANALYST' role should be able to query any data from the view. The masking policy 'email_mask' has already been created. Which of the following sequence of commands correctly implements this requirement?

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

Antwort: C

Begründung:
Option C correctly implements the requirements. First, a 'SECURE VIEW' is created to ensure data security. Then, the 'ALTER VIEW' command correctly applies the masking policy to the column of the view. Finally, 'GRANT SELECT ensures that only the 'ANALYST role can query data from the view. Option B attempts to alter the underlying table directly which isn't the intention, and uses an invalid command 'MODIFY COLUMN'. Option A does not create a secure view. Option D uses incorrect syntax 'MODIFY COLUMN' for altering view. Option E try to alter the masked_customer_data, which is not a TABLE instead it is a View.

164. Frage
A marketing team wants to visualize customer engagement across different channels (email, website, social media). They have engagement data stored in a Snowflake table 'CUSTOMER ENGAGEMENT' with columns 'CUSTOMER ID, 'CHANNEL', 'EVENT TYPE', 'EVENT TIMESTAMP', and 'EVENT VALUE' (representing engagement score). They need a dashboard that efficiently displays the total engagement score per channel for the last 7 days, allowing for quick drill-down into individual customer behavior. Which approach provides the MOST optimized query and view structure for this requirement, prioritizing performance for a large dataset?

A. Use a stored procedure to calculate the engagement score and update a temporary table daily. The dashboard queries this temporary table.
B. Create a view that directly aggregates data from 'CUSTOMER_ENGAGEMENT for the last 7 days in the dashboard query itself. Use 'GROUP BY CHANNEL' and ' SUM(EVENT_BALUE)'.
C. Load the entire 'CUSTOMER ENGAGEMENT table into the dashboard tool's internal memory and perform the aggregation and filtering there.
D. Create a materialized view 'MV CUSTOMER ENGAGEMENT that pre-aggregates the total engagement score per channel for the last 7 days, refreshed daily using a Snowflake task. The dashboard query then selects directly from this materialized view.
E. Create a standard view that aggregates data from 'CUSTOMER ENGAGEMENT using 'GROUP BY CHANNEL' , but without filtering for the last 7 days. Filter for the last 7 days within the dashboard query.

Antwort: D

Begründung:
Materialized views (B) are the most performant option because they pre-compute and store the aggregated data. A daily refresh using a Snowflake task ensures the data is up-to-date. Option A requires the aggregation to be performed every time the dashboard is loaded. Option C is less efficient than B because it filters after aggregation. Option D is not scalable for large datasets. Option E adds unnecessary complexity and potential performance bottlenecks with the stored procedure and temporary table.

165. Frage
You are investigating why a Snowflake data replication process between two regions is experiencing significant lag. You need to collect data to determine if the issue stems from network latency, insufficient warehouse resources in the target region, or data transformation bottlenecks. Select the data collection methods that will provide the MOST relevant insights.

A. Run traceroute commands between the source and target regions to measure network latency.
B. Restart the data replication process.
C. Analyze the query history in the target region to identify slow-running transformation queries that might be bottlenecking the replication process.
D. Monitor the replication lag metrics (e.g., DATABASE REPLICATION_LAG, TABLE REPLICATION_LAG) exposed through Snowflake system functions and the web interface for both the source and target regions.
E. Monitor the CPU utilization of the virtual machines running the Snowflake service in both regions.

Antwort: A,C,D

Begründung:
Options A, B, and C provide specific data points relevant to the identified potential causes. Monitoring replication lag metrics (A) directly quantifies the lag. Traceroute (B) measures network latency. Analyzing query history (C) identifies transformation bottlenecks. Restarting the process (D) might temporarily resolve the issue but doesn't address the root cause. Snowflake manages the underlying infrastructure; therefore, monitoring VM CPU utilization (E) is not something that a data analyst has access to or is needed for the diagnostic in this case. The Snowflake service runs and manages the queries.

166. Frage
You're designing a data pipeline in Snowflake to process order data'. The raw order data, including customer information, is stored in a JSON format within a single 'RAW ORDERS table. Due to privacy regulations, you need to mask the customer's email addresses before loading the data into a 'CLEANED ORDERS' table, while maintaining referential integrity. Furthermore, you want to track the data lineage (which raw order resulted in which cleaned order) in a separate 'ORDER LINEAGE' table. Which of the following approaches achieves these requirements effectively and efficiently? (Select TWO)

A. Create a masking policy on the 'email' column of the 'RAW_ORDERS' table. Then, create a task to copy all data from 'RAW_ORDERS to 'CLEANED_ORDERS. Finally, create a separate task that runs after the copying task to populate the 'ORDER_LINEAGE table with relevant mappings.
B. Create a view on the 'RAW ORDERS table that masks the email address. Load the raw order data into 'RAW ORDERS table using Snowpipe and a stream. Use a task chained to the stream to ingest the view into 'CLEANED_ORDERS and populate lineage information into 'ORDER_LINEAGE table.
C. Create a masking policy on the 'email' column in 'CLEANED ORDERS'. Create a stream on RAW ORDERS, then create a task to insert data from 'RAW ORDERS' into 'CLEANED ORDERS' and to INSERT the order to 'ORDER LINEAGE
D. Use a Snowpipe to load data into 'RAW ORDERS. Create a stream on 'RAW ORDERS'. Create a task chained to the stream that reads the new records from the stream, masks the email, inserts the cleaned data into 'CLEANED_ORDERS' table and inserts lineage information into the 'ORDER _ LINEAGE table.
E. Implement a stored procedure that reads data from 'RAW ORDERS, masks the email using SHA256, inserts the cleaned data into 'CLEANED ORDERS', and simultaneously inserts lineage information into 'ORDER LINEAGE. Schedule this stored procedure to run periodically.

Antwort: D,E

Begründung:
Option B: A stored procedure provides the most control over the masking process and lineage tracking, ensuring data is masked during the transfer. It allows you to perform all operations (masking, inserting into 'CLEANED ORDERS, and inserting into 'ORDER LINEAGE) within a single transaction. Option C: Snowpipe loading data to RAW_ORDERS, using streams to capture the data, and scheduling the tasks to read from the stream, while masking and adding lineage information provides automation and efficiency. Option A is incorrect, as it copies all data using a task into 'CLEANED_ORDERS and then masks the email on RAW_ORDERS after data is already in 'CLEANED_ORDERS'. Option D is incorrect as masking policy is on rather than Option E requires a view to mask email address and is not an effective approach to apply a masking policy. Also, its not the right procedure to load view into a table.

167. Frage
......

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