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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q192-Q197):
NEW QUESTION # 192
You are preparing a CSV file for ingestion into Snowflake, and you need to ensure that the data types are correctly interpreted. The CSV contains a column named 'transaction_amount' that sometimes contains values with leading zeros (e.g., '00123.45'). You want to load this data into a Snowflake table where 'transaction_amount' is defined as NUMBER(IO, 2). Without modifying the CSV file itself, how can you ensure that the leading zeros are handled correctly during the COPY INTO operation?
- A. Snowflake automatically handles leading zeros in numeric fields during COPY INTO, so no special action is required.
- B. Use the 'VALIDATE option in COPY INTO to identify rows with leading zeros and manually correct them in the CSV file.
- C. Use the 'STRIP file format option to remove the leading zeros before loading.
- D. During the COPY INTO operation, use the 'TRANSFORM_COLUMN' option to cast the VARCHAR column to a NUMBER(10, 2). Snowflake will implicitly handle the leading zeros during the cast.
- E. Define the 'transaction_amount' column as VARCHAR in Snowflake, load the data, and then cast it to NUMBER(10, 2) using a transformation query, which will implicitly remove leading zeros.
Answer: D
Explanation:
Option E is the correct approach. Snowflake implicitly handles leading zeros when casting a VARCHAR column to a NUMBER type using during the COPY INTO operation. This avoids modifying the original CSV file or requiring a separate transformation step after loading. Option A is incorrect, as implicit type conversion might not always work as expected. Option B and C are incorrect because 'STRIP NULL_VALUE' is not relevant in the case of leading zeros. Option D is viable, but less efficient than handling during load time.
NEW QUESTION # 193
An organization needs to provide daily summaries of key metrics to its executive team via email. They currently have a complex SQL query that generates the report, but the query takes several minutes to run. The executive team needs the data before 8 AM daily. The data warehouse is also used for other reporting and analytics tasks throughout the day. Given these constraints, what is the MOST efficient and reliable approach to operationalize this report?
- A. Use Snowflake's Data Marketplace to find a pre-built reporting solution that matches their requirements.
- B. Create a Snowpipe to continuously ingest data into a separate table. Write a simplified SQL query on the target table to generate the report. Schedule a task to execute the query and email the results.
- C. Use a third-party ETL tool to extract the data, transform it, and generate the report. Schedule the ETL job to run nightly and email the report.
- D. Schedule a Snowflake task to execute the complex SQL query directly and email the results using Snowflake's email integration. Ensure the task runs on a large warehouse.
- E. Create a materialized view based on the complex SQL query. Schedule a task to refresh the materialized view nightly before 8 AM. Create a separate, simpler query against the materialized view to generate the report and email it.
Answer: E
Explanation:
Creating a materialized view (B) is the most efficient approach. Materialized views pre-compute the results, so the nightly task refreshes the pre-calculated data. This ensures fast report generation before 8 AM, even with a complex underlying query. Running the complex query directly (A) might exceed the time limit. Using a third-party ETL tool (C) adds unnecessary complexity. Snowpipe (D) is for continuous data ingestion, not report generation. Data Marketplace (E) is unlikely to perfectly match the custom needs.
NEW QUESTION # 194
Consider a Snowflake table 'LOG ENTRIES' with a VARIANT column 'LOG DATA' containing log messages with potentially varying structures. Some logs contain error details under the key 'error', while others do not. If 'error' exists, it contains a nested object with 'code' and 'message'. You want to extract the error 'message' when it exists, otherwise return 'No Error'. Which query accomplishes this with optimal performance?
- A.
- B.
- C.
- D.
- E.
Answer: B
Explanation:
Option D is the most robust and efficient solution. 'IF-NULLS handles cases where 'LOG DATA:error:message' is NULL (either because 'error' doesn't exist or 'message' is NULL). The explicitly casts the value to a string, ensuring consistent data type. Options A and B will return NULL when the 'error' object or the 'message' key doesn't exist without handling the null replacement. Option C will error as it does not cast value into a String from a variant data type. Option E is not valid as TRY_TO_STRING function only accepts one argument
NEW QUESTION # 195
A healthcare organization uses Snowflake to store patient data and needs to create a dashboard to monitor key performance indicators (KPIs) such as patient readmission rates, average length of stay, and patient satisfaction scores. The dashboard should allow analysts to filter data by hospital, department, and time period. Considering data governance and security best practices, which of the following approaches should you implement to ensure that analysts only have access to the data relevant to their roles and responsibilities, while also optimizing dashboard performance and maintainability? Choose all that apply.
- A. Grant all analysts full access to the entire patient data warehouse and rely on the dashboard's built-in filtering capabilities to restrict data visibility.
- B. Implement data masking techniques to redact sensitive patient information (e.g., names, addresses) within the dashboard visualizations. Ensure that data masking policies are applied consistently across all dashboards and reports.
- C. Build a single view that joins all necessary tables and use the view to power the dashboards. Apply role-based access control to the view.
- D. Create separate Snowflake roles for different analyst groups (e.g., hospital analysts, department analysts) and grant each role access only to the specific views or tables containing the data they need. Apply row-level security policies to filter data based on the user's role.
- E. Create a set of stored procedures that encapsulate the logic for retrieving and aggregating the KPI data. Grant analysts execute privileges on these stored procedures, but not direct access to the underlying tables.
Answer: B,D,E
Explanation:
Option A enforces role-based access control at the data layer, preventing unauthorized access. Option C provides a controlled interface for data access, enhancing security and simplifying data governance. Option D protects sensitive data within the dashboard. Granting full access (B) violates security principles. Single view can become unmanageable.
NEW QUESTION # 196
You are using a Snowflake Marketplace data feed that provides daily stock prices. The data is updated daily, and you need to create a process to automatically load the new data into your existing 'STOCK PRICES' table. The Marketplace data feed provides a view called 'MARKETPLACE STOCK PRICES' with columns 'DATE' (DATE), 'SYMBOL' (VARCHAR), and 'PRICE (NUMBER). Your 'STOCK PRICES' table has the same columns. Which of the following Snowflake features or techniques would be BEST suited for automatically loading the new data each day, ensuring that duplicate entries for the same 'DATE and 'SYMBOL' are avoided?
- A. Use Snowpipe Auto-Ingest with an external stage pointing to the Snowflake Marketplace data feed. Configure the pipe to copy new files from the Marketplace's internal stage to your table.
- B. Create a Stream on the 'MARKETPLACE STOCK PRICES view, and then create a Snowflake Task that runs daily and processes the stream, inserting only new or changed data into the table. Schedule the Stream refresh frequency to once every day to match the new data feed frequency.
- C. Create a Snowflake Pipe that automatically ingests data from the view into the 'STOCK_PRICES table whenever new data is available.
- D. Create a Snowflake Task that runs daily and executes a simple 'INSERT INTO STOCK_PRICES SELECT FROM statement.
- E. Create a Snowflake Task that runs daily and executes a 'MERGE statement to insert new data from into 'STOCK PRICES' , updating existing records if a match exists based on and 'SYMBOLS and inserting new records if there is no match. Also add a condition to remove the duplicate entries for the same 'DATE and ' SYMBOL'
Answer: E
Explanation:
Option C is the most appropriate. A 'MERGE statement allows for both inserting new records and updating existing records based on a matching condition (in this case, and 'SYMBOL). This ensures that new data is added, and existing data can be updated if necessary. It also allows for handling potential duplicate scenarios. Option A will simply insert all data, leading to duplicates. Option B is not applicable as Snowpipe ingests from stages not views. Option D is incorrect as Snowpipe is designed for external stages, not internal Marketplace feeds. Option E, while a valid approach for change data capture, is more complex and unnecessary for a simple daily load, also the marketplace feeds don't support change data capture through Streams
NEW QUESTION # 197
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