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| Section | Weight | Objectives |
|---|
| Topic 1: Data Analysis | 30%-32% | - Perform advanced analytics using SQL
- 1. Snowflake-specific analytical features
- 2. Time-series analysis
- 3. Aggregate functions
|
| Topic 2: Data Transformation and Data Modeling | 22%-30% | - Design data models
- 1. Data vault models
- 2. Snowflake schema design
- 3. Star schema design
- Transform data using SQL
- 1. PIVOT/UNPIVOT operations
- 2. Window functions
- 3. QUALIFY clauses
- 4. Common Table Expressions (CTEs)
|
| Topic 3: Data Ingestion and Data Preparation | 15%-20% | - Implement data processing solutions
- 1. Cleanse, conform, and enrich data
- 2. Automate and implement data pipelines (scheduling)
- 3. Use logging and monitoring solutions (auditing, data lineage)
- 4. Respond to processing failures
- Enrich data by identifying and accessing relevant data from the Snowflake Marketplace
- 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings)
- 2. Find external data sets that correlate with available data
- 3. Create tables and views
- Use best practice considerations relating to data integrity structures
- 1. Perform table joins between parent/child tables
- 2. Implement constraints
- 3. Define primary keys for tables
- Prepare data and load into Snowflake
- 1. Load files using Snowsight
- 2. Load data from external/internal stages into a table
- Perform data discovery to identify what is needed from available datasets
- 1. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
- 2. Evaluate required transformations (table joins, set operations, ASOF JOINS)
- 3. Identify elements required for business goals using BI reports or SQL analysis
- 4. Determine the level of data granularity required
- 5. Query tables to assess data elements and statistics maintained by Snowflake
- Use a collection system to retrieve data
- 1. Retrieve data from structured sources (CSV)
- 2. Retrieve data from unstructured sources
- 3. Synthetic Data Generation
- 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
|
| Topic 4: Data Presentation and Data Visualization | 28%-29% | - Integrate with BI tools
- 1. Power BI integration
- 2. Other partner visualization tools
- 3. Tableau integration
- Create dashboards
- 1. Snowsight dashboards
- 2. Present analytical results
|
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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q51-Q56):
NEW QUESTION # 51
When managing Snowsight dashboards, what role do subscriptions and updates play in meeting business requirements?
- A. Managing subscriptions and updates complicates dashboard usage.
- B. They enhance dashboard usability without impacting data updates.
- C. Subscriptions and updates ensure timely information delivery.
- D. Subscriptions and updates don't impact dashboard management.
Answer: C
Explanation:
Subscriptions and updates ensure timely information delivery, meeting business requirements.
NEW QUESTION # 52
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?
Answer: D
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 # 53
A data analyst is tasked with optimizing query performance for a frequently accessed report that calculates daily sales totals for different product categories. The report currently takes an unacceptable amount of time to generate. The underlying table 'SALES DATA contains columns: 'ORDER DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), (NUMBER). Which of the following database objects would be MOST effective to significantly improve the report's performance without requiring changes to the existing SQL query used for the report?
- A. A standard view created using the same SQL query as the report.
- B. An external table pointing to the same data source as 'SALES_DATR.
- C. A sequence object to pre-calculate sales amounts.
- D. A temporary table created before running the report.
- E. A materialized view created using the same SQL query as the report.
Answer: E
Explanation:
A materialized view stores the results of the query, so subsequent queries can retrieve the pre-computed results directly. This avoids the need to re-run the query each time the report is generated, significantly improving performance. A standard view is just a stored query and doesn't store data. An external table is used for data residing outside of Snowflake. A temporary table would only be useful during the session it's created in and would need to be populated, adding to processing time. A sequence object is used for generating unique numbers, not for calculating sales amounts.
NEW QUESTION # 54
When utilizing User-Defined Functions (UDFs) in SQL, which statement accurately describes their usage?
- A. UDFs are only applicable to numeric data types.
- B. They allow custom-defined operations on data, extending SQL functionalities.
- C. UDFs can't be invoked within Stored Procedures.
- D. UDFs are solely used for administrative tasks within databases.
Answer: B
Explanation:
UDFs extend SQL functionalities by enabling custom operations on data, going beyond standard SQL capabilities.
NEW QUESTION # 55
A marketing team requires a daily report showcasing website traffic, conversion rates, and cost per acquisition (CPA). They want to receive this report as a CSV file attached to an email. The data is stored in a Snowflake table 'WEB ACTIVITY with columns 'DATE , 'VISITS, , and SPEND. Which of the following steps, combined and executed in the correct order, would be the MOST efficient and secure way to automate this report delivery?
- A. Create a Snowflake Task that executes a stored procedure. The stored procedure uses a Snowflake Scripting block to query the data, format it as CSV using Javascript UDF, writes the CSV to an internal stage, and then uses a Java UDF (Util.EmailSender) to send the email with the CSV attachment. Grant necessary permissions to the task's service account to access the stage and execute the UDFs.
- B. Use a third-party reporting tool (e.g., Tableau, Power Bl) to connect to Snowflake, create the report, and schedule it for daily email delivery with a CSV attachment. Configure the tool with the appropriate Snowflake connection details and credentials.
- C. Create a Snowflake Stream on the 'WEB ACTIVITY table. When data changes are detected, trigger an external function (e.g., AWS Lambda) via a pipe. The Lambda function queries the data, formats it as CSV, uploads the file to S3, and sends an email with a download link.
- D. Create a scheduled task in an external orchestrator (e.g., Airflow). This task uses the Snowflake Python connector to query the data, format it as CSV, writes the CSV to an external stage (e.g., AWS S3), and then uses an email service (e.g., AWS SES) to send the email with the CSV attachment. Configure appropriate IAM roles for Snowflake to access S3.
- E. Create a Snowflake Alert that triggers when the 'WEB ACTIVITY table is updated. The alert executes a stored procedure that queries the data, formats it as CSV using Snowflake Scripting, writes the CSV to an internal stage, and then uses a Python UDF to send the email with the CSV attachment. Grant the alert necessary privileges.
Answer: D
Explanation:
Option B is the most efficient and scalable. Using an external orchestrator provides better scheduling control and separation of concerns. Writing to S3 allows for easier integration with other external services. Python connector is preferred for data transformation and CSV generation compared to Snowflake scripting. Utilizing AWS SES for email delivery scales well and aligns with a cloud-native approach. Option A is viable but can be resource-intensive within Snowflake. Options C, D and E are less efficient due to relying on event-based triggers or third party tools for scheduling and email delivery. Option D exposes the data to a third-party tool. Option E requires complex setup and is not the best choice for a simple daily report.
NEW QUESTION # 56
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