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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Perform Simple Data Transformations for Analysis	15–20%	- Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs
Prepare and Load Data	15–20%	- External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro
Perform Predictive Analysis	5–10%	- Using Snowflake ML and built-in analytics - Forecasting and predictive modeling
Build and Troubleshoot Advanced SQL Queries	20–25%	- Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing
Prepare and Present Data	10–15%	- Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results
Perform Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis
Use Built-in Functions and Create UDFs	10–15%	- User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
A telecommunications company wants to identify customers whose service addresses fall within a specific service area polygon defined as a Well-Known Text (WKT) string. The customer addresses are stored in a table 'CUSTOMER ADDRESSES' with a 'ADDRESS POINT column of type GEOGRAPHY. You have the WKT representation of the service area polygon stored in a variable '@service area_wkt'. Which of the following statements will correctly identify the customers within the service area? (Select all that apply)

Answer: B,D

Explanation:
TO_GEOGRAPHY(@service_area_wkt))' correctly identifies points that fall within the service area polygon. converts the WKT string into a GEOGRAPHY object. 'ST_COVERS(TO_GEOGRAPHY(@service_area_wkt), ADDRESS_POINT)' checks if the polygon covers the point.

NEW QUESTION # 57
Which statement accurately describes the use of regular views in data analysis?

A. Regular views offer real-time updates to reflect instantaneous database changes.
B. Regular views limit data accessibility for improved security.
C. They simplify complex data structures, aiding ease of analysis.
D. Regular views are exclusively used for administrative tasks.

Answer: C

Explanation:
Regular views aid analysis by simplifying complex data structures, improving comprehension.

NEW QUESTION # 58
Why is the Parquet format preferred for complex data sets?

A. It randomly alters data for testing
B. It supports efficient compression and encoding schemes
C. It has visually appealing data presentation
D. It changes data colors for differentiation

Answer: B

NEW QUESTION # 59
You have a large dataset of IoT sensor readings stored in compressed JSON files within an AWS S3 bucket. Each JSON file contains an array of sensor readings with the following structure:
You need to load this data into a Snowflake table named 'sensor data' with columns 'sensor id', 'timestamp', 'temperature', and 'humidity'. Which of the following Snowflake commands would be the MOST efficient and appropriate to ingest this data, assuming you have already created the table and a named stage pointing to the S3 bucket?

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

Answer: B

Explanation:
Option A is the most efficient and direct way to load the data. = TRUE' correctly handles the array of JSON objects within each file. Option B would fail because it doesn't strip the outer array. Option C is incorrect as it treats the JSON as CSV. Option D, although functional, involves creating a temporary table and flattening, making it less efficient. Option E is invalid syntax, and unnecessarily complex.

NEW QUESTION # 60
You have developed a Snowsight dashboard for your marketing team that contains sensitive customer data'. You need to share this dashboard with a specific group of users, but ensure that they can only view the data and cannot modify the dashboard itself or the underlying queries. Which of the following steps should you take to securely share the dashboard?

A. Share the dashboard with 'Can view' permission and no further action is required.
B. Share the dashboard with the group using the 'Can view' permission. Ensure the group has the 'SELECT' privilege on the tables/views used in the queries and the ' USAGE privilege on the database and schema. Also ensure that any intermediate tables created by the dashboard are also granted these privleges.
C. Share the dashboard with the group using the 'Can edit' permission. Then, grant the users in the group the 'USAGE' privilege on the database and schema containing the data.
D. Create a scheduled task to export the dashboard as a PDF and email it to the group on a daily basis.
E. Share the dashboard with the group using the 'Can view' permission. Then, grant the users in the group the 'MONITOR privilege on the virtual warehouse used by Snowsight.

Answer: B

Explanation:
Sharing with 'Can view' (B) ensures the users cannot modify the dashboard itself. Granting 'SELECT on the underlying tables/views and USAGE' on the database/schema allows them to see the data when the dashboard executes queries on their behalf. 'Can edit' (A) would allow them to modify the dashboard, which is not desired. 'MONITOR (C) is for monitoring warehouse usage, not data access. Exporting as PDF (D) is a workaround, but it loses interactivity and isn't a direct sharing method. E isn't correct because permissions on the data sources must be granted separately.

NEW QUESTION # 61
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