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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Modeling and Performance Optimization	- Modeling approaches in Snowflake 1. Star and snowflake schemas 2. Data normalization vs denormalization - Performance tuning 1. Clustering and pruning techniques 2. Warehouse sizing and auto-suspend/auto-resume
Topic 2: Snowflake Architecture and Data Platform Fundamentals	- Snowflake architecture concepts 1. Cloud services layer, compute layer, storage layer 2. Virtual warehouses and scaling - Data platform fundamentals 1. Separation of storage and compute 2. Data lifecycle in Snowflake
Topic 3: Data Transformation and Analysis	- Analytical workloads 1. Query optimization for analytics 2. Materialized views and caching - SQL-based transformations 1. Semi-structured data (VARIANT, JSON, XML) 2. Joins, aggregations, window functions
Topic 4: Security, Governance, and Data Sharing	- Access control and security 1. Authentication and encryption concepts 2. Role-based access control (RBAC) - Data sharing and governance 1. Data masking and policies 2. Secure data sharing
Topic 5: Data Loading and Unloading	- Data ingestion methods 1. COPY INTO and bulk loading 2. Continuous ingestion and Snowpipe concepts - Data export 1. UNLOAD and external stages

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
When employing window functions versus table functions in Snowflake, how do they differ in their application and output?

A. Table functions return tables as their output
B. Window functions operate on entire datasets and generate tables as output
C. Window functions provide aggregate results and modify table structures directly
D. Table functions process data within specified partitions or frames

Answer: D

Explanation:
Table functions in Snowflake process data within specified partitions or frames, whereas window functions generate aggregate results and operate on entire datasets, differing in their scope and operation.

NEW QUESTION # 18
You are tasked with building a dashboard that visualizes website traffic data stored in Snowflake. The data includes daily unique visitors, bounce rate, and average session duration. The business stakeholders want to understand the correlation between these metrics. They also want to identify any outliers or anomalies. Which chart type is BEST suited for identifying correlation and outliers in this dataset?

A. A pie chart showing the percentage contribution of each metric to the total.
B. A bar chart comparing the average values of each metric.
C. A histogram showing the distribution of individual Metrics.
D. A line chart showing each metric over time.
E. A scatter plot matrix showing the pairwise relationships between all metrics.

Answer: E

Explanation:
A scatter plot matrix displays the pairwise relationships between multiple variables. This allows for easy identification of correlations (positive, negative, or none) and outliers in the data. Line charts are good for showing trends over time, but not for directly visualizing correlations between different metrics. Bar charts compare average values, and pie charts show proportions. Histograms helps to show single distribution only.

NEW QUESTION # 19
You're working with product catalog data in Snowflake. The product information is stored in a table named 'PRODUCTS' , and a key attribute, 'attributes' , contains a semi-structured JSON object for each product. This 'attributes' object can have varying keys, but you are interested in extracting specific keys and pivoting them into columns. The relevant JSON structure is as follows : { "color": "red", "size": "L", "material": "cotton", "style": "casual"} '"What method is the MOST efficient to transform this data to a relational structure, assuming you want to analyze product attributes such as 'color' and 'size' as separate columns?

A. Creating a view with direct JSON path accessors (e.g., for each desired attribute.
B. Using LATERAL FLATTEN to unnest the 'attributes' and then using a CASE statement to pivot the data.
C. Using dynamic SQL to generate a query that extracts the required attributes using JSON path accessors and then creates a new table.
D. Creating a new table with a 'VARIANT column for the attributes and performing transformations in a BI tool.
E. Using a stored procedure to iterate through each row, parse the JSON, and update a new table with pivoted columns.

Answer: A

Explanation:
Option B is the most efficient. Directly accessing the JSON elements using path accessors like allows Snowflake to optimize the query execution, which typically offers superior performance compared to flattening and pivoting with 'CASE statements. Flattening (Option A) introduces unnecessary complexity and overhead when specific attributes are known and desired. Options C and D are generally inefficient and should be avoided for this type of transformation. Creating a view is more performant and simple. Option E is overkill and introduces complexity that isn't needed since the required attributes are known.

NEW QUESTION # 20
You have a large CSV file containing customer transaction data that you need to load into Snowflake using Snowsight. The CSV file is located in an AWS S3 bucket. The file contains fields like 'transaction id', 'customer id', 'transaction date', and 'transaction amount. However, the 'transaction_date' column is in the format 'YYYYMMDD' and you need to convert it to Snowflake's DATE format ('YYYY-MM-DD') during the load process. Which of the following steps should you take in Snowsight to accomplish this efficiently and correctly?

A. Use Snowsight's 'Load Data' wizard to load the CSV file directly into a table with the required schema. After loading, execute an "UPDATE statement to convert the 'transaction_date' column using 'TO DATE(transaction_date, YYYYMMDD'V.
B. Create an external table pointing to the S3 bucket. Then, create a view on top of the external table with the 'TO_DATE(transaction_date, 'YYYYMMDD')' transformation applied. Finally, create a new table using 'CREATE TABLE AS SELECT from the view.
C. Load the CSV file into Snowflake without any transformation. Write a stored procedure to transform the 'transaction_date' column and schedule the stored procedure to run periodically.
D. Load the data into a staging table with all columns as VARCHAR. Then, create a new table with the desired schema. Finally, use a 'CREATE TABLE AS SELECT (CTAS) statement with 'TO DATE(transaction_date, to transform and load the data from the staging table to the final table.
E. Create a new table in Snowflake with the desired schema (including DATE data type for 'transaction_date'). Use Snowsight's 'Load Data' wizard to load the CSV file, selecting the appropriate file format options and using a computed column expression 'TO_DATE(transaction_date, 'YYYYMMDD')' for the 'transaction date' column.

Answer: E

Explanation:
Option A is the most efficient and correct approach. Snowsight's 'Load Data' wizard allows you to specify transformations during the load process using computed columns, which is more performant than loading into a staging table or updating after loading. Options B, C, D and E are functional but less efficient due to the extra steps involved. Using external tables for initial loading then CTAS can be good for exploration but not as direct as option A. Updates should generally be avoided on large datasets after loading when you have a chance to transform during load.

NEW QUESTION # 21
You are tasked with ingesting data from a REST API that provides customer order information in JSON format. The API returns a nested JSON structure with an array of orders, each containing customer details and order items. The data volume is expected to be high. You need to efficiently load this data into Snowflake. Which of the following approaches would be MOST efficient, considering cost and performance, and taking into account potential data quality issues?

A. Load the data into a temporary stage, manually parse it with SQL statements, and then load it into your target tables. Schedule the task for periodic execution.
B. Use a third-party ETL tool to extract, transform, and load the data into Snowflake.
C. Load the JSON data directly into a VARIANT column, then create a view to flatten the data as needed. Use Snowflake's JSON functions for querying and transformation.
D. Use an external function to call a Python script that parses the JSON, transforms the data into a flat relational structure, and then uses the Snowflake Python Connector to insert the data directly into relational tables.
E. Use Snowpipe with a stream and task to continuously ingest the JSON data into a staging table with a VARIANT column. Then, create a separate task to flatten the data into relational tables using SQL transformations after ingestion.

Answer: E

Explanation:
Snowpipe with a stream and task offers continuous, near real-time ingestion. Using a staging table with a VARIANT column allows for handling the complex JSON structure initially. Subsequent tasks can then transform and flatten the data into relational tables. This approach combines efficiency, automation, and scalability, minimizing manual intervention and maximizing performance for high-volume data. Other options are less efficient or scalable: A requires constant parsing during queries, C introduces external dependencies and overhead, D adds complexity and cost of a third-party tool, and E is manual and not scalable.

NEW QUESTION # 22
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