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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
You're developing a data quality process in Snowflake that relies on identifying duplicate records within a large table named 'TRANSACTIONS. You need to generate a hash value for each row based on several key columns ('transaction_id', 'customer_id' , amount', to efficiently compare rows and detect duplicates. However, some of these columns may contain NULL values, which you want to handle consistently during the hash generation. Which of the following approaches, utilizing Snowflake's system functions, will MOST reliably generate a consistent hash value for duplicate rows, even when some of the key columns contain NULLs? (Select TWO)

A. Use the WS('I', transaction_id, customer_id, amount, function. This concatenates strings with a separator, handling NULLs implicitly by skipping them in concatenation, leading to inconsistencies.
B. Use the 'SHA2(CONCAT(NVL(transaction_id, 'I), NVL(customer_id, "), NVL(amount, "), NVL(transaction_date, "))) function, replacing NULLs with empty strings using the NVL function before concatenation.
C. Use the AS VARCHAR), AS VARCHAR), AS VARCHAR), NVL(CAST(transaction_date AS VARCHAR), function, explicitly converting each value to a string and replacing NULLs with empty strings using NVL.
D. Use the customer_id, amount, transaction_date)' function directly, as Snowflake automatically handles NULLs in hashing functions.
E. Use the II customer_id II amount II transaction_date)' function. Snowflake implicitly converts NULL to a default value during string concatenation.

Answer: B,C

Explanation:
Options B and E are the most reliable. Option B concatenates the value of the columns as a string to create a seed for SHA2, ensuring to convert the NULL to empty string, which is necessary so that SHA2 does not return NULL in the face of NULL column values. Option E also uses SHA2 to encrypt after concatenating all the column values but it casts all those columns to varchar, which is necessary for the data preparation and data ingestion as they might be of different datatype. The first option is wrong because Snowflake's HASH function automatically returns NULL if any of the input are NULL. Option C uses the 'II' operator to concatenate values and Snowflake will return NULL in case any value is null. Option D concatenates strings with a separator, handling NULLs implicitly by skipping them in concatenation, leading to inconsistencies

NEW QUESTION # 65
You are designing a data ingestion pipeline for IoT sensor data'. Sensors transmit data in JSON format every 5 seconds. The volume of data is expected to grow exponentially. The business requires both real-time dashboards and historical trend analysis. Which of the following strategies should you employ to address these requirements, particularly focusing on optimizing for both ingestion frequency and cost?

A. Use Snowpipe to ingest data into a raw landing table, and then use Snowflake tasks to transform and load the data into separate tables optimized for real-time dashboards and historical analysis.
B. Utilize an external stream processing engine to pre-aggregate the data into time windows (e.g., I-minute, I-hour) before ingesting into Snowflake using Snowpipe.
C. Ingest data directly into a single Snowflake table using Snowpipe with JSON data type. Create separate materialized views for real-time dashboards and historical trend analysis.
D. Employ a combination of Snowpipe for near real-time data ingestion into a raw table, and then use Snowflake's Search Optimization Service for faster queries.
E. Implement Snowpipe for initial data ingestion, complemented by Snowflake's clustering feature based on timestamp to optimize historical analysis queries. And employ a stream processing engine to perform time window pre-aggregation.

Answer: A,E

Explanation:
This question has multiple correct answers. Option B and E addresses the need for optimizing both ingestion frequency and cost. Option B involves using Snowflake tasks for transformations, which allows for separating data for real-time and historical analysis. This optimizes query performance for both use cases. Using only Snowpipe directly into a single table is simple but doesn't provide optimization for the different query patterns, while the other options are less efficient or don't fully address the dual requirements. Option E complements using Snowpipe by using Snowflake clustering features and employing stream processing engine to perform pre-aggregation. Snowflake clustering optimize the speed of historical analysis, reducing cost of scanning large datasets.

NEW QUESTION # 66
You are tasked with validating the data in a 'SALES table. The table contains 'ORDER ID', 'CUSTOMER ID', 'PRODUCT ID, 'SALE DATE, and 'SALE AMOUNT'. You need to identify orders where the 'SALE AMOUNT is negative or zero, indicating potential data entry errors, and also find orders with duplicate 'ORDER ID' values but different 'SALE AMOUNT values. Which of the following queries, when combined, would efficiently achieve this validation?

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

Answer: D

Explanation:
The first query 'SELECT ORDER ID, SALE_AMOUNT FROM SALES WHERE SALE AMOUNT O;' identifies orders with non- positive sale amounts. The second query 'SELECT ORDER_ID, FROM SALES GROUP BY ORDER_ID HAVING COUNT(DISTINCT SALE_AMOUNT) > 1;' efficiently finds ORDER_lDs that have multiple distinct SALE_AMOUNT values, indicating duplicates with conflicting data. "ANY _ VALUE is used since we only care about the 'ORDER_ID'.

NEW QUESTION # 67
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 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.
B. 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.
C. 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.
D. 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.
E. Use a third-party ETL tool to extract, transform, and load the data into Snowflake.

Answer: D

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 # 68
How do Snowsight dashboards enable effective data presentation for business use analyses?

A. Snowsight dashboards rely solely on text-based representations.
B. Snowsight offers limited data representation options.
C. They enable diverse data representation for effective analyses.
D. Snowsight doesn't support visual data representation.

Answer: C

Explanation:
Snowsight dashboards enable diverse data representation for effective analyses in business use cases.

NEW QUESTION # 69
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