Free PDF Quiz Snowflake DAA-C01 - First-grade SnowPro Advanced: Data Analyst Certification Exam New Braindumps Pdf

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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
You are tasked with creating a dashboard in Snowsight to visualize sales data'. You have a table 'SALES DATA' with columns 'ORDER_DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), 'SALES_AMOUNT (NUMBER), and 'REGION' (VARCHAR). The business requirements include the following: 1. Display total sales amount by product category in a pie chart. 2. Display a table showing sales amount for each region for a user-selected date range. 3. Allow the user to filter both visualizations by a specific region.
Which of the following approaches would BEST satisfy these requirements using Snowsight dashboards and features?

A. Create a view with all calculations of the total sale amount, grouping by product category and region. Then create the dashboard with charts based off of this view. This will allow for easier modification if the business requirements change.
B. Create a single Snowsight dashboard with a Python chart for product category sales, querying data using Snowflake Connector, and a table showing regional sales using SQL query. No dashboard variables are needed, as the Python script handles all filtering.
C. Create a single Snowsight dashboard with two charts: a pie chart showing total sales by product category using the query 'SELECT PRODUCT_CATEGORY, SUM(SALES AMOUNT) FROM SALES DATA WHERE REGION = $REGION GROUP BY PRODUCT_CATEGORY, and a table showing regional sales using the query 'SELECT REGION, FROM SALES_DATA WHERE ORDER_DATE BETWEEN $START_DATE AND $END_DATE AND REGION = $REGION GROUP BY REGION'. Define three dashboard variables: 'REGION' (Dropdown), 'START DATE (Date), and 'END DATE (Date).
D. Create two separate charts: a pie chart for product category sales and a table for regional sales. Use the same filter on the dashboard for region, and manually enter the date range in the SQL query for the table chart.
E. Create two separate dashboards: one for the pie chart and another for the table. Use a global session variable to store the selected region and date range, and access it in the SQL queries for both dashboards.

Answer: C

Explanation:
Option B is the most effective because it utilizes dashboard variables for 'REGION' , 'START_DATE, and 'END_DATE' , allowing users to dynamically filter both the pie chart and the table. It also leverages SQL queries within Snowsight for data retrieval and aggregation, making it a straightforward and efficient solution. Option A is less flexible due to the manual date range entry. Option C unnecessarily introduces Python scripting, complicating the solution. Option D is inefficient as it creates separate dashboards. Option E creates a good base, but relies on option B to implement a dashboard with interactive filters.

NEW QUESTION # 35
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 customer_id, amount, transaction_date)' function directly, as Snowflake automatically handles NULLs in hashing functions.
B. 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.
C. 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.
D. Use the II customer_id II amount II transaction_date)' function. Snowflake implicitly converts NULL to a default value during string concatenation.
E. 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.

Answer: B,E

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 # 36
A marketing company is analyzing customer purchase data stored in Snowflake to understand which customer demographics are most likely to purchase a newly launched product. The 'CUSTOMERS table has columns: 'customer_id', 'age' , 'gender' , 'location' , and 'household income'. The 'PURCHASES' table has columns: 'customer_id', 'purchase_date', and 'product id'. Which SQL query would most effectively identify the top three age groups with the highest purchase rate for the new product (product_id = 'NEW PRODUCT')?

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

Answer: A

Explanation:
Option D is the most effective because it calculates the purchase rate by dividing the count of distinct customers who purchased the new product by the total number of customers in each age group. This provides a normalized purchase rate for each age group, allowing for a fair comparison regardless of the size of each group. Options A and C only provide the raw count of purchases or unique purchasers, which doesn't account for the size of each age group. Option B uses 'SUM(CASE...V , which can work, but is less efficient than using 'COUNT(DISTINCTV in combination with a left join to account for all customers, even those who didn't make any purchases. Option E would produce an incorrect rate as it is dividing a customer's count by the distinct count of each customer, resulting in a rate close to 1.0 for each customer that made a purchase.

NEW QUESTION # 37
You are tasked with creating a data access strategy for a marketing analytics team. They need access to customer purchase data, but only aggregated by region and product category. They should not be able to see individual customer details due to PII compliance. You decide to use a Secure View. Which of the following are the MOST appropriate steps to ensure data security and minimize performance impact?

A. Create a Secure View that aggregates the data and grant SELECT privilege on the view to the marketing analytics role.
B. Create a Materialized View that aggregates the data. Create a Secure View on top of the Materialized View and grant SELECT privilege on the secure view to the marketing analytics role.
C. Create a regular view that aggregates the data and grant SELECT privilege on the view to the marketing analytics role.
D. Create a Materialized View directly on the base tables with the aggregation logic. Grant SELECT privilege on the Materialized View to the marketing analytics role.
E. Create a Secure View directly on the base tables with the aggregation logic. Grant SELECT privilege on the view to the marketing analytics role.

Answer: A

Explanation:
Secure Views prevent access to the underlying tables and data lineage. Creating the Secure View directly with the aggregation prevents unauthorized access to detailed data and avoids exposing intermediate tables or views. Materialized views while improving performance, when used along with a Secure View adds unnecessary complexity as aggregation should happen in secure view only.

NEW QUESTION # 38
You are using Snowpipe to continuously load data from an AWS S3 bucket into a Snowflake table called 'ORDERS. The data is in JSON format. You observe that Snowpipe is occasionally missing records, even though the S3 event notifications are being correctly sent to the Snowflake-managed SQS queue. Upon investigation, you discover that some JSON records are larger than the maximum size supported by Snowpipe for a single record (16MB). You need to implement a solution to handle these oversized JSON records without losing data,. Which of the following approaches is the most efficient and reliable?

A. Implement a pre-processing step using an AWS Lambda function triggered by S3 events to split the oversized JSON records into smaller, valid-sized chunks before they are ingested by Snowpipe. Update the Snowpipe COPY statement to handle the new chunked data format.
B. Increase the 'MAX FILE SIZE parameter of the Snowpipe configuration to accommodate the larger JSON records. Snowflake automatically handles oversized records by splitting them internally.
C. Use the 'VALIDATE function in Snowflake to identify oversized JSON records. Then, manually extract and split those records into smaller files and load them separately.
D. Configure the S3 bucket to automatically split oversized JSON files into smaller files before they are sent to the SQS queue. Snowpipe will then process these smaller files independently.
E. Disable Snowpipe and switch to a batch loading approach using the COPY INTO command with automatic data compression. The COPY INTO command handles larger files more efficiently than Snowpipe.

Answer: A

Explanation:
The correct answer is B. Snowpipe has a limitation of 16MB per record. The most reliable solution is to pre-process the oversized records before they reach Snowpipe. Using an AWS Lambda function is a serverless and scalable way to split these records. Option A is incorrect because 'MAX FILE_SIZE pertains to the size of the files, not individual records within those files. Option C is not feasible as S3 doesn't natively split JSON files. Option D is inefficient as it involves manual intervention. Option E defeats the purpose of continuous data loading with Snowpipe. By splitting oversized records before Snowpipe ingests them, you ensure that no data is lost, and Snowpipe can continue to operate as designed.

NEW QUESTION # 39
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