Snowflake - DAA-C01 Authoritative Valid Test Practice

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prepare and Present Data	10–15%	- Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements
Topic 2: Build and Troubleshoot Advanced SQL Queries	20–25%	- Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting
Topic 3: Prepare and Load Data	15–20%	- Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation
Topic 4: Perform Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
Topic 5: Use Built-in Functions and Create UDFs	10–15%	- User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions
Topic 6: Perform Descriptive and Diagnostic Analysis	10–15%	- Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis
Topic 7: Perform Simple Data Transformations for Analysis	15–20%	- Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
Which Snowflake feature or object can be used to dynamically create and execute SQL statements?

A. System-defined functions
B. Stored procedures
C. User-Defined Functions (UDFs)
D. Tasks

Answer: B

Explanation:
While Snowflake provides various ways to encapsulate logic, Stored Procedures are the primary tool for procedural programming and dynamic SQL execution. Unlike standard UDFs, which are generally designed to calculate and return values within a query, stored procedures can execute administrative commands (DDL) and data manipulation (DML) that are not possible in a simple SELECT statement.
Using Snowflake Scripting (or languages like Python, Java, or JavaScript), an analyst can write a procedure that builds a SQL string as a variable and then executes it using the EXECUTE IMMEDIATE command. This is demonstrated in procedural logic flows where inputs determine specific execution paths. While Tasks can be used to schedule the execution of these procedures, the task itself is a scheduling agent and does not provide the logic-driven dynamic string construction inherent to the procedure.

NEW QUESTION # 53
You are preparing to load a large dataset from Parquet files stored in an Azure Blob Storage container into Snowflake using Snowsight. The dataset contains personally identifiable information (PII) and you need to ensure that only authorized users can access this data,. You want to use Snowflake's data masking policies to protect the PII. Which of the following options represents the correct sequence of steps and considerations for achieving this, specifically using Snowsight for the loading and initial policy application phases?

A. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Load the Parquet files into a staging table with all columns as VARCHAR using Snowsight. 4. Define masking policies on the staging table columns. 5. Create the final table and transfer data from staging, applying the masking policies on the target table as the data moves.
B. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Define masking policies on the target table columns. 4. Create the target table with the desired schema, referring to masking policies. 5. Load the Parquet files directly into the target table using Snowsight.
C. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Create the target table with the desired schema, including data types. 4. Load the Parquet files directly into the target table using Snowsight. 5. Define masking policies on the target table columns after loading.
D. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Load the Parquet files into a staging table with all columns as VARCHAR using Snowsight. 3. Create a file format object specifying PARQUET as the file type. 4. Define masking policies on the staging table columns. 5. Create the final table and transfer data from staging, applying the masking policies on the target table as the data moves.
E. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Create the target table with the desired schema, including data types. 4. Define masking policies on the target table columns. 5. Load the Parquet files directly into the target table using Snowsight.

Answer: E

Explanation:
Option E is the most efficient and recommended approach. First, you need to set up the connection to Azure Blob Storage (the external stage) and define the file format for Parquet files. Then, you create the target table with the correct schema to receive the data. The critical step is to define the masking policies before loading the data. This ensures that the masking policies are in place when the data is loaded into the target table. Finally, load the data using Snowsight. Applying masking policies after loading the data (as in option B) is less efficient and leaves a window of opportunity where PII is exposed. Defining policies and then referring to them (as in option C) isn't the typical workflow. Option A is more complex and introduces a VARCHAR staging table, which isn't optimal for Parquet data.

NEW QUESTION # 54
A company receives daily CSV files containing customer order data'. Each file contains a header row and is compressed using GZIP.
The files are landed in an AWS S3 bucket. The company wants to automate the data ingestion into a Snowflake table named 'orders table'. The requirements are: 1. Automated ingestion: New files should be automatically ingested as they arrive in the S3 bucket. 2. Data validation: Records with invalid dates or missing product IDs should be rejected and logged for review. 3. Data transformation: The column (string format 'YYYY-MM-DD') needs to be converted to a DATE data type, and a new column 'order _ year' needs to be derived from the 'order_date'. Which combination of Snowflake features and configurations provides the MOST efficient and reliable solution to meet these requirements?

A. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that performs the date conversion using TO DATE() and extracts the order year using YEAR(). Configure the COPY INTO statement with "ON_ERROR = 'CONTINUE" and a validation table to log rejected records.
B. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that includes 'ON ERROR = 'SKIP_FILE". Use a downstream task to periodically validate and transform the data in the 'orders_table'.
C. Create a Snowpipe that points to the S3 bucket. Use a COPY INTO statement with 'VALIDATE(O)' and a BEFORE trigger to invoke a stored procedure that validates the data against a set of rules. Use a stored procedure to transform the data into 'orders_table' .
D. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that utilizes a user-defined function (UDF) written in Python to perform complex data validation and transformation before loading the data into the 'orders_table'. Set 'ON_ERROR = 'SKIP_FILEP to avoid loading erroneous data.
E. Create an external table pointing to the S3 bucket. Use a stream on the external table to track changes and a task to periodically move the new data into the 'orders_table' while performing the necessary transformations and validations. This could also be achieved using Dynamic Tables.

Answer: A,E

Explanation:
Options B and C offer the best combination of features to address the requirements effectively. Option B leverages Snowpipe's COPY INTO statement to directly convert the date, calculate order year, and handle errors by continuing the load and logging invalid records into a validation table. This maximizes efficiency and ensures that valid data is ingested quickly. ON ERROR = 'CONTINUE' is better than SKIP FILE since it is preferable to ingest valid data in file even some has issues. Option C uses external tables combined with streams and tasks or dynamic tables which is an alternative to Snowpipe and COPY INTO and also provides automatic ingestion and transformation capabilities. Option A is less effective because it does not provide a mechanism to capture and log errors from the copy process; skipping files provides no insight to the validity of data. Option D is not the most efficient. While UDFs can handle complex transformations, relying solely on them for all validation and transformation steps can lead to performance bottlenecks and introduce maintenance overhead; Also setting ON_ERROR = 'SKIP_FILE' isn't a great pattern if you want to ingest partial data. Option E's BEFORE trigger might add significant overhead since Snowflake triggers have limitations.

NEW QUESTION # 55
Why are Stored Procedures valuable in data analysis using SQL?

A. They are exclusively used for one-time data operations.
B. Stored Procedures solely facilitate data visualization.
C. Stored Procedures enable custom and repeated data operations, enhancing efficiency.
D. They restrict the execution of repetitive tasks, limiting efficiency.

Answer: C

Explanation:
Stored Procedures aid in data analysis by enabling custom and repeated data operations, enhancing efficiency.

NEW QUESTION # 56
A data analyst is tasked with identifying the top 3 performing sales representatives in each region based on their total sales amount. The sales data is stored in a table named 'sales data" with columns 'region', 'sales_rep', and 'sales_amount'. Which Snowflake SQL statement(s) would efficiently achieve this?

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

Answer: A,D,E

Explanation:
Options A, B, and C correctly utilize window functions to rank sales representatives within each region. assigns a unique rank, assigns the same rank to ties and skips the subsequent ranks, and 'DENSE RANK()' assigns the same rank to ties but does not skip ranks. All three can be used with a 'WHERE clause to filter for the top 3. Option D incorrectly uses 'LAG' and doesn't achieve the desired ranking. Option E uses 'NTILE' which divides the rows into three groups; filtering 'tile = 1' might not always return the top three based on sales.

NEW QUESTION # 57
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