Real and Updated Snowflake DAA-C01 Exam Questions

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Transformation and Analysis	- Analytical workloads 1. Query optimization for analytics 2. Materialized views and caching - SQL-based transformations 1. Joins, aggregations, window functions 2. Semi-structured data (VARIANT, JSON, XML)
Topic 2: Snowflake Architecture and Data Platform Fundamentals	- Data platform fundamentals 1. Separation of storage and compute 2. Data lifecycle in Snowflake - Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer
Topic 3: Data Modeling and Performance Optimization	- Modeling approaches in Snowflake 1. Star and snowflake schemas 2. Data normalization vs denormalization - Performance tuning 1. Warehouse sizing and auto-suspend/auto-resume 2. Clustering and pruning techniques
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. Secure data sharing 2. Data masking and policies
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 (Q34-Q39):

NEW QUESTION # 34
Consider a scenario where you have a table 'CUSTOMER ORDERS' with columns 'CUSTOMER ID', 'ORDER DATE' , 'ORDER TOTAL' , and 'PRODUCT CATEGORY'. You want to create a materialized view that calculates the sum of order totals for each customer, grouped by product category, and refreshed automatically on a daily basis. However, you are also concerned about minimizing the cost of materialized view maintenance. Which of the following strategies would be MOST cost-effective while still providing reasonably up-to-date data?

A. Create a materialized view and schedule a daily refresh at a time of low system activity.
B. Create a materialized view and set the refresh schedule to 'ON CHANGE' with a clustering key on
C. Create a materialized view and set the refresh schedule to 'ON CHANGE'.
D. Create a standard view with the same aggregation logic and optimize the underlying table using clustering.
E. Create a materialized view without specifying a refresh schedule, and manually refresh it whenever the report is run.

Answer: A

Explanation:
Scheduling a daily refresh allows the materialized view to be updated regularly without incurring the overhead of 'ON CHANGE' refreshes, which can be very costly if the underlying table is frequently updated. Manual refreshes would not provide up-to-date data automatically. 'ON CHANGE' without further optimization can be extremely expensive. A standard view wouldn't provide the performance benefits of a materialized view. Clustering on CUSTOMER ID might improve performance but would not address the refresh cost directly.

NEW QUESTION # 35
What option would allow a Data Analyst to efficiently estimate cardinality on a data set that contains trillions of rows?

A. Count(Distinct *)
B. SYSTEM$ESTIMATE
C. Count(Distinct *)/Count(*)
D. HLL(*)

Answer: D

Explanation:
When working with "Big Data" at the scale of trillions of rows, calculating an exact count of unique values using COUNT(DISTINCT column) is extremely resource-intensive. This is because Snowflake must keep track of every unique value encountered to ensure no duplicates are counted, leading to high memory usage and long execution times (often referred to as "spilling to disk").
To solve this, Snowflake provides HyperLogLog (HLL) functions. HLL(*) (or specifically HLL_ACCUMULATE and HLL_ESTIMATE) allows an analyst to estimate the cardinality (the number of unique elements) with a very small, known margin of error (typically around 1%). This is significantly faster and uses far fewer credits than an exact count because it uses a probabilistic algorithm rather than a state- heavy tracking mechanism.
Evaluating the Options:
* Option A is technically correct for small datasets but is highly inefficient for trillions of rows, directly contradicting the "efficiently" requirement of the question.
* Option C is a distractor; while Snowflake has various SYSTEM$ functions, SYSTEM$ESTIMATE is not a standard function for cardinality.
* Option D is a formula that doesn't target cardinality but rather a ratio (density).
* Option B is the correct answer. The HLL family of functions is the industry standard within Snowflake for high-performance cardinality estimation on massive datasets.

NEW QUESTION # 36
You need to load data from a set of CSV files located in an AWS S3 bucket into a Snowflake table called 'SALES DATA. The CSV files have a header row, and you want to automatically detect the data types of the columns. However, some files might have inconsistent data types (e.g., a column that is usually an integer might occasionally contain a string). Furthermore the file are compressed using GZIP. Which of the following COPY INTO command options can you use to achieve this goal while handling potential data type mismatches?

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

Answer: B

Explanation:
The 'ON_ERROR = 'SKIP_RECORD" option allows the COPY INTO command to skip individual records that have data type mismatches or other errors during the load process. This ensures that the load process continues even if some records fail validation. 'SKIP_FILE' would skip entire file, 'CONTINUE' will only continue the process with the given data, and 'SKIP_FILE' skips the file. The VALIDATE UTF8 isn't directly relevant to data type issues

NEW QUESTION # 37
How does leveraging partition pruning enhance query performance in Snowflake?

A. Reduces metadata storage requirements
B. Speeds up data loading processes significantly
C. Limits data access for specific user roles
D. Optimizes query planning by eliminating unnecessary partitions

Answer: D

Explanation:
Partition pruning optimizes query planning by excluding unnecessary partitions from query execution, improving query performance by focusing on relevant data subsets.

NEW QUESTION # 38
You're designing a data pipeline in Snowflake to process order data'. The raw order data, including customer information, is stored in a JSON format within a single 'RAW ORDERS table. Due to privacy regulations, you need to mask the customer's email addresses before loading the data into a 'CLEANED ORDERS' table, while maintaining referential integrity. Furthermore, you want to track the data lineage (which raw order resulted in which cleaned order) in a separate 'ORDER LINEAGE' table. Which of the following approaches achieves these requirements effectively and efficiently? (Select TWO)

A. Create a masking policy on the 'email' column in 'CLEANED ORDERS'. Create a stream on RAW ORDERS, then create a task to insert data from 'RAW ORDERS' into 'CLEANED ORDERS' and to INSERT the order to 'ORDER LINEAGE
B. Use a Snowpipe to load data into 'RAW ORDERS. Create a stream on 'RAW ORDERS'. Create a task chained to the stream that reads the new records from the stream, masks the email, inserts the cleaned data into 'CLEANED_ORDERS' table and inserts lineage information into the 'ORDER _ LINEAGE table.
C. Implement a stored procedure that reads data from 'RAW ORDERS, masks the email using SHA256, inserts the cleaned data into 'CLEANED ORDERS', and simultaneously inserts lineage information into 'ORDER LINEAGE. Schedule this stored procedure to run periodically.
D. Create a masking policy on the 'email' column of the 'RAW_ORDERS' table. Then, create a task to copy all data from 'RAW_ORDERS to 'CLEANED_ORDERS. Finally, create a separate task that runs after the copying task to populate the 'ORDER_LINEAGE table with relevant mappings.
E. Create a view on the 'RAW ORDERS table that masks the email address. Load the raw order data into 'RAW ORDERS table using Snowpipe and a stream. Use a task chained to the stream to ingest the view into 'CLEANED_ORDERS and populate lineage information into 'ORDER_LINEAGE table.

Answer: B,C

Explanation:
Option B: A stored procedure provides the most control over the masking process and lineage tracking, ensuring data is masked during the transfer. It allows you to perform all operations (masking, inserting into 'CLEANED ORDERS, and inserting into 'ORDER LINEAGE) within a single transaction. Option C: Snowpipe loading data to RAW_ORDERS, using streams to capture the data, and scheduling the tasks to read from the stream, while masking and adding lineage information provides automation and efficiency. Option A is incorrect, as it copies all data using a task into 'CLEANED_ORDERS and then masks the email on RAW_ORDERS after data is already in 'CLEANED_ORDERS'. Option D is incorrect as masking policy is on rather than Option E requires a view to mask email address and is not an effective approach to apply a masking policy. Also, its not the right procedure to load view into a table.

NEW QUESTION # 39
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