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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
You have developed a Snowsight dashboard for your marketing team that contains sensitive customer data'. You need to share this dashboard with a specific group of users, but ensure that they can only view the data and cannot modify the dashboard itself or the underlying queries. Which of the following steps should you take to securely share the dashboard?

A. Share the dashboard with the group using the 'Can view' permission. Then, grant the users in the group the 'MONITOR privilege on the virtual warehouse used by Snowsight.
B. Share the dashboard with the group using the 'Can edit' permission. Then, grant the users in the group the 'USAGE' privilege on the database and schema containing the data.
C. Share the dashboard with 'Can view' permission and no further action is required.
D. Share the dashboard with the group using the 'Can view' permission. Ensure the group has the 'SELECT' privilege on the tables/views used in the queries and the ' USAGE privilege on the database and schema. Also ensure that any intermediate tables created by the dashboard are also granted these privleges.
E. Create a scheduled task to export the dashboard as a PDF and email it to the group on a daily basis.

Answer: D

Explanation:
Sharing with 'Can view' (B) ensures the users cannot modify the dashboard itself. Granting 'SELECT on the underlying tables/views and USAGE' on the database/schema allows them to see the data when the dashboard executes queries on their behalf. 'Can edit' (A) would allow them to modify the dashboard, which is not desired. 'MONITOR (C) is for monitoring warehouse usage, not data access. Exporting as PDF (D) is a workaround, but it loses interactivity and isn't a direct sharing method. E isn't correct because permissions on the data sources must be granted separately.

NEW QUESTION # 42
Which action is crucial in performing diagnostic analysis to identify reasons/causes of anomalies in historical data?

A. Collecting unrelated data and ignoring statistical trends
B. Collecting related data and demographics
C. Analyzing data from isolated points in time
D. Ignoring demographic variations in the data

Answer: B

Explanation:
Collecting related data and demographics is crucial in identifying reasons/causes of anomalies in historical data.

NEW QUESTION # 43
How do row access policies and Dynamic Data Masking impact the creation of dashboards in terms of data visibility and security?

A. Row access policies limit data visibility based on user privileges.
B. They improve data visibility for all users without restrictions.
C. Dynamic Data Masking doesn't affect data visibility in dashboards.
D. Both policies restrict data visibility for better security.

Answer: A

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in dashboard creation.

NEW QUESTION # 44
In the context of data processing solutions, how does handling processing failures contribute to ensuring data reliability?

A. Increases data redundancy
B. Ensures real-time data availability
C. Reduces system downtime
D. Prevents data loss or corruption

Answer: D

Explanation:
Handling processing failures ensures data reliability by preventing data loss or corruption, maintaining the integrity of processed data.

NEW QUESTION # 45
What option would allow a Data Analyst to efficiently estimate cardinality on a data set that contains trillions of rows?

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

Answer: C

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 # 46
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