Pass the Snowflake SnowPro Advanced Certification DAA-C01 Questions and answers with CertsForce

To predict future values based on historical data, an analyst must determine the mathematical relationship between two variables—typically time (independent variable) and revenue (dependent variable). This is the foundation of linear regression.

The REGR_SLOPE function is a linear regression function that calculates the slope of the "least squares" regression line for non-null pairs in a group. In the context of sales forecasting, the "slope" represents the rate of change in revenue over time. By calculating the slope, an analyst can project that trend forward to estimate what the revenue will be at a future date (the end of the year).

Evaluating the Options:

Option A (CORR) measures the correlation coefficient, which tells you how strongly two variables are related (between -1 and 1), but it does not provide a mathematical formula to predict a specific future value.

Option C (COVAR_SAMP) calculates the sample covariance, which indicates the direction of a linear relationship but not the magnitude or slope required for prediction.

Option D (VARIANCE) is a descriptive statistic that measures data spread (how far numbers are from the mean) and is not used for trend projection or prediction.

Option B is the 100% correct answer. Along with REGR_INTERCEPT, REGR_SLOPE allows the analyst to build the linear equation $y = mx + b$ to perform predictive analytics.

Snowflake Cortex ML functions, such as FORECAST, return a tabular result set when called using the instance method syntax (e.g., CALL modelX!FORECAST(...)). While this output is visible in the Snowsight results pane, the CALL statement itself cannot be used directly as a subquery within a standard INSERT INTO or CREATE TABLE AS SELECT (CTAS) statement.

To persist the results of a model's prediction into a permanent table (tableX), the Data Analyst must utilize the RESULT_SCAN table function. Snowflake stores the results of every query and function call in a temporary cache for 24 hours. The RESULT_SCAN function allows you to treat that cache as a queryable table.

The standard workflow is:

Execute the forecast: CALL modelX!FORECAST(FORECASTING_PERIODS => 12);

Immediately after, use the LAST_QUERY_ID() function to identify the query that generated the forecast results.

Create the table by querying that result set: CREATE TABLE tableX AS SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID()));

Evaluating the Options:

Option A is incorrect because the CALL syntax does not support a direct INTO clause for table creation.

Option B is incorrect because passing a table as an argument is part of the training or input phase, not the output persistence phase.

Option D is overly complex and contains non-standard terminology ("List the cache content").

Option C is the 100% correct answer. It reflects the required "post-processing" step in the Snowflake Data Cloud to bridge the gap between procedural model calls and relational table storage.

The INFER_SCHEMA function is a powerful tool in the Snowflake ingestion toolkit, specifically designed to bridge the gap between semi-structured data and relational tables. In modern data engineering, files like Parquet, Avro, and ORC are "self-describing," meaning they contain their own schema definitions (field names and data types) within the file header.

Before INFER_SCHEMA existed, a Data Analyst had to manually inspect a Parquet file, identify the keys, determine their data types, and then manually write a CREATE TABLE statement with the matching columns. INFER_SCHEMA automates this by scanning a set of files in a stage and returning a result set that describes the detected schema. This allows for the dynamic creation of tables that perfectly match the incoming data files.

Evaluating the Options:

Option A is incorrect because "unstructured data" (like PDFs or Images) does not have a schema that can be "inferred" in a relational sense.

Option C is incorrect because INFER_SCHEMA operates on files in a Stage, not on data already sitting in a table cell. To parse JSON in a table, you would use functions like FLATTEN or simple path notation.

Option D is incorrect because the DESCRIBE TABLE or GET_DDL commands are used to retrieve definitions for existing tables.

Option B is the Correct answer. It specifically identifies the role of the function: detecting metadata (schema) from staged semi-structured files. This function is often used in conjunction with GENERATE_COLUMN_DESCRIPTION and CREATE TABLE ... USING TEMPLATE to fully automate the schema evolution and ingestion process.

The provided image shows a specific section of a Snowflake Query Profile, which is a visual representation of the execution plan and the actual performance of a query. Analyzing this profile is a critical skill for a Data Analyst to identify performance bottlenecks.

1. Identifying the Exploding Join: The most striking evidence in this profile is the relationship between the input and output row counts of the Join [5] operator.

Input Row Counts: The join receives approximately 95.26k rows from one branch and 150k rows from the other.

Output Row Count: The join produces a staggering 571.6M rows.

When a join operation produces a significantly larger number of rows than the sum of its inputs, it is known as an exploding join (or a "Cartesian product-like" behavior). This typically occurs when the join condition is not restrictive enough or when there are many-to-many relationships with duplicate keys in both joining tables. In this specific case, joining on C1.C_NATIONKEY = C2.C_NATIONKEY has caused the row count to balloon from thousands to over half a billion.

2. Impact on Performance: Exploding joins consume excessive CPU and memory resources to process the massive intermediate result set. This often leads to secondary problems like spilling (Option A), where the data exceeds the virtual warehouse's memory and must be written to disk. However, the profile clearly identifies the join itself as the root cause.

Evaluating the Options:

Option A is a symptom, but the visual evidence of row expansion directly points to the join logic itself.

Option C (inefficient pruning) would be identified by a high percentage of partitions scanned in the Table Scan nodes, not by a row count explosion after a join.

Option D is irrelevant; while foreign keys can help the optimizer, their absence doesn't cause this specific visual profile on its own.

Option B is the 100% correct answer. The "exploded" arrow indicating 571.6M rows leaving a join fed by significantly smaller inputs is the textbook definition of an exploding join.

In Snowsight, the modern web interface for Snowflake, System Filters are specialized keywords that provide out-of-the-box interactivity for dashboards and worksheets. These filters allow non-technical users to manipulate the timeframes of visualizations (e.g., switching from "Last 7 days" to "Last 12 months") without requiring an analyst to manually rewrite the underlying SQL code.

The most critical keyword for temporal filtering is :daterange. When a dashboard contains a date filter widget (as shown in the provided exhibit), the :daterange keyword acts as a dynamic placeholder for the range selected by the user. Unlike a standard variable that might represent a single date, :daterange is specifically designed to handle the start and end boundaries of a period. When injected into a WHERE clause, Snowflake automatically expands this keyword into the appropriate logic to filter records between those two points in time.

Evaluating the Options:

Options A and B are incorrect because they use hard-coded logic (-7 days). While these would return data for the last week, they are static. Changing the filter in the dashboard UI would have no effect on these queries, failing the requirement to "easily change the time period" via the filter selection.

Option C is incorrect because :date_filter is not a reserved system keyword in Snowsight. While an analyst could create a custom filter with that name, it would not automatically link to the standard system date widget shown in the image.

Option D is the 100% correct answer. Using WHERE = :daterange is the documented standard for linking a query to the Snowsight system date filter. This ensures that every tile in the dashboard remains synchronized with the user's global date selection, providing a cohesive and interactive reporting experience.

In Snowflake, the performance of a metadata-based query (like MAX, MIN, or COUNT) is typically near-instantaneous because Snowflake maintains constant-time statistics in its Cloud Services layer. For a standard table, SELECT MAX(num) does not even require a virtual warehouse to be active; it simply reads the value from the table's metadata.

However, when a Row Access Policy (RAP) is applied to a table, the query's behavior changes fundamentally. A row access policy is a security feature that restricts which rows are visible to a user based on their role or other attributes. To enforce this policy, Snowflake can no longer rely on the high-level metadata of the entire table because it must first determine which specific rows the user is authorized to see. Consequently, the query engine must scan the individual micro-partitions and evaluate the policy logic for every row (or block of rows) to filter out unauthorized data before calculating the maximum value. This turns a "metadata-only" operation into a data-scanning operation, which requires a running warehouse and significantly more time.

Evaluating the Options:

Option A is incorrect because the prompt states the tables are "similarly-sized." Even if it were slightly larger, a metadata lookup for a standard table would still be nearly instant.

Option C is incorrect because a multi-cluster warehouse helps with concurrency (multiple users), not the raw execution speed of a single simple aggregate query.

Option D is incorrect because USE_CACHED_RESULT refers to the Query Result Cache. While turning it off would prevent a "0ms" response from a previous run, it wouldn't explain a "significant" slowdown compared to a standard metadata fetch.

Option B is the 100% correct answer. The presence of a Row Access Policy forces a full data scan and policy evaluation, which is the most common reason for performance degradation in otherwise simple metadata queries.

One of the standout features of the Snowsight interface is its ability to perform automatic Data Profiling. When a Data Analyst executes a query, Snowflake doesn't just return a raw grid of data; it analyzes the result set to provide immediate visual insights.

When you click on a column header in the results pane, a summary statistics panel appears. For numeric, date, and time columns, Snowflake automatically generates a histogram (Option A). This histogram provides a visual representation of the data distribution, allowing the analyst to quickly identify patterns, concentrations of values, or significant outliers without writing additional SQL code.

Evaluating the Options:

Option B: While a histogram is a type of frequency distribution, Option A is more accurate because Snowsight also provides these visualizations for date and time types, not just integers/floats.

Option C: While MIN and MAX values are displayed in the summary panel, they are text-based statistics, not the "visualized" contextual statistic (the histogram) emphasized in the question.

Option D: "Key distribution" is not a standard visualization term used in the Snowsight profiling tool.

Option A: Is the 100% correct answer. It highlights the breadth of the profiling tool (covering numbers, dates, and times) and the specific visual element (the histogram) that makes exploratory data analysis significantly faster for a Data Analyst.

In data modeling and analysis, "rotating" data is a common task used to normalize datasets for reporting or visualization. The operation of taking multiple columns (like the individual months in the source image) and turning them into values within a single column (like the "MONTH" column in the target image) is specifically known as unpivoting.

The UNPIVOT relational operator in Snowflake allows an analyst to transform a "wide" table format into a "narrow" (or "long") table format. In the wide format shown in the first image, data is distributed across columns named JAN, FEB, MAR, and APRIL. While this is often easier for humans to read in a spreadsheet, it is difficult to query for trends. By applying UNPIVOT, Snowflake collapses these columns into two new ones: one for the name of the original column (the attribute, such as "MONTH") and one for the value that was stored in that column (the metric, such as "SALES").

Evaluating the Options:

Option A (PIVOT) is the opposite of the required action. It is used to turn unique values from one column into multiple separate columns (narrow to wide), which is not what is happening in the exhibit.

Option C (INTERSECT) is a set operator that returns only the distinct rows that are present in both the first and second query results. It does not perform data rotation.

Option D (EXCEPT) is a set operator that returns rows from the first query that are not present in the second.

Option B is the 100% correct answer. It is the dedicated relational operator for converting column headers into row values, which is exactly the transformation required to move from the first image to the second. Mastering this operator is a critical skill for any SnowPro Advanced: Data Analyst when preparing messy source data for high-performance analytics.

When working within the Snowsight interface, the transition from a standalone worksheet to a dashboard component involves a change in how the underlying SQL and its associated metadata are managed. When a worksheet is converted or used to create a dashboard, the ownership and sharing model shifts to the dashboard level.

According to Snowflake's documentation on Snowsight collaboration, when a user creates a dashboard from a worksheet that was previously shared with others, the original worksheet's individual share settings are essentially superseded by the dashboard's own permissions. In many workflow scenarios within the UI, once the worksheet is finalized into a dashboard tile, the direct, independent access to that specific worksheet is severed for the original "sharees." Their permissions on that specific worksheet are revoked to prevent conflicting edits between the standalone version and the dashboard-integrated version.

Evaluating the Options:

Option A is incorrect because Snowsight manages the lifecycle of worksheets used in dashboards as part of the dashboard object; independent sharing of the underlying worksheet is typically disabled or revoked.

Option B and C are distractors; there is no mechanism in Snowsight that grants "additional" access or "temporary" loss during the creation process.

Option D is the 100% correct answer based on the SnowPro Data Analyst standard regarding Snowsight object ownership and sharing lifecycle. To allow others to see the work, the Analyst must now share the Dashboard itself, rather than relying on the previous worksheet-level permissions. This ensures a "single source of truth" for the visualization's logic.