Snowflake SnowPro Advanced Data Engineer Exam Dumps & Practice Test Questions

Question 1:

A data engineer needs to run a complex query in Snowflake and wants to benefit from the query results caching feature to reduce processing time and resource usage.

Which three of the following conditions must be satisfied for Snowflake to successfully reuse cached query results?

A. The query must be rerun within a 72-hour window.
B. The query must be executed using the same virtual warehouse as the original.
C. A USED_CACHED_RESULT parameter must be included in the SQL statement.
D. The structure of the tables referenced in the query must remain unchanged.
E. The SQL syntax of the repeated query must match exactly with the previous one.
F. The micro-partitions involved must not have been modified since the query was last executed.

Correct Answers: B, D, F

Explanation:

Snowflake’s query result caching is designed to enhance performance and efficiency by reusing previously computed query results when certain conditions are met. However, not all queries are eligible for reuse. Here’s a breakdown of the key factors that enable result reuse:

B. Same Virtual Warehouse
Query result caching in Snowflake is tied to the virtual warehouse that originally ran the query. Reusing cached results is only possible if the same virtual warehouse is used again. Even if the SQL syntax is identical, using a different warehouse results in a cache miss because each warehouse maintains its own execution context.

D. Unchanged Table Structure
For cached results to remain valid, the structure of the tables used in the query must remain the same. This means no changes to columns, data types, or schema definitions. Any structural modification invalidates the cache because the results may no longer reflect the current state of the table.

F. Unmodified Micro-Partitions
Snowflake organizes data in micro-partitions. If any part of the table data involved in the original query is altered (e.g., through updates, inserts, or deletes), the affected micro-partitions are updated. These changes invalidate the previously cached result. Only if the data remains untouched can the cached results be reused.

Why Not the Other Options?

• A is incorrect because Snowflake’s result cache typically persists for 24 hours, not 72.

• C is incorrect because there's no such query parameter required; caching happens automatically.

• E is partially true—similar syntax helps—but Snowflake uses query plan matching, so exact matching isn't strictly necessary if the logic is consistent.

Understanding these conditions is vital for engineers aiming to leverage caching for faster and cost-effective query execution.

Question 2:

A data engineer is responsible for importing JSON-formatted data from an application into Snowflake using Snowpipe.

Which of the following are recommended best practices to ensure high performance and efficient data ingestion?

A. Use very large files (1 GB or more) for each load.
B. Use compressed files ranging from 100–250 MB in size.
C. Store multiple JSON records in a single massive array within one table row.
D. Make sure each JSON element uses a consistent native data type like string or number.
E. Convert null values from semi-structured elements into relational columns before loading.
F. Upload small files (under 100 MB) to cloud storage more than once per minute.

Correct Answers: B, D, F

Explanation:

When ingesting semi-structured JSON data using Snowpipe, Snowflake provides powerful capabilities to automatically load and optimize data. However, efficiency depends heavily on how the data is prepared and staged. The following are key practices for ensuring smooth, high-performance ingestion:

B. Optimal File Size and Compression
Snowflake performs best when file sizes are between 100 MB and 250 MB. Files in this size range strike the right balance between efficient parallelism and manageable system overhead. Additionally, compressed files are highly recommended as they reduce both upload times and storage costs, speeding up the overall loading process.

D. Consistent Data Types
For each key-value pair in the JSON structure, values should maintain a consistent native data type (either string, number, boolean, etc.). Inconsistencies—like using both strings and integers for the same field—can complicate parsing and degrade query performance. Maintaining a stable schema ensures efficient storage and more predictable query behavior.

F. Small, Frequent File Uploads
While moderately sized files are ideal, in streaming scenarios or when real-time ingestion is needed, Snowflake recommends using smaller files (under 100 MB) and staging them frequently (e.g., every 1 minute or less). This ensures near-real-time data ingestion without overwhelming Snowpipe or introducing latency.

Why Not the Other Options?

• A is incorrect because files over 1 GB can lead to performance bottlenecks and slower processing.

• C is inefficient; loading multiple records into a single row as a large array complicates querying and reduces performance.

• E is unnecessary since Snowflake supports null values directly within semi-structured types like VARIANT.

By adhering to these guidelines—optimizing file size, using consistent data types, and staging files appropriately—data engineers can maximize the performance and reliability of their Snowpipe-based data ingestion workflows.

Question 3:

You have a table named SALES, where a clustering key has been defined on the CLOSED_DATE column. To calculate the average clustering depth for the SALES_REPRESENTATIVE column, filtered specifically for the "North America" region.

Which of the following queries should be used?

A. SELECT system$clustering_information('Sales', 'sales_representative', 'region = ''North America''');
B. SELECT system$clustering_depth('Sales', 'sales_representative', 'region = ''North America''');
C. SELECT system$clustering_depth('Sales', 'sales_representative') WHERE region = 'North America';
D. SELECT system$clustering_information('Sales', 'sales_representative') WHERE region = 'North America';

Correct Answer: B

Explanation:

In Snowflake, clustering refers to the physical arrangement of table data on disk based on certain key columns, which can improve performance by reducing the data scanned during queries. When you define a clustering key such as CLOSED_DATE, Snowflake organizes the underlying micro-partitions to group similar values of that column together.

To assess how effectively the data is organized according to the clustering key, Snowflake provides two important table functions: system$clustering_information and system$clustering_depth. While both are useful, they serve different purposes.

• system$clustering_information gives a high-level overview, such as what the clustering keys are and how many micro-partitions exist. However, it doesn’t give detailed metrics like clustering depth.
  
  

• system$clustering_depth, in contrast, returns the clustering depth — a metric that shows how many partitions contain overlapping values for a given column. A lower depth indicates more effective clustering.

The correct option, B, uses the system$clustering_depth function and also includes a filter condition for region = 'North America' within the function call. This is the proper way to scope the analysis to a specific region. Filters must be passed as string parameters inside the function call — they cannot be appended externally using a WHERE clause.

Options C and D are syntactically incorrect because they misuse the WHERE clause with system functions that don’t support it directly. Option A, though syntactically correct, calls the wrong function and therefore won't return the required clustering depth.

By using option B, you get an accurate measure of how well data for sales_representative is clustered in the North American region, which is crucial for optimizing performance.

Question 4:

A data engineer is working with Snowflake hosted on AWS in the eu-west-1 (Ireland) region. They need to load data into tables using the COPY INTO command. 

Which three of the following sources are valid for loading data in this scenario?

A. Internal stage on GCP us-central1 (Iowa)
B. Internal stage on AWS eu-central-1 (Frankfurt)
C. External stage on GCP us-central1 (Iowa)
D. External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland)
E. External stage in an Amazon S3 bucket on AWS eu-central-1 (Frankfurt)
F. SSD attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland)

Correct Answers: D, E, F

Explanation:

When using the COPY INTO command in Snowflake to load data from staged files into tables, it's important to understand what types of storage stages are supported, and how regional and provider restrictions apply. Stages in Snowflake can be internal or external, and they must be compatible with the region and cloud provider where Snowflake is hosted.

Let’s analyze each option in this context:

• Option A: Invalid. Snowflake cannot access internal stages hosted on a different cloud provider (GCP) if the deployment is on AWS. Internal stages are tied to the specific cloud and region of your Snowflake instance.

• Option B: Invalid. Even though this is an AWS internal stage, it is located in a different AWS region (eu-central-1) than the Snowflake deployment (eu-west-1). Snowflake does not support loading from internal stages across regions.

• Option C: Invalid. External stages hosted on a different cloud provider (GCP) are not supported for a Snowflake account deployed in AWS. Both internal and external stages must reside within the same cloud ecosystem.

• Option D: Valid. This option uses an Amazon S3 bucket in the same region as the Snowflake deployment (eu-west-1). Snowflake fully supports this configuration for external stages.

• Option E: Valid. External stages using Amazon S3 buckets are allowed across different AWS regions. While eu-central-1 is not the same as the deployment region, Snowflake can still load from it since it's within the same cloud provider.

• Option F: Valid. An SSD attached to an EC2 instance in the same AWS region (eu-west-1) is an acceptable data source. Snowflake can load data from custom file systems if they are properly staged and accessible.

In summary, valid data sources for the COPY INTO command in this AWS-based Snowflake environment include:

• D: S3 bucket in eu-west-1

• E: S3 bucket in eu-central-1

• F: EC2 SSD in eu-west-1

These comply with Snowflake’s requirement for cloud provider compatibility and acceptable region handling for external data loading.

Question 5:

A Data Engineer needs to create a development (DEV) database by cloning the existing production (PROD) database. As part of the task, the engineer must ensure that all tables in the DEV database are created without Fail-safe enabled.

Which of the following SQL commands fulfills this requirement?

A. CREATE DATABASE DEV CLONE PROD FAIL_SAFE = FALSE;
B. CREATE DATABASE DEV CLONE PROD;
C. CREATE TRANSIENT DATABASE DEV CLONE PROD;
D. CREATE DATABASE DEV CLONE PROD DATA_RETENTION_TIME_IN_DAYS = 0;

Correct Answer: C

Explanation:

The requirement is to clone a production database into a development environment while ensuring that Fail-safe is disabled for all tables in the cloned database. In Snowflake, Fail-safe is a feature designed to protect data from permanent loss, allowing Snowflake Support to recover deleted data during a 7-day recovery window. However, this feature is often unnecessary for development or testing environments where data durability isn't mission-critical.

To achieve this, the correct approach is to create a transient database. A transient database differs from a standard (permanent) database because it does not include Fail-safe protection. Thus, using the command CREATE TRANSIENT DATABASE DEV CLONE PROD; meets both criteria: cloning the PROD database and ensuring Fail-safe is disabled.

Let's evaluate the other options:

• Option A suggests directly setting FAIL_SAFE = FALSE in the CREATE DATABASE command. However, Snowflake does not support the FAIL_SAFE parameter in that context. This option is syntactically incorrect.

• Option B will successfully clone the PROD database into DEV, but it does so as a permanent database by default. This means that Fail-safe remains enabled, which violates the requirement.

• Option D sets DATA_RETENTION_TIME_IN_DAYS = 0, which disables Time Travel but has no effect on Fail-safe. Time Travel and Fail-safe are distinct Snowflake features; addressing one does not configure the other.

Therefore, only Option C uses the correct database type—transient—that inherently lacks Fail-safe protection, fulfilling the objective efficiently. This method is ideal for DEV and test environments due to reduced storage costs and simpler cleanup.

Question 6:

Which of the following Snowflake features is best used to reduce query time on frequently accessed, unchanging datasets?

A. Streams
B. Materialized Views
C. External Tables
D. Clustering Keys

Correct Answer: B

Explanation:

Materialized Views are ideal when you want to improve performance on queries that access frequently used but rarely changing datasets. Unlike standard views, which recalculate data every time they are queried, materialized views store the results of a query and automatically refresh them based on changes in the base tables.

This feature is especially useful in scenarios where performance is critical, and where querying large amounts of raw data would be inefficient. For instance, if you have a report that aggregates sales data daily, creating a materialized view on that report query will speed up retrieval since Snowflake stores the precomputed data.

Option A (Streams) is used for change data capture (CDC) – identifying what changes (INSERTs, UPDATEs, DELETEs) happened to a table – but doesn’t improve query performance directly.

C (External Tables) allow querying data stored outside Snowflake (e.g., in S3), but they typically have slower performance than native Snowflake tables.

D (Clustering Keys) can help optimize certain queries by improving micro-partition pruning but do not store precomputed results like materialized views do.

Thus, Materialized Views are the best option for reducing query time on frequently accessed static or slowly changing datasets.

Question 7:

When designing a Snowflake data pipeline that ingests semi-structured JSON data, which file format and table type should you prioritize for optimal performance and querying?

A. CSV with External Table
B. Parquet with Transient Table
C. JSON with Variant column in a Native Table
D. XML with Temporary Table

Correct Answer: C

Explanation:

Snowflake provides strong support for semi-structured data, such as JSON, through its VARIANT data type. When dealing with JSON data ingestion, the best approach is to load the data into a VARIANT column in a native (permanent or transient) table.

The VARIANT column stores semi-structured data in a flexible format that Snowflake can parse, index, and query efficiently using dot notation or functions like FLATTEN. This makes C (JSON with VARIANT column in a native table) the best option.

Option A is suboptimal because CSV is a flat format and doesn’t support nested or hierarchical data structures well, and external tables are slower compared to internal tables for active queries.

Option B uses Parquet, which is efficient and columnar but is more suited for structured data or analytics via external tables. Also, transient tables are used when data persistence isn’t required long-term – this doesn't optimize for JSON querying.

Option D (XML) is supported, but JSON is more commonly used and better supported with functions and integration. Temporary tables are session-specific and not useful for ongoing pipelines.

Therefore, for optimal ingestion and querying of JSON, a VARIANT column in a native Snowflake table is the correct and most efficient approach.

Question 8:

What is the most efficient way to implement incremental data loading in Snowflake when tracking changes from a source system?

A. Replacing the entire table each time
B. Using Snowflake Streams and Tasks
C. Creating a full snapshot every day
D. Using clustering keys with timestamp filtering

Correct Answer: B

Explanation:

Snowflake Streams and Tasks are designed specifically for incremental data processing. A Stream tracks changes (inserts, updates, deletes) to a table since the last time it was read. A Task can automate SQL execution on a schedule or in response to a trigger, making it ideal for event-driven or scheduled ETL workflows.

Together, Streams and Tasks enable Change Data Capture (CDC), allowing you to efficiently process only the delta (changed) data, rather than reprocessing everything. This significantly reduces compute costs and improves pipeline efficiency.

Option A (replacing the table) is inefficient for large datasets and increases storage and compute overhead.

Option C (daily full snapshots) can be useful in some audit scenarios but is storage-heavy and not efficient for pipelines where only changed records are relevant.

Option D (timestamp filtering with clustering keys) can help if the source system tracks update timestamps, but it still requires careful design and doesn't provide automatic tracking of DML operations like Streams do.

Thus, for modern, efficient, and automated incremental data loading in Snowflake, Streams and Tasks are the preferred tools.

Question 9:

A Snowflake data engineer wants to optimize a long-running transformation job that uses a large number of semi-structured JSON files. 

Which feature should the engineer implement to improve query performance?

A. Use Materialized Views
B. Create External Tables
C. Use LATERAL FLATTEN with VARIANT columns
D. Apply Clustering Keys on VARIANT columns

Correct Answer: C

Explanation:

When working with semi-structured data such as JSON in Snowflake, performance optimization becomes important—especially for large-scale transformation jobs. JSON data is stored in VARIANT columns, which allows Snowflake to ingest and manage data flexibly. However, to extract and query nested elements efficiently, engineers must process this data appropriately.

The LATERAL FLATTEN function in Snowflake is specifically designed to normalize nested structures, such as arrays or deeply nested JSON objects. It allows each nested element to be returned as a separate row, making the data easier to query and transform.

Using LATERAL FLATTEN with VARIANT columns enables the query engine to effectively traverse and optimize access to complex, hierarchical data. This approach improves query performance significantly by reducing overhead and enabling parallel processing of nested items.

Let’s review the other options:

• A. Materialized Views: While these can help in performance by caching query results, they are not optimal for semi-structured JSON unless the views target extracted scalar values.

• B. External Tables: These are useful for querying data stored in external stages (e.g., S3), but they do not improve performance on their own during complex transformations.

• D. Clustering Keys on VARIANT columns: Snowflake doesn’t support direct clustering on semi-structured columns. Clustering keys work best with scalar columns and structured data.

Thus, using LATERAL FLATTEN with VARIANT columns is the most appropriate method for improving performance when handling large JSON datasets.

Question 10:

You are designing a data pipeline in Snowflake that must guarantee consistency and recoverability across multiple sequential data transformations. Which feature should be used to meet this requirement?

A. Streams and Tasks
B. Fail-Safe
C. Time Travel
D. Multi-statement Transactions

Correct Answer: D

Explanation:

When building complex data pipelines in Snowflake, it's often necessary to run multiple transformations in sequence while guaranteeing that either all succeed or none are applied. This is especially important for data consistency, auditability, and rollback in case of failures.

The best feature for this scenario is Multi-statement Transactions.

Snowflake supports ACID-compliant transactions, allowing multiple SQL statements (such as INSERT, UPDATE, or DELETE) to be grouped together. If any one of these statements fails, the entire transaction is rolled back, ensuring the database remains in a consistent state. This is crucial for maintaining data integrity in pipelines that process multiple stages.

Let’s evaluate the alternatives:

• A. Streams and Tasks: These are used for incremental change tracking and automation, but they do not guarantee atomicity across multiple transformations. They are useful in orchestrated pipelines but not for ensuring consistency across a single logical unit of work.

• B. Fail-Safe: This is a disaster recovery feature used by Snowflake to recover data after Time Travel expires. It is not designed for transactional consistency.

• C. Time Travel: While this allows reverting to previous data versions, it does not prevent partial execution of transformations. It’s more suited for recovery or auditing.

Only Multi-statement Transactions provide the atomic behavior needed to ensure that data changes are committed only when all operations succeed. This makes it the most appropriate choice for building consistent and recoverable pipelines.