Snowflake SnowPro Advanced Data Engineer Practice Test Questions, Exam Dumps

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SnowPro Advanced Data Engineer Snowflake Practice Test Questions and Exam Dumps

Question No 1:

A Data Engineer runs a complex query and wants to utilize Snowflake's query results caching capabilities to reuse the results.

Which of the following conditions must be met for the results to be cached and reused efficiently? (Choose three.)

A. The query results must be reused within 72 hours.
B. The query must be executed using the same virtual warehouse.
C. The USED_CACHED_RESULT parameter must be included in the query.
D. The table structure contributing to the query result cannot have changed.
E. The new query must have the same syntax as the previously executed query.
F. The micro-partitions cannot have changed due to changes to other data in the table.

Answer:
B. The query must be executed using the same virtual warehouse.
D. The table structure contributing to the query result cannot have changed.
F. The micro-partitions cannot have changed due to changes to other data in the table.

Explanation:

In Snowflake, query results caching is an important feature that allows queries to be executed more efficiently by reusing results from previously run queries. This not only saves computation time but also reduces the overall resource consumption in the cloud data warehouse. However, for the query results to be reused, specific conditions must be met to ensure that the cached results are still valid and applicable.

Let’s break down the key conditions that must be met for Snowflake to successfully reuse query results.

1. The Query Must Be Executed Using the Same Virtual Warehouse (Option B)

Snowflake's query result cache is tied to a specific virtual warehouse. This means that the query results can only be reused within the same virtual warehouse where the original query was executed. The virtual warehouse is responsible for executing the queries, and the results are stored temporarily in that warehouse's cache. If a new query is run on a different warehouse, even if the SQL query is identical, the result will not be retrieved from the cache, because each warehouse has its own isolated cache.

Therefore, to reuse query results, it is essential that the same virtual warehouse is used for executing the query that was used during the original execution.

2. The Table Structure Contributing to the Query Result Cannot Have Changed (Option D)

Snowflake’s cache depends on the integrity of the data that was used in the original query. If the table structure changes (e.g., columns are added or removed, or data types are modified), the previously cached results may no longer be applicable because they were based on the older table structure.

For the cache to be used effectively, the underlying structure of the table, including the schema and column configurations, must remain unchanged. If any modification occurs to the structure of the table, the cache will be invalidated and the query will be re-executed to generate new results.

3. The Micro-Partitions Cannot Have Changed Due to Changes in Other Data in the Table (Option F)

Snowflake stores data in a columnar format, and it organizes the data into micro-partitions. These micro-partitions are the smallest units of storage that Snowflake uses to manage data efficiently. When a query is executed, it may involve reading specific micro-partitions, and Snowflake may cache those results if the partitions have not changed.

If any changes occur in the data, such as insertions, deletions, or updates to the underlying table, the micro-partitions involved in the previous query may be altered. In such cases, Snowflake will invalidate the cache for those partitions, meaning the cached result can no longer be reused. To ensure that cached results are used, the underlying micro-partitions must not have changed since the query was last executed.

Invalid Conditions

Option A: The query results must be reused within 72 hours: While Snowflake’s query result cache can persist for up to 24 hours, it does not specifically require a 72-hour timeframe. Query results will be cached until they are invalidated by structural changes to the data or by certain operations like re-running the query on a different warehouse. Therefore, the 72-hour condition is not a strict requirement for caching.
Option C: The USED_CACHED_RESULT parameter must be included in the query: There is no need for the explicit inclusion of a USED_CACHED_RESULT parameter to enable query result caching in Snowflake. Snowflake automatically checks if cached results are available for reuse based on the conditions outlined above.
Option E: The new query must have the same syntax as the previously executed query: While the query syntax (SQL structure) must be similar, Snowflake does not require the query to be exactly the same. Minor changes, such as adding new filters or changing the order of clauses, may still allow for the reuse of the cache, provided the underlying data has not changed.

Snowflake’s query results caching can significantly improve the efficiency of repetitive queries by reusing results when the conditions are right. These conditions include executing the query on the same virtual warehouse, ensuring the table structure remains unchanged, and ensuring that the micro-partitions have not been modified. Understanding these conditions is critical for data engineers who want to take full advantage of Snowflake's caching capabilities. By adhering to these guidelines, organizations can optimize query performance and reduce unnecessary computation costs.

Question No 2:

A Data Engineer is tasked with loading JSON output from a software application into Snowflake using Snowpipe.

Which of the following best practices and recommendations should be followed to ensure optimal loading and performance in this scenario? (Choose three.)

A. Load large files (1 GB or larger).
B. Ensure that data files are 100-250 MB (or larger) in size and compressed.
C. Load a single huge array containing multiple records into a single table row.
D. Verify that each value of each unique element stores a single native data type (string or number).
E. Extract semi-structured data elements containing null values into relational columns before loading.
F. Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once per minute.

Answer:

The correct recommendations are B, D, and F.

B. Ensure that data files are 100-250 MB (or larger) in size and compressed.
D. Verify that each value of each unique element stores a single native data type (string or number).
F. Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once per minute.

Explanation:

When loading semi-structured data, such as JSON files, into Snowflake via Snowpipe, the goal is to optimize both the data loading process and the performance of subsequent queries. Snowpipe is an automatic data loading service in Snowflake that ingests data from cloud storage (e.g., AWS S3, Google Cloud Storage, or Azure Blob Storage) into Snowflake tables. Snowflake supports semi-structured data formats like JSON, Parquet, and Avro. To ensure efficiency and avoid errors during the loading process, it is important to follow best practices for file sizes, data structure, and ingestion frequency.

1. Recommendation B: Ensure that data files are 100-250 MB (or larger) in size and compressed.

Snowflake’s data loading mechanism works most efficiently when files are appropriately sized. A file size of 100-250 MB is ideal because it allows Snowflake to perform parallel loading of the files. Files that are too small will result in excessive metadata operations, which can increase overhead and reduce performance. Files larger than 1 GB may cause a strain on Snowpipe's processing and lead to slower performance due to the time it takes to process and load the large data. Additionally, compression helps reduce storage costs and speeds up the data loading process. Compressed files can be loaded faster and more efficiently, reducing the time it takes to upload and load the data.

2. Recommendation D: Verify that each value of each unique element stores a single native data type (string or number).

One of the key features of Snowflake’s support for semi-structured data is its ability to handle mixed data types within a single column. However, for optimal performance and simpler querying, it is a best practice to ensure that each element of a unique JSON key has a single, consistent data type. For example, if a particular field (e.g., age) contains both numeric and string values across different records, this could result in inefficient processing during querying. Consistent data types allow Snowflake’s optimization engine to better handle the data, improve query performance, and reduce unnecessary type coercion.

3. Recommendation F: Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once per minute.

This recommendation suggests creating small files (less than 100 MB) and staging them frequently, which is important for Snowpipe’s automatic ingestion process. Snowpipe loads data incrementally as files are added to cloud storage, so having small files ensures faster loading times and reduces the time to detect and load changes in data. Moreover, if files are staged frequently (at least once per minute), Snowpipe can efficiently handle continuous data streaming and avoid delays in processing.

Why Not the Other Options?

A. Load large files (1 GB or larger).
This is generally not recommended. Larger files (greater than 1 GB) can introduce performance bottlenecks, especially during parallel processing. Snowflake performs better when files are between 100 MB and 250 MB, allowing for parallelization without overwhelming the system.
C. Load a single huge array containing multiple records into a single table row.
Loading large arrays into a single table row is inefficient because it will force Snowflake to process the entire array as a single entity. This can hinder performance, especially when querying large datasets. It’s better to break down the data into smaller, more manageable rows for easier querying and more efficient processing.
E. Extract semi-structured data elements containing null values into relational columns before loading.
Snowflake’s VARIANT data type supports null values within semi-structured data. There is no need to convert null values into relational columns before loading, as Snowflake can efficiently handle nulls within the VARIANT type. Extracting such values unnecessarily would add extra processing steps and complexity.

In summary, to ensure efficient and optimized loading of JSON data into Snowflake using Snowpipe, it is crucial to follow best practices such as maintaining appropriate file sizes (100-250 MB), ensuring consistency in data types, and staging data files frequently. By adhering to these recommendations, data engineers can significantly improve the efficiency of their data loading processes and enhance overall performance in Snowflake.

Question No 3:

Given a table named SALES that has a clustering key defined on the column CLOSED_DATE, which of the following table functions would correctly return the average clustering depth for the SALES_REPRESENTATIVE column for the North American region?

Options:

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';

Answer:
B. select system$clustering_depth('Sales', 'sales_representative', 'region = ''North America''');

Explanation:

To understand why option B is correct, let's break down the components and logic behind clustering, as well as the functions involved.

1. Understanding Clustering:

Clustering in databases refers to how data is physically stored on disk. In Snowflake, a clustering key is defined on one or more columns of a table, and the data is physically ordered in storage based on the values of these columns. This can optimize query performance by reducing the amount of data that needs to be scanned.

In the context of the SALES table, the clustering key is on the column CLOSED_DATE, meaning the data is stored in a way that minimizes the effort required to read ranges of data based on closed dates.

2. The Concept of Clustering Depth:

Clustering depth refers to how effectively the data in a table is clustered according to the defined clustering key. If the data is well-clustered, each cluster will represent a compact set of values for the clustering key. On the other hand, if the clustering is not optimal, the clustering depth will be higher because more data partitions are needed to store the data.

Snowflake provides functions to retrieve clustering depth and other clustering-related information, which helps understand how the data is distributed across clusters and whether re-clustering is necessary.

3. The Functions Used in the Query:

system$clustering_information: This function provides general information about the clustering of a table. It returns details such as the clustering key and the total number of clusters.
system$clustering_depth: This function, on the other hand, returns the actual clustering depth for a given column or set of columns in a table. Clustering depth essentially measures how many partitions the data is spread across for the specified clustering key.

4. Evaluating Each Option:

Option A: select system$clustering_information('Sales', 'sales_representative', 'region = ''North America''');

The system$clustering_information function provides general clustering details, but it doesn't specifically return clustering depth. Therefore, this is incorrect because the question asks for the average clustering depth, not general information about the clustering.

Option B: select system$clustering_depth('Sales', 'sales_representative', 'region = ''North America''');

This is the correct choice. The system$clustering_depth function is designed to return the clustering depth for a given column, in this case, the sales_representative column, and it can also accept a filter condition ('region = ''North America''') to limit the results to a specific region. This directly answers the question of returning the average clustering depth for the specified column and region.

Option C: select system$clustering_depth('Sales', 'sales_representative') where region = 'North America';

This query is incorrect because the system$clustering_depth function does not support WHERE clauses directly. Filter conditions should be applied as part of the function’s argument rather than as a SQL filter. This would result in a syntax error.

Option D: select system$clustering_information('Sales', 'sales_representative') where region = 'North America';

Like Option A, this query is incorrect because system$clustering_information only provides general clustering information and does not return clustering depth. Furthermore, it incorrectly uses the WHERE clause, which is not applicable in this context for the function.

5. Conclusion:

The correct answer is B, as it uses the system$clustering_depth function with appropriate arguments, including the region filter, to return the clustering depth for the sales_representative column in the North American region.

6. Why Understanding Clustering Depth is Important:

Clustering depth is a crucial metric for performance optimization in Snowflake. A high clustering depth means that data is spread across many small partitions, which can increase query costs and reduce performance. Analyzing and managing clustering depth allows data engineers to optimize the physical storage of data, improving query performance by reducing the amount of data that needs to be read during a query.

Question No 4:

A Data Engineer is working on a Snowflake deployment hosted in AWS’s eu-west-1 (Ireland) region. The engineer plans to load data from staged files into target tables using the COPY INTO command.

Which of the following sources are valid for the COPY INTO command in this scenario? Choose three.

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 Answer:

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)

Explanation:

The COPY INTO command in Snowflake is used to load data into target tables from staged files, where the staging area can be either internal or external. The staging locations can differ based on the platform and region but must adhere to specific compatibility rules to work with Snowflake. Let’s review the different options provided in the question and why some are valid and others are not.

1. Internal Stage on GCP us-central1 (Iowa) – Option A

Invalid: Snowflake internal stages are tied to specific cloud providers, and for an AWS deployment, only internal stages hosted within AWS regions can be used. An internal stage in GCP (Google Cloud Platform) is not compatible with Snowflake’s AWS-based deployment. This means that an internal stage in GCP is not a valid source for the COPY INTO command when your Snowflake instance is deployed on AWS.

2. Internal Stage on AWS eu-central-1 (Frankfurt) – Option B

Invalid: While the internal stage in AWS (eu-central-1 in Frankfurt) is hosted on the same cloud provider as the Snowflake deployment, Snowflake does not allow loading data from an internal stage located in a different region than the Snowflake instance. Since the Snowflake instance is in the eu-west-1 (Ireland) region, it can only use internal stages that are located within that same region, not in a different AWS region like eu-central-1 (Frankfurt). This makes Option B invalid.

3. External Stage on GCP us-central1 (Iowa) – Option C

Invalid: Similar to the internal stage in GCP, Snowflake does not support using external stages in different cloud providers for an AWS-based Snowflake deployment. External stages can only reference storage locations (e.g., Amazon S3 buckets) that are within the same cloud provider as the Snowflake deployment. Thus, a stage located in GCP is incompatible with Snowflake’s AWS deployment. Hence, Option C is invalid.

4. External Stage in an Amazon S3 Bucket on AWS eu-west-1 (Ireland) – Option D

Valid: Snowflake allows external stages in Amazon S3 buckets, and for an AWS-based Snowflake deployment, an S3 bucket located within the same region as the Snowflake instance (in this case, eu-west-1 Ireland) is fully supported. Therefore, an external stage pointing to an S3 bucket in the same region as the Snowflake instance can be used successfully for data loading with the COPY INTO command. This makes Option D a valid source.

5. External Stage in an Amazon S3 Bucket on AWS eu-central-1 (Frankfurt) – Option E

Valid: Snowflake supports external stages located in Amazon S3 buckets that are hosted in different AWS regions than the Snowflake deployment region. In this case, even though the S3 bucket is in eu-central-1 (Frankfurt) and the Snowflake deployment is in eu-west-1 (Ireland), Snowflake can still access the S3 bucket in Frankfurt for loading data. Therefore, Option E is a valid source for the COPY INTO command.

6. SSD Attached to an Amazon EC2 Instance on AWS eu-west-1 (Ireland) – Option F

Valid: Snowflake allows loading data from an external stage pointing to storage devices like an Amazon EC2 instance, as long as the instance is within the same region as the Snowflake deployment. In this case, the EC2 instance is located in eu-west-1 (Ireland), which is the same region as the Snowflake deployment. As long as the data is accessible and the EC2 instance is properly configured to work with Snowflake’s external stages, this can be a valid data source for the COPY INTO command. Hence, Option F is valid.

In Snowflake, the COPY INTO command can only access staging areas that are either internal or external and are located in the same cloud provider as the Snowflake deployment. Furthermore, regions matter: for internal stages, the region must be the same as the Snowflake deployment. However, external stages can cross region boundaries within the same cloud provider. Therefore, valid sources for loading data into Snowflake in this scenario are:

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)

This highlights the flexibility of Snowflake in handling different types of external storage while enforcing specific compatibility and region restrictions.

Question No 5:

A Data Engineer is tasked with creating a new development database (DEV) as a clone of an existing production database (PROD). As part of the task, there is a requirement to disable Fail-safe for all the tables in the cloned development database.

Which SQL command should the Data Engineer use to meet these requirements?

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;

Answer:

The correct answer is C. CREATE TRANSIENT DATABASE DEV CLONE PROD;

Explanation:

In this scenario, the Data Engineer is asked to create a new development (DEV) database as a clone of the production (PROD) database, with the added requirement of disabling Fail-safe for all tables in the cloned database. Let’s break down each of the answer options and explain why option C is the correct one.

1. Understanding the Requirements:

Cloning the Production Database (PROD): The goal is to create a new development database (DEV) by cloning the existing production database (PROD). This is typically done to create a copy of the data from production for testing, development, or troubleshooting purposes without impacting the live environment.
Disabling Fail-safe: Fail-safe is a Snowflake feature that provides an additional level of data protection by allowing recovery of data after it has been dropped or permanently deleted. However, for a development environment, data protection requirements might be relaxed, and disabling Fail-safe can make the cloning process more efficient.

2. Review of Each Option:

Option A: CREATE DATABASE DEV CLONE PROD FAIL_SAFE = FALSE;

This option suggests using the FAIL_SAFE parameter directly in the CREATE DATABASE statement when cloning. However, Snowflake does not allow the use of the FAIL_SAFE parameter when creating or cloning a database. Fail-safe is a feature that operates at the table level and is not directly configurable when cloning a database at the time of creation. Therefore, this option is incorrect.

Option B: CREATE DATABASE DEV CLONE PROD;

This option is almost correct but does not meet the full requirement of disabling Fail-safe. The command creates a database named DEV as a clone of the PROD database, but it does not disable Fail-safe. Snowflake will retain Fail-safe settings in the cloned database as they are in the production database, and there is no specific parameter here to disable it. Thus, while it clones the database correctly, it does not meet the requirement to disable Fail-safe, making this option incorrect.

Option C: CREATE TRANSIENT DATABASE DEV CLONE PROD;

This option is correct. The key here is the use of the TRANSIENT keyword. A transient database in Snowflake is one that does not have Fail-safe enabled, which is exactly what is needed in this scenario. Fail-safe is not available for transient databases, which allows for more flexible development and testing environments where data recovery is not a priority. By using the CREATE TRANSIENT DATABASE command, the Data Engineer ensures that Fail-safe is automatically disabled. The cloning process itself will replicate all data from the PROD database to the DEV database, but without the Fail-safe protection.

Option D: CREATE DATABASE DEV CLONE PROD DATA_RETENTION_TIME_IN_DAYS = 0;

This option sets the DATA_RETENTION_TIME_IN_DAYS to 0, which determines how long Snowflake retains historical data for Time Travel. Setting this to 0 essentially disables the Time Travel feature for the cloned database, meaning there would be no ability to recover data from a prior state within the specified retention period. However, this does not directly address Fail-safe, which is the primary concern in this scenario. Fail-safe and Time Travel are two separate features in Snowflake. This option does not meet the requirement of disabling Fail-safe, making it incorrect.

To meet the requirements of creating a development database (DEV) as a clone of the production database (PROD) while ensuring that Fail-safe is disabled for all tables, the correct approach is to use the CREATE TRANSIENT DATABASE command, as it automatically disables Fail-safe. Therefore, Option C is the correct answer.

In practice, using a transient database is a common choice for development and testing environments because it provides a cost-effective and efficient way to manage data without the overhead of Fail-safe and Time Travel features, which are typically more important in production environments where data protection is critical.