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NEW QUESTION # 24
Let us say you have List of 50 Source files, which needs to be loaded into Snowflake internal stage. All these Source system files are already Brotli-compressed files. Which statement is correct with respect to Compression of Staged Files?
- A. When staging 50 compressed files in a Snowflake stage, the files are automatically com-pressed using gzip.
- B. Snowflake automatically detect Brotli Compression, will skip further compression of all 50 files.
- C. Even though Source files are already compressed, Snowflake do apply default gzip2 Compression to optimize the storage cost.
- D. Auto-detection is not yet supported for Brotli-compressed files; when staging or loading Brotli-compressed files, you must explicitly specify the compression method that was used.
Answer: D
Explanation:
Explanation
Auto-detection is not yet supported for Brotli-compressed files; when staging or loading Brotli-compressed files, you must explicitly specify the compression method that was used.
To Know more about Compression of Staged Files, please refer the link:
https://docs.snowflake.com/en/user-guide/intro-summary-loading.html#compression-of-staged-files
NEW QUESTION # 25
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
- B. Use API calls to access and integrate third-party datasets from AWS DataSync.
- C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).
- D. Use API calls to access and integrate third-party datasets from AWS Data Exchange.
Answer: B
Explanation:
Data Exchange is the AWS official third-party datasets repository:
https://aws.amazon.com/data-exchange
NEW QUESTION # 26
Can Masking policies be applied to virtual columns?
- A. TRUE
- B. FALSE
Answer: B
NEW QUESTION # 27
Which system role is recommended for a custom role hierarchy to be ultimately assigned to?
- A. ACCOUNTADMIN
- B. USERADMIN
- C. SECURITYADMIN
- D. SYSTEMADMIN
Answer: C
Explanation:
Explanation
The system role that is recommended for a custom role hierarchy to be ultimately assigned to is SECURITYADMIN. This role has the manage grants privilege on all objects in an account, which allows it to grant access privileges to other roles or revoke them as needed. This role can also create or modify custom roles and assign them to users or other roles. By assigning custom roles to SECURITYADMIN, the role hierarchy can be managed centrally and securely. The other options are not recommended system roles for a custom role hierarchy to be ultimately assigned to. Option A is incorrect because ACCOUNTADMIN is the most powerful role in an account, which has full access to all objects and operations. Assigning custom roles to ACCOUNTADMIN can pose a security risk and should be avoided. Option C is incorrect because SYSTEMADMIN is a role that has full access to all objects in the public schema of the account, but not to other schemas or databases. Assigning custom roles to SYSTEMADMIN can limit the scope and flexibility of the role hierarchy. Option D is incorrect because USERADMIN is a role that can manage users and roles in an account, but not grant access privileges to other objects. Assigning custom roles to USERADMIN can prevent the role hierarchy from controlling access to data and resources.
NEW QUESTION # 28
A Data Engineer is working on a Snowflake deployment in AWS eu-west-1 (Ireland). The Engineer is planning to load data from staged files into target tables using the copy into command Which sources are valid? (Select THREE)
- A. Internal stage on AWS eu-central-1 (Frankfurt)
- B. External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland)
- C. Internal stage on GCP us-central1 (Iowa)
- D. SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland)
- E. External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt)
- F. External stage on GCP us-central1 (Iowa)
Answer: B,E,F
Explanation:
Explanation
The valid sources for loading data from staged files into target tables using the copy into command are:
External stage on GCP us-central1 (Iowa): This is a valid source because Snowflake supports cross-cloud data loading from external stages on different cloud platforms and regions than the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland): This is a valid source because Snowflake supports data loading from external stages on the same cloud platform and region as the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt): This is a valid source because Snowflake supports cross-region data loading from external stages on different regions than the Snowflake deployment within the same cloud platform. The invalid sources are:
Internal stage on GCP us-central1 (Iowa): This is an invalid source because internal stages are always located on the same cloud platform and region as the Snowflake deployment. Therefore, an internal stage on GCP us-central1 (Iowa) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
Internal stage on AWS eu-central-1 (Frankfurt): This is an invalid source because internal stages are always located on the same region as the Snowflake deployment. Therefore, an internal stage on AWS eu-central-1 (Frankfurt) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland): This is an invalid source because SSO stands for Single Sign-On, which is a security integration feature in Snowflake, not a data staging option.
NEW QUESTION # 29
Ron, Snowflake Developer needs to capture change data (insert only) on the source views, for that he follows the below steps:
Enable change tracking on the source views & its underlying tables.
Inserted the data via Scripts scheduled with the help of Tasks.
then simply run the below Select statements.
1.select *
2.from test_table
3.changes(information => append_only)
4.at(timestamp => (select current_timestamp()));
Select the Correct Query Execution Output option below:
- A. Developer missed to create stream on the source table which can further query to cap-ture DML records.
- B. No Error reported, select command gives Changed records with Metadata columns as change tracking enabled on the Source views & its underlying tables.
- C. Select statement complied but gives erroneous results.
- D. Select query will fail with error: 'SQL compilation error-Incorrect Keyword "Chang-es()" found'
Answer: B
Explanation:
Explanation
As an alternative to streams, Snowflake supports querying change tracking metadata for tables or views using the CHANGES clause for SELECT statements. The CHANGES clause enables query-ing change tracking metadata between two points in time without having to create a stream with an explicit transactional offset.
To Know more about Snowflake CHANGES clause, please refer the mentioned link:
https://docs.snowflake.com/en/sql-reference/constructs/changes
NEW QUESTION # 30
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?
- A. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.
- B. Transform the data during the data load with Snowpipe by modifying the related copy into statement to include transformation steps such as case statements andJOIN'S.
- C. Use a create ok replace table as statement that references the staging table and includes all the transformation SQL. Use a task to run the full create or replace table as statement on a scheduled basis
- D. Schedule a task that dynamically retrieves the last time the task was run from information_schema-rask_hiSwOry and use that timestamp to process the delta of the new rows since the last time the task was run.
Answer: A
Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.
NEW QUESTION # 31
A company receives call logs as Amazon S3 objects that contain sensitive customer information.
The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access.
Which solution will meet these requirements with the LEAST effort?
- A. Use an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.
- B. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.
- C. Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.
- D. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.
Answer: B
NEW QUESTION # 32
What kind of Snowflake integration is required when defining an external function in Snowflake?
- A. Security integration
- B. API integration
- C. Notification integration
- D. HTTP integration
Answer: B
Explanation:
Explanation
An API integration is required when defining an external function in Snowflake. An API integration is a Snowflake object that defines how Snowflake communicates with an externalservice via HTTPS requests and responses. An API integration specifies parameters such as URL, authentication method, encryption settings, request headers, and timeout values. An API integration is used to create an external function object that invokes the external service from within SQL queries.
NEW QUESTION # 33
A media company uses software as a service (SaaS) applications to gather data by using third- party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?
- A. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
- B. Amazon Kinesis
- C. AWS Glue Data Catalog
- D. Amazon AppFlow
Answer: D
Explanation:
https://d1.awsstatic.com/solutions/guidance/architecture-diagrams/integrating-third-party-saas- data-using-amazon-appflow.pdf Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between Software as a Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks. It can store the raw data pulled from SaaS applications in Amazon S3, and integrates with AWS Glue Data Catalog to catalog and store metadata.
NEW QUESTION # 34
A company has a data warehouse that contains a table that is named Sales. The company stores the table in Amazon Redshift. The table includes a column that is named city_name. The company wants to query the table to find all rows that have a city_name that starts with "San" or
"El".
Which SQL query will meet this requirement?
- A. Select * from Sales where city_name ~ '$(San|El)*';
- B. Select * from Sales where city_name ~'$(San&El)*';
- C. Select * from Sales where city_name ~ '^(San&El)*';
- D. Select * from Sales where city_name ~ '^(San|El)*';
Answer: D
Explanation:
This query uses a regular expression pattern with the ~ operator. The caret ^ at the beginning of the pattern indicates that the match must start at the beginning of the string. (San|El) matches either "San" or "El", and * means zero or more of the preceding element. So this query will return all rows where city_name starts with either "San" or "El".
NEW QUESTION # 35
A company built a sales reporting system with Python, connecting to Snowflake using the Python Connector.
Based on the user's selections, the system generates the SQL queries needed to fetch the data for the report First it gets the customers that meet the given query parameters (on average 1000 customer records for each report run) and then it loops the customer records sequentially Inside that loop it runs the generated SQL clause for the current customer to get the detailed data for that customer number from the sales data table When the Data Engineer tested the individual SQL clauses they were fast enough (1 second to get the customers 0 5 second to get the sales data for one customer) but the total runtime of the report is too long How can this situation be improved?
- A. Define a clustering key for the sales data table
- B. Increase the number of maximum clusters of the virtual warehouse
- C. Increase the size of the virtual warehouse
- D. Rewrite the report to eliminate the use of the loop construct
Answer: D
Explanation:
Explanation
This option is the best way to improve the situation, as using a loop construct to run SQL queries for each customer is very inefficient and slow. Instead, the report should be rewritten to use a single SQL query that joins the customer and sales data tables and applies the query parameters as filters. This way, the report can leverage Snowflake's parallel processing and optimization capabilities and reduce the network overhead and latency.
NEW QUESTION # 36
Which privilege are required on an object (i.e. user or role) with USERADMIN Role can modify the object properties?
- A. OPEARTE
- B. OWNERSHIP
- C. MANAGE GRANTS
- D. MODIFY
Answer: B
NEW QUESTION # 37
A company is building a dashboard for thousands of Analysts. The dashboard presents the results of a few summary queries on tables that are regularly updated. The query conditions vary by tope according to what data each Analyst needs Responsiveness of the dashboard queries is a top priority, and the data cache should be preserved.
How should the Data Engineer configure the compute resources to support this dashboard?
- A. Create a virtual warehouse for every 250 Analysts Monitor to determine how many of these virtual warehouses are being utilized at capacity.
- B. Create a size XL virtual warehouse to support all the dashboard queries Monitor query runtimes to determine whether the virtual warehouse should be resized.
- C. Assign queries to a multi-cluster virtual warehouse with economy auto-scaling Allow the system to automatically start and stop clusters according to demand.
- D. Assign all queries to a multi-cluster virtual warehouse set to maximized mode Monitor to determine the smallest suitable number of clusters.
Answer: D
Explanation:
Explanation
This option is the best way to configure the compute resources to support this dashboard. By assigning all queries to a multi-cluster virtual warehouse set to maximized mode, the Data Engineer can ensure that there is enough compute capacity to handle thousands of concurrent queries from different analysts. A multi-cluster virtual warehouse can scale up or down by adding or removing clusters based on the load. A maximized scaling policy ensures that there is always at least one cluster running and that new clusters are added as soon as possible whenneeded. By monitoring the utilization and performance of the virtual warehouse, the Data Engineer can determine the smallest suitable number of clusters that can meet the responsiveness requirement and minimize costs.
NEW QUESTION # 38
A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.
A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.
Which actions should the data engineer take to meet this requirement? (Choose two.)
- A. Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.
- B. Reboot the RDS DB instance once each week.
- C. Upgrade to a larger instance size.
- D. Implement caching to reduce the database query load.
- E. Modify the database schema to include additional tables and indexes.
Answer: A,C
NEW QUESTION # 39
A stream called TRANSACTIONS_STM is created on top of a transactions table in a continuous pipeline running in Snowflake. After a couple of months, the TRANSACTIONS table is renamed transactiok3_raw to comply with new naming standards What will happen to the TRANSACTIONS _STM object?
- A. TRANSACTIONS _STMwill be automatically renamedTRANSACTIONS _RAW_STM.
- B. Reading from the traksactioks_3T>: stream will succeed for some time after the expected STALE_TIME.
- C. TRANSACTIONS _STMwill be stale and will need to be re-created
- D. TRANSACTIONS _STMwill keep working as expected
Answer: C
Explanation:
Explanation
A stream is a Snowflake object that records the history of changes made to a table. A stream is associated with a specific table at the time of creation, and it cannot be altered to point to a different table later. Therefore, if the source table is renamed, the stream will become stale and will need to be re-created with the new table name. The other options are not correct because:
TRANSACTIONS _STM will not keep working as expected, as it will lose track of the changes made to the renamed table.
TRANSACTIONS _STM will not be automatically renamed TRANSACTIONS _RAW_STM, as streams do not inherit the name changes of their source tables.
Reading from the transactions_stm stream will not succeed for some time after the expected STALE_TIME, as streams do not have a STALE_TIME property.
NEW QUESTION # 40
A Data Engineer executes a complex query and wants to make use of Snowflake s query results caching capabilities to reuse the results.
Which conditions must be met? (Select THREE).
- A. The results must be reused within 72 hours.
- B. The query must be executed using the same virtual warehouse.
- C. The new query must have the same syntax as the previously executed query.
- D. The USED_CACHED_RESULT parameter must be included in the query.
- E. The micro-partitions cannot have changed due to changes to other data in the table
- F. The table structure contributing to the query result cannot have changed
Answer: A,C,F
Explanation:
Explanation
Snowflake's query results caching capabilities allow users to reuse the results of previously executed queries without re-executing them. For this to happen, the following conditions must be met:
The results must be reused within 24 hours (not 72 hours), which is the default time-to-live (TTL) for cached results.
The query must be executed using any virtual warehouse (not necessarily the same one), as long as it is in the same region and account as the original query.
The USED_CACHED_RESULT parameter does not need to be included in the query, as it is enabled by default at the account level. However, it can be disabled or overridden at the session or statement level.
The table structure contributing to the query result cannot have changed, such as adding or dropping columns, changing data types, or altering constraints.
The new query must have the same syntax as the previously executed query, including whitespace and case sensitivity.
The micro-partitions cannot have changed due to changes to other data in the table, such as inserting, updating, deleting, or merging rows.
NEW QUESTION # 41
A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.
- B. Create an AWS Lambda Python function with provisioned concurrency.
- C. Create an AWS Lambda function. Ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events.
- D. Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).
Answer: B
Explanation:
AWS Lambda functions can be easily integrated with Amazon API Gateway to create RESTful APIs. This integration allows API Gateway to directly invoke the Lambda function when the API endpoint is hit.
NEW QUESTION # 42
While creating External function, Which Database object required with at least ACCOUNTAD-MIN privileges?
- A. None of the above required.
- B. API Integration
- C. SECURITY Integration
- D. STORAGE Integration
Answer: B
NEW QUESTION # 43
The following is returned fromSYSTEMCLUSTERING_INFORMATION () for a tablenamed orders with adate column named O_ORDERDATE:
What does the total_constant_partition_count value indicate about this table?
- A. The table is clustered very well on_ORDERDATE, as there are 493 micro-partitions that could not be significantly improved by reclustering
- B. The data inO_ORDERDATEhas a very low cardinality as there are 493 micro-partitions where there is only a single distinct value in that column for all rows in the micro-partition
- C. The data inO_ORDERDATEdoes not change very often as there are 493 micro-partitionscontaining rows where that column has not been modified since the row was created
- D. The table is not clustered well on O_ORDERDATE, as there are 493 micro-partitions where the range of values in that column overlap with every other micro partition in the table.
Answer: D
Explanation:
Explanation
The total_constant_partition_count value indicates the number of micro-partitions where the clustering key column has a constant value across all rows in the micro-partition. However, this does not necessarily mean that the table is clustered well on that column, as there could be other micro-partitions where the range of values in that column overlap with each other. This is the case for the orders table, as the clustering depth is 1, which means that every micro-partition overlaps with every other micro-partition on O_ORDERDATE. This indicates that the table is not clustered well on O_ORDERDATE and could benefit from reclustering.
NEW QUESTION # 44
Which use case would be BEST suited for the search optimization service?
- A. Data Scientists who seek specific JOIN statements with large volumes of data
- B. Analysts who need to perform aggregates over high cardinality columns
- C. Business users who need fast response times using highly selective filters
- D. Data Engineers who create clustered tables with frequent reads against clustering keys
Answer: C
Explanation:
Explanation
The use case that would be best suited for the search optimization service is business users who need fast response times using highly selective filters. The search optimization service is a feature that enables faster queries on tables with high cardinality columns by creating inverted indexes on those columns. High cardinality columns are columns that have a large number of distinct values, such as customer IDs, product SKUs, or email addresses. Queries that use highly selective filters on high cardinality columns can benefit from the search optimization service because they can quickly locate the relevant rows without scanning the entire table. The other options are not best suited for the search optimization service. Option A is incorrect because analysts who need to perform aggregates over high cardinality columns will not benefit from the search optimization service, as they will still need to scan all the rows that match the filter criteria. Option C is incorrect because data scientists who seek specific JOIN statements with large volumes of data will not benefit from the search optimization service, as they will still need to perform join operations that may involve shuffling or sorting data across nodes. Option D is incorrect because data engineers who create clustered tables with frequent reads against clustering keys will not benefit from the search optimization service, as they already have an efficient way to organize and access data based on clustering keys.
NEW QUESTION # 45
A company uses a data lake that is based on an Amazon S3 bucket. To comply with regulations, the company must apply two layers of server-side encryption to files that are uploaded to the S3 bucket. The company wants to use an AWS Lambda function to apply the necessary encryption.
Which solution will meet these requirements?
- A. Use server-side encryption with customer-provided keys (SSE-C) before files are uploaded.
- B. Use dual-layer server-side encryption with AWS KMS keys (DSSE-KMS).
- C. Use server-side encryption with AWS KMS keys (SSE-KMS).
- D. Use both server-side encryption with AWS KMS keys (SSE-KMS) and the Amazon S3 Encryption Client.
Answer: B
Explanation:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingDSSEncryption.html
NEW QUESTION # 46
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