ARA-C01 Exam Info and Free Practice Test All-in-One Exam Guide Jul-2024 [Q17-Q35]

ARA-C01 Exam Info and Free Practice Test All-in-One Exam Guide Jul-2024

Pass Snowflake ARA-C01 Actual Free Exam Q&As Updated Dump Jul 18, 2024

To earn the Snowflake ARA-C01 certification, candidates must pass a rigorous exam that covers a wide range of topics related to Snowflake architecture and functionality. ARA-C01 exam consists of 60 multiple-choice questions and is timed for 90 minutes. ARA-C01 exam is computer-based and can be taken at any authorized testing center or remotely from a candidate's home or office.

Snowflake ARA-C01 certification exam is computer-based and consists of 60 multiple-choice questions. ARA-C01 exam is timed, and candidates have 90 minutes to complete it. To pass the exam, candidates must score at least 70%. ARA-C01 exam is administered by Pearson VUE and can be taken at any of their authorized testing centers.

 

NEW QUESTION # 17
Which columns can be included in an external table schema? (Select THREE).

• A. METADATASROW_ID
• B. METADATASISUPDATE
• C. METADATASEXTERNAL TABLE PARTITION
• D. VALUE
• E. METADATAS FILE_ROW_NUMBER
• F. METADAT A$ FILENAME

Answer: D,E,F

Explanation:
An external table schema defines the columns and data types of the data stored in an external stage. All external tables include the following columns by default:
* VALUE: A VARIANT type column that represents a single row in the external file.
* METADATA$FILENAME: A pseudocolumn that identifies the name of each staged data file included in the external table, including its path in the stage.
* METADATA$FILE_ROW_NUMBER: A pseudocolumn that shows the row number for each record in a staged data file.
You can also create additional virtual columns as expressions using the VALUE column and/or the pseudocolumns. However, the following columns are not valid for external tables and cannot be included in the schema:
* METADATASROW_ID: This column is only available for internal tables and shows the unique identifier for each row in the table.
* METADATASISUPDATE: This column is only available for internal tables and shows whether the row was inserted or updated by a merge operation.
* METADATASEXTERNAL TABLE PARTITION: This column is not a valid column name and does not exist in Snowflake.
References: Introduction to External Tables, CREATE EXTERNAL TABLE

NEW QUESTION # 18
To increase performance, materialized views can be created on external table without any additional cost

• A. TRUE
• B. FALSE

Answer: B

NEW QUESTION # 19
How can the Snowpipe REST API be used to keep a log of data load history?

• A. Call insertReport every 8 minutes for a 10-minute time range.
• B. Call loadHistoryScan every minute for the maximum time range.
• C. Call loadHistoryScan every 10 minutes for a 15-minute time range.
• D. Call insertReport every 20 minutes, fetching the last 10,000 entries.

Answer: C

Explanation:
* Snowpipe is a service that automates and optimizes the loading of data from external stages into Snowflake tables. Snowpipe uses a queue to ingest files as they become available in the stage. Snowpipe also provides REST endpoints to load data and retrieve load history reports1.
* The loadHistoryScan endpoint returns the history of files that have been ingested by Snowpipe within a specified time range. The endpoint accepts the following parameters2:
* pipe: The fully-qualified name of the pipe to query.
* startTimeInclusive: The start of the time range to query, in ISO 8601 format. The value must be within the past 14 days.
* endTimeExclusive: The end of the time range to query, in ISO 8601 format. The value must be later than the start time and within the past 14 days.
* recentFirst: A boolean flag that indicates whether to return the most recent files first or last. The default value is false, which means the oldest files are returned first.
* showSkippedFiles: A boolean flag that indicates whether to include files that were skipped by
* Snowpipe in the response. The default value is false, which means only files that were loaded are returned.
* The loadHistoryScan endpoint can be used to keep a log of data load history by calling it periodically with a suitable time range. The best option among the choices is D, which is to call loadHistoryScan every 10 minutes for a 15-minute time range. This option ensures that the endpoint is called frequently enough to capture the latest files that have been ingested, and that the time range is wide enough to avoid missing any files that may have been delayed or retried by Snowpipe. The other options are either too infrequent, too narrow, or use the wrong endpoint3.
References:
* 1: Introduction to Snowpipe | Snowflake Documentation
* 2: loadHistoryScan | Snowflake Documentation
* 3: Monitoring Snowpipe Load History | Snowflake Documentation

NEW QUESTION # 20
A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schema. One of the requirements is to have online recovery of data on a rolling 7-day basis.
After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.
What would cause this to occur? (Choose two.)

• A. The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.
• B. The staging tables are of the TRANSIENT type.
• C. The tables exceed the 1 TB limit for data recovery.
• D. The staging schema has not been setup for MANAGED ACCESS.
• E. The DevOps role should be granted ALLOW_RECOVERY privilege on the staging schema.

Answer: A,B

Explanation:
Explanation
* The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for an object (database, schema, or table) in Snowflake. This parameter specifies the number of days for which historical data is preserved and can be accessed using Time Travel operations (SELECT, CREATE ... CLONE, UNDROP)1.
* The requirement for recovery of staging tables on a rolling 7-day basis means that the DATA_RETENTION_TIME_IN_DAYS parameter should be set to 7 at the database level. However, this parameter can be overridden at the lower levels (schema or table) if they have a different value1.
* Therefore, one possible cause for certain tables to remain unrecoverable past 1 day is that the DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day. This would override the database level setting and limit the Time Travel retention period for all the tables in the schema to 1 day. To fix this, the parameter should be unset or set to 7 at the schema level1. Therefore, option B is correct.
* Another possible cause for certain tables to remain unrecoverable past 1 day is that the staging tables are of the TRANSIENT type. Transient tables are tables that do not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated2. To fix this, the tables should be created as permanent tables, which can have a Time Travel retention period of up to 90 days1. Therefore, option D is correct.
* Option A is incorrect because the MANAGED ACCESS feature is not related to the data recovery requirement. MANAGED ACCESS is a feature that allows granting access privileges to objects without explicitly granting the privileges to roles. It does not affect the Time Travel retention period or the data availability3.
* Option C is incorrect because there is no 1 TB limit for data recovery in Snowflake. The data storage size does not affect the Time Travel retention period or the data availability4.
* Option E is incorrect because there is no ALLOW_RECOVERY privilege in Snowflake. The privilege required to perform Time Travel operations is SELECT, which allows querying historical data in tables5.
References: : Understanding & Using Time Travel : Transient Tables : Managed Access : Understanding Storage Cost : Table Privileges

NEW QUESTION # 21
What transformations are supported in the below SQL statement? (Select THREE).
CREATE PIPE ... AS COPY ... FROM (...)

• A. Columns can be omitted.
• B. Columns can be reordered.
• C. The ON ERROR - ABORT statement command can be used.
• D. Incoming data can be joined with other tables.
• E. Type casts are supported.
• F. Data can be filtered by an optional where clause.

Answer: A,B,F

NEW QUESTION # 22
Which system functions does Snowflake provide to monitor clustering information within a table (Choose two.)

• A. SYSTEM$CLUSTERING_PERCENT
• B. SYSTEM$CLUSTERING_DEPTH
• C. SYSTEM$CLUSTERING_USAGE
• D. SYSTEM$CLUSTERING_INFORMATION
• E. SYSTEM$CLUSTERING_KEYS

Answer: B,D

Explanation:
According to the Snowflake documentation, these two system functions are provided by Snowflake to monitor clustering information within a table. A system function is a type of function that allows executing actions or returning information about the system. A clustering key is a feature that allows organizing data across micro-partitions based on one or more columns in the table. Clustering can improve query performance by reducing the number of files to scan.
* SYSTEM$CLUSTERING_INFORMATION is a system function that returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns a JSON string with the clustering information. The clustering information includes the cluster by keys, the total partition count, the total constant partition count, the average overlaps, and the average depth1.
* SYSTEM$CLUSTERING_DEPTH is a system function that returns the clustering depth for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns an integer value with the clustering depth. The clustering depth is the maximum number of overlapping micro-partitions for any micro-partition in the table. A lower clustering depth indicates a better clustering2.
References:
* SYSTEM$CLUSTERING_INFORMATION | Snowflake Documentation
* SYSTEM$CLUSTERING_DEPTH | Snowflake Documentation

NEW QUESTION # 23
What is a key consideration when setting up search optimization service for a table?

• A. Search optimization service works best with a column that has a minimum of 100 K distinct values.
• B. Search optimization service can significantly improve query performance on partitioned external tables.
• C. Search optimization service can help to optimize storage usage by compressing the data into a GZIP format.
• D. The table must be clustered with a key having multiple columns for effective search optimization.

Answer: A

NEW QUESTION # 24
What actions are permitted when using the Snowflake SQL REST API? (Select TWO).

• A. The use of a ROLLBACK command
• B. The use of a CALL command to a stored procedure which returns a table
• C. The use of a GET command
• D. The use of a PUT command
• E. Submitting multiple SQL statements in a single call

Answer: B,E

NEW QUESTION # 25
An Architect is designing a pipeline to stream event data into Snowflake using the Snowflake Kafka connector. The Architect's highest priority is to configure the connector to stream data in the MOST cost-effective manner.
Which of the following is recommended for optimizing the cost associated with the Snowflake Kafka connector?

• A. Utilize a higher Buffer.flush.time in the connector configuration.
• B. Utilize a lower Buffer.size.bytes in the connector configuration.
• C. Utilize a lower Buffer.count.records in the connector configuration.
• D. Utilize a higher Buffer.size.bytes in the connector configuration.

Answer: A

Explanation:
Explanation
The minimum value supported for the buffer.flush.time property is 1 (in seconds). For higher average data flow rates, we suggest that you decrease the default value for improved latency. If cost is a greater concern than latency, you could increase the buffer flush time. Be careful to flush the Kafka memory buffer before it becomes full to avoid out of memory exceptions.https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-kafka

NEW QUESTION # 26
Which query will identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse to improve the performance of a particular workload?

• A.
• B.
• C.
• D.

Answer: D

Explanation:
A multi-cluster warehouse is a virtual warehouse that can scale compute resources by adding or removing clusters based on the workload demand. A multi-cluster warehouse can improve the performance of a particular workload by reducing the query queue time and the data spillage to local storage. To identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse, you need to analyze the query history and look for the following indicators:
High average queued load: This metric shows the average number of queries waiting in the queue for each warehouse cluster. A high value indicates that the warehouse is overloaded and cannot handle the concurrency demand.
High bytes spilled to local storage: This metric shows the amount of data that was spilled from memory to local disk during query processing. A high value indicates that the warehouse size is too small and cannot fit the data in memory.
High variation in workload: This metric shows the fluctuation in the number of queries submitted to the warehouse over time. A high variation indicates that the workload is unpredictable and dynamic, and requires a flexible scaling policy.
The query in option C is the best one to identify these indicators, as it selects the date, warehouse name, bytes spilled to local storage, and sum of average queued load from the query history table, and filters the results where bytes spilled to local storage is greater than zero. This query will show the days and warehouses that experienced data spillage and high queue time, and could benefit from a multi-cluster warehouse with auto-scale mode.
The query in option A is not correct, as it only selects the date and warehouse name, and does not include any metrics to measure the performance of the workload. The query in option B is not correct, as it selects the date, warehouse name, and average execution time, which is not a good indicator of the need for a multi-cluster warehouse. The query in option D is not correct, as it selects the date, warehouse name, and average credits used, which is not a good indicator of the need for a multi-cluster warehouse either.

NEW QUESTION # 27
Database DB1 has schema S1 which has one table, T1.
DB1 --> S1 --> T1
The retention period of EG1 is set to 10 days.
The retention period of s: is set to 20 days.
The retention period of t: Is set to 30 days.
The user runs the following command:
Drop Database DB1;
What will the Time Travel retention period be for T1?

• A. 10 days
• B. 20 days
• C. 30 days
• D. 37 days

Answer: C

Explanation:
The Time Travel retention period for T1 will be 30 days, which is the retention period set at the table level.
The Time Travel retention period determines how long the historical data is preserved and accessible for an object after it is modified or dropped. The Time Travel retention period can be set at the account level, the database level, the schema level, or the table level. The retention period set at the lowest level of the hierarchy takes precedence over the higher levels. Therefore, the retention period set at the table level overrides the retention periods set at the schema level, the database level, or the account level. When the user drops the database DB1, the table T1 is also dropped, but the historical data is still preserved for 30 days, which is the retention period set at the table level. The user can use the UNDROP command to restore the table T1 within the 30-day period. The other options are incorrect because:
* 10 days is the retention period set at the database level, which is overridden by the table level.
* 20 days is the retention period set at the schema level, which is also overridden by the table level.
* 37 days is not a valid option, as it is not the retention period set at any level.
References:
* Understanding & Using Time Travel
* AT | BEFORE
* Snowflake Time Travel & Fail-safe

NEW QUESTION # 28
Which of the following are characteristics of how row access policies can be applied to external tables?
(Choose three.)

• A. While cloning a database, both the row access policy and the external table will be cloned.
• B. An external table can be created with a row access policy, and the policy can be applied to the VALUE column.
• C. A row access policy can be applied to the VALUE column of an existing external table.
• D. A row access policy cannot be applied to a view created on top of an external table.
• E. External tables are supported as mapping tables in a row access policy.
• F. A row access policy cannot be directly added to a virtual column of an external table.

Answer: B,C,F

Explanation:
These three statements are true according to the Snowflake documentation and the web search results. A row access policy is a feature that allows filtering rows based on user-defined conditions. A row access policy can be applied to an external table, which is a table that reads data from external files in a stage. However, there are some limitations and considerations for using row access policies with external tables.
* An external table can be created with a row access policy by using the WITH ROW ACCESS POLICY clause in the CREATE EXTERNAL TABLE statement. The policy can be applied to the VALUE column, which is the column that contains the raw data from the external files in a VARIANT data type1.
* A row access policy can also be applied to the VALUE column of an existing external table by using the ALTER TABLE statement with the SET ROW ACCESS POLICY clause2.
* A row access policy cannot be directly added to a virtual column of an external table. A virtual column is a column that is derived from the VALUE column using an expression. To apply a row access policy to a virtual column, the policy must be applied to the VALUE column and the expression must be repeated in the policy definition3.
* External tables are not supported as mapping tables in a row access policy. A mapping table is a table that is used to determine the access rights of users or roles based on some criteria. Snowflake does not support using an external table as a mapping table because it may cause performance issues or errors4.
* While cloning a database, Snowflake clones the row access policy, but not the external table. Therefore, the policy in the cloned database refers to a table that is not present in the cloned database. To avoid this issue, the external table must be manually cloned or recreated in the cloned database4.
* A row access policy can be applied to a view created on top of an external table. The policy can be applied to the view itself or to the underlying external table. However, if the policy is applied to the view, the view must be a secure view, which is a view that hides the underlying data and the view definition from unauthorized users5.
References:
* CREATE EXTERNAL TABLE | Snowflake Documentation
* ALTER EXTERNAL TABLE | Snowflake Documentation
* Understanding Row Access Policies | Snowflake Documentation
* Snowflake Data Governance: Row Access Policy Overview
* Secure Views | Snowflake Documentation

NEW QUESTION # 29
For authentication, snowflake Kafka connector relies on:

• A. key pair authentication
• B. Both key pair authentication and username/password
• C. username/password

Answer: A

NEW QUESTION # 30
You have set time-travel retention to 10 days. You now increase the retention period by 10 more days to make it 20 days.
What will be the impact on the table data?

• A. Any data which has not reached the 10 days time-travel period, will now have time-travel extended for 20 days
• B. Any data that is 10 days older and moved to fail-safe will not have any impact
• C. Changes will impact only new data

Answer: A,B

NEW QUESTION # 31
Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).

• A. Data lake
• B. Graph model
• C. Bayesian hierarchical model
• D. lnmon/3NF
• E. Dimensional/Kimball
• F. Data vault

Answer: D,E,F

Explanation:
Snowflake is a cloud data platform that supports various data models for modeling tables in a Snowflake environment. The data models can be classified into two categories: dimensional and normalized. Dimensional data models are designed to optimize query performance and ease of use for business intelligence and analytics. Normalized data models are designed to reduce data redundancy and ensure data integrity for transactional and operational systems. The following are some of the data models that can be used in Snowflake:
Dimensional/Kimball: This is a popular dimensional data model that uses a star or snowflake schema to organize data into fact and dimension tables. Fact tables store quantitative measures and foreign keys to dimension tables. Dimension tables store descriptive attributes and hierarchies. A star schema has a single denormalized dimension table for each dimension, while a snowflake schema has multiple normalized dimension tables for each dimension. Snowflake supports both star and snowflake schemas, and allows users to create views and joins to simplify queries.
Inmon/3NF: This is a common normalized data model that uses a third normal form (3NF) schema to organize data into entities and relationships. 3NF schema eliminates data duplication and ensures data consistency by applying three rules: 1) every column in a table must depend on the primary key, 2) every column in a table must depend on the whole primary key, not a part of it, and 3) every column in a table must depend only on the primary key, not on other columns. Snowflake supports 3NF schema and allows users to create referential integrity constraints and foreign key relationships to enforce data quality.
Data vault: This is a hybrid data model that combines the best practices of dimensional and normalized data models to create a scalable, flexible, and resilient data warehouse. Data vault schema consists of three types of tables: hubs, links, and satellites. Hubs store business keys and metadata for each entity. Links store associations and relationships between entities. Satellites store descriptive attributes and historical changes for each entity or relationship. Snowflake supports data vault schema and allows users to leverage its features such as time travel, zero-copy cloning, and secure data sharing to implement data vault methodology.

NEW QUESTION # 32
Who can provide permission to EXECUTE TASK?

• A. THE TASK OWNER
• B. ACCOUNTADMIN
• C. SYSADMIN

Answer: B

NEW QUESTION # 33
A company is using Snowflake in Azure in the Netherlands. The company analyst team also has data in JSON format that is stored in an Amazon S3 bucket in the AWS Singapore region that the team wants to analyze.
The Architect has been given the following requirements:
1. Provide access to frequently changing data
2. Keep egress costs to a minimum
3. Maintain low latency
How can these requirements be met with the LEAST amount of operational overhead?

• A. Use an external table against the S3 bucket in AWS Singapore and copy the data into transient tables.
• B. Copy the data between providers from S3 to Azure Blob storage to collocate, then use Snowpipe for data ingestion.
• C. Use a materialized view on top of an external table against the S3 bucket in AWS Singapore.
• D. Use AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then use an external table against the Blob storage.

Answer: A

NEW QUESTION # 34
Databases created from shares cannot be replicated

• A. FALSE
• B. TRUE

Answer: B

NEW QUESTION # 35
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