At ValidExamDumps, we consistently monitor updates to the Snowflake DEA-C01 exam questions by Snowflake. Whenever our team identifies changes in the exam questions,exam objectives, exam focus areas or in exam requirements, We immediately update our exam questions for both PDF and online practice exams. This commitment ensures our customers always have access to the most current and accurate questions. By preparing with these actual questions, our customers can successfully pass the Snowflake SnowPro Advanced: Data Engineer Certification Exam exam on their first attempt without needing additional materials or study guides.
Other certification materials providers often include outdated or removed questions by Snowflake in their Snowflake DEA-C01 exam. These outdated questions lead to customers failing their Snowflake SnowPro Advanced: Data Engineer Certification Exam exam. In contrast, we ensure our questions bank includes only precise and up-to-date questions, guaranteeing their presence in your actual exam. Our main priority is your success in the Snowflake DEA-C01 exam, not profiting from selling obsolete exam questions in PDF or Online Practice Test.
A Data Engineer is trying to load the following rows from a CSV file into a table in Snowflake with the following structure:
....engineer is using the following COPY INTO statement:
However, the following error is received.
Which file format option should be used to resolve the error and successfully load all the data into the table?
The file format option that should be used to resolve the error and successfully load all the data into the table is FIELD_OPTIONALLY_ENCLOSED_BY = '''. This option specifies that fields in the file may be enclosed by double quotes, which allows for fields that contain commas or newlines within them. For example, in row 3 of the file, there is a field that contains a comma within double quotes: ''Smith Jr., John''. Without specifying this option, Snowflake will treat this field as two separate fields and cause an error due to column count mismatch. By specifying this option, Snowflake will treat this field as one field and load it correctly into the table.
What is a characteristic of the use of binding variables in JavaScript stored procedures in Snowflake?
A characteristic of the use of binding variables in JavaScript stored procedures in Snowflake is that only JavaScript variables of type number, string and sf Date can be bound. Binding variables are a way to pass values from JavaScript variables to SQL statements within a stored procedure. Binding variables can improve the security and performance of the stored procedure by preventing SQL injection attacks and reducing the parsing overhead. However, not all types of JavaScript variables can be bound. Only the primitive types number and string, and the Snowflake-specific type sf Date, can be bound. The other options are incorrect because they do not describe a characteristic of the use of binding variables in JavaScript stored procedures in Snowflake. Option A is incorrect because authenticator is not a type of JavaScript variable, but a parameter of the snowflake.connector.connect function. Option B is incorrect because arrow_number_to_decimal is not a type of JavaScript variable, but a parameter of the snowflake.connector.connect function. Option D is incorrect because users are not restricted from binding JavaScript variables, but encouraged to do so.
A Data Engineer is building a set of reporting tables to analyze consumer requests by region for each of the Data Exchange offerings annually, as well as click-through rates for each listing
Which views are needed MINIMALLY as data sources?
The SNOWFLAKE.DATASHARING _USAGE.LISTING_CONSOKE>TION_DAILY view provides information about consumer requests by region for each of the Data Exchange offerings annually, as well as click-through rates for each listing. This view is the minimal data source needed for building the reporting tables. The other views are not relevant for this use case.
The following chart represents the performance of a virtual warehouse over time:
A Data Engineer notices that the warehouse is queueing queries The warehouse is size X-Small the minimum and maximum cluster counts are set to 1 the scaling policy is set to i and auto-suspend is set to 10 minutes.
How can the performance be improved?
The performance can be improved by increasing the size of the warehouse. The chart shows that the warehouse is queueing queries, which means that there are more queries than the warehouse can handle at its current size. Increasing the size of the warehouse will increase its processing power and concurrency limit, which could reduce the queueing time and improve the performance. The other options are not likely to improve the performance significantly. Option A, changing the cluster settings, will not help unless the minimum and maximum cluster counts are increased to allow for multi-cluster scaling. Option C, changing the scaling policy to economy, will not help because it will reduce the responsiveness of the warehouse to scale up or down based on demand. Option D, changing auto-suspend to a longer time frame, will not help because it will only affect how long the warehouse stays idle before suspending itself.
The following code is executed in a Snowflake environment with the default settings:
What will be the result of the select statement?
