UiPath-SAIAv1 Practice Questions

A.  

To forecast the number of trainers that are required to achieve an excellent performing model.

B.  

To ensure the objectives are focused on delivering targeted value and the model's taxonomy is aligned to value realization.

C.  

To calculate the potential reduction in data storage costs due to model training.

D.  

To estimate the amount of time required to build an excellent performing model.

A.  

You need to process documents larger than five pages.

B.  

You need real-time interaction and you are only processing documents with maximum five pages.

C.  

You need to handle multiple operations simultaneously, allowing for concurrent processing and avoiding idle time.

D.  

You have a large dataset that needs long-term processing.

A.  

Existence of a non-empty root folder, requirements.txt file, and train.py file in the root folder which implements a class Main. The class is further validated to implement an__init_function.

B.  

Existence of a non-empty root folder, requlrements.txt file, and main.py file in the root folder which implements a class Main. The class is further validated to implement an__init and a predict function.

C.  

Existence of a non-empty root folder, main.py file in the root folder which implements a dass Main. The class is further validated to implement an__init__and a predict function.

D.  

Existence of a requirements.txt file, and main.py file which implements a class Main. The class is further validated lo implement an__init__and a predict function.

A.  

Languages using the Greek left-to-right alphabet. Japanese, and Chinese.

B.  

Languages using the Cyrillic alphabet, the Greek left-to-right alphabet, and Chinese.

C.  

Languages using the Latin alphabet (like Italian, French. Portuguese. Spanish, and Romanian), and the Greek left-to-right alphabet.

D.  

Languages using the Latin alphabet, the Cyrillic alphabet, the Greek left-to-right alphabet. Japanese, and Chinese.

A.  

Pipeline type: FullPackage Major Version: 23.10.4.0 Package Minor Version: 1 Input Dataset: 800 pages Evaluation Dataset: 100 pages

B.  

Pipeline type: EvaluatePackage Major Version: 23.10.4.0 Package Minor Version: 0 Input Dataset: 1000+800 pages Evaluation Dataset: 100 pages

C.  

Pipeline type: Full Package Major Version: 23.10.4.0 Package Minor Version: 0 Input Dataset: 1000+800 pages Evaluation Dataset: 100 pages

D.  

Pipeline type: Training Package Major Version: 23.10.4.0 Package Minor Version: 1 Input Dataset: 1000+800 Evaluation Dataset: N/A

A.  

The popularity of the ML Extractor among other UiPath users should be the primary factor. Opt for the ML Extractor that has the highest number of downloads or positive reviews.

B.  

Consider the document types, language, and data quality. It is important to select one that is specifically trained or optimized for the document types being processed. It is also important to takeinto account the quality and diversity of the training data used to train the ML Extractor to ensure accurate and reliable extraction results.

C.  

The size of the ML Extractor is the most important factor to consider. Bigger models always perform better and provide more accurate extraction results because the development team invested time and effort into creating the algorithm, which in turn will result in better performance for the trained model.

D.  

The cost of the ML Extractor should be the main consideration. Select the ML Extractor that offers the lowest price, regardless of its performance or suitability for the specific document understanding needs.

A.  

RPA Developer.

B.  

Process Controller.

C.  

Administrator.

D.  

Data Scientist.

A.  

ML skills can be created, stopped, redeployed, updated to a new package version, rolled back to a previous package version, modified to use or not use GPU. modified to use or not use Al units, made public or private, or deleted.

B.  

ML skills can be created, stopped, redeployed, updated to a new package version, rolled back to a previous package version, made public or private, or deleted.

C.  

ML skills can be created, stopped, redeployed, updated to a new package version, rolled back to a previous package version, modified to use or not use GPU. made public or private, or deleted.

D.  

ML skills can be created, updated to a new package version, rolled back to a previous package version, modified to use or not use GPU. made public or private, or deleted.