The latest Microsoft AI-900 Azure AI Fundamentals certification actual real practice exam question and answer (Q&A) dumps are available free, which are helpful for you to pass the Microsoft AI-900 Azure AI Fundamentals exam and earn Microsoft AI-900 Azure AI Fundamentals certification.
Table of Contents
Question 491
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Statement 1: Automated machine learning is the process of automating the time consuming, iterative tasks of machine learning model development.
Statement 2: Automated machine learning can automatically infer the training data from the use case provided.
Statement 3: Automated machine learning works by running multiple training iterations that are scored and ranked by the metrics you specify.
Statement 4: Automated machine learning enables you to specify a dataset and will automatically understand which label to predict.
Answer
Statement 1: Yes
Statement 2: No
Statement 3: Yes
Statement 4: No
Explanation
Statement 1: Yes. Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Statement 2: No
Statement 3: Yes. During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to “fit” your data. It will stop once it hits the exit criteria defined in the experiment.
Statement 4: No. Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify. The label is the column you want to predict.
Question 492
A banking system that predicts whether a loan will be repaid is an example of the _____ type of machine learning.
A. Classification
B. Regression
C. Clustering
Answer
B. Regression
Explanation
In the most basic sense, regression refers to prediction of a numeric target.
Example: Regression Model: A Boosted Decision Tree algorithm was used to create and train the model for predicting the repayment rate.
Question 493
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Statement 1: Labelling is the process of tagging training data with known values.
Statement 2: You should evaluate a model by using the same data used to train the model.
Statement 3: Accuracy is always the primary metric used to measure a model’s performance.
Answer
Statement 1: Yes
Statement 2: No
Statement 3: No
Explanation
Statement 1: Yes. In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict. In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Statement 2: No.
Statement 3: No. Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn’t really capture the effectiveness of a classifier.
Question 494
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Tasks:
- Grouping
- Identification
- Similarity
- Verification
Answer Area:
- Do two images of a face belong to the same person?
- Does this person look like other people?
- Do all the faces belong together?
- Who is this person in this group of people?
Answer
- Verification: Do two images of a face belong to the same person?
- Similarity: Does this person look like other people?
- Grouping: Do all the faces belong together?
- Identification: Who is this person in this group of people?
Explanation
Task 1: Verification: Face verification: Check the likelihood that two faces belong to the same person and receive a confidence score.
Task 2: Similarity
Task 3: Grouping
Task 4: Identification: Face detection: Detect one or more human faces along with attributes such as: age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image.
Question 495
Match the types of computer vision to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Workloads Types:
- Facial recognition
- Image classification
- Object detection
- Optical character recognition (OCR)
Answer Ares:
- Identify celebrities in images
- Extract move title names from movie poster images
- Locate vehicles in images.
Answer
- Facial recognition: Identify celebrities in images
- Optical character recognition (OCR): Extract move title names from movie poster images
- Object detection: Locate vehicles in images.
Explanation
- Facial recognition: Face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images.
- OCR.
- Objection detection: Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image. The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like “indoor”, which can’t be localized with bounding boxes.
Question 496
You can use the _____ service to train an object detection model by using your own images.
A. Computer Vision
B. Custom Vision
C. Form Recognizer
D. Video Indexer
Answer
B. Custom Vision
Explanation
Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.
Incorrect Answers:
Computer Vision: Azure’s Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Question 497
Natural language processing can be used to ______ .
A. Classify email messages as work-related or personal.
B. Predict the number of future car rentals.
C. Predict which website visitors will make a transaction.
D. Stop a process in a factory when extremely high temperatures are registered.
Answer
A. Classify email messages as work-related or personal.
Explanation
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Question 498
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Statement 1: The Text Analytics service can identify in which language text is written.
Statement 2: The Text Analytics service can detect handwritten signatures in a document.
Statement 3: The Text Analytics service can identify companies and organizations mentioned in a document.
Answer
Statement 1: Yes
Statement 2: No
Statement 3: Yes
Explanation
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 1: Yes. You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.
Box 2: No.
Box 3: Yes. Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Question 499
Match the types of natural languages processing workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
Workloads Types:
- Entity recognition
- Key phrase extraction
- Language modeling
- Sentiment analytics
- Natural language processing
- Translation
- Speech recognition and speech synthesis
Answer Area:
- Extracts persons, locations, and organizations from the text.
- Evaluates text along a positive-negative scale.
- Returns text translated to the specified target language.
Answer
Key phrase extraction: Extracts persons, locations, and organizations from the text.
Sentiment analysis: Evaluates text along a positive-negative scale.
Translation: Returns text translated to the specified target language.
Explanation
Box 1: Key phrase extraction: Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 2: Sentiment analysis: Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 3: Translation:
Using Microsoft’s Translator text API
This versatile API from Microsoft can be used for the following:
Translate text from one language to another.
Transliterate text from one script to another.
Detecting language of the input text.
Find alternate translations to specific text.
Determine the sentence length.
Question 500
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Statement 1: Monitoring online service reviews for profanities is an example of natural language processing.
Statement 2: Identifying brand logos in an image is an example of natural language processing.
Statement 3: Monitoring public news sites for negative mentions of a product is an example of natural language processing.
Answer
Statement 1: Yes
Statement 2: No
Statement 3: Yes
Explanation
Statement 1: Yes. Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.
The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.
Statement 2: No. Azure’s Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Statement 3: Yes: Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.