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CertNexus AIP-210 Exam Syllabus Topics:
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NEW QUESTION # 42
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)
- A. Explainability
- B. Data size
- C. Scalability
- D. Complexity
- E. Portability
Answer: A,C
Explanation:
Scalability and explainability are two criteria that are essential for ML models to achieve before deployment.
Scalability is the ability of an ML model to handle increasing amounts of data or requests without compromising its performance or quality. Scalability can help ensure that the model can meet the demand and expectations of users or customers, as well as adapt to changing conditions or environments. Explainability is the ability of an ML model to provide clear and intuitive explanations for its predictions or decisions.
Explainability can help increase trust and confidence among users or stakeholders, as well as enable accountability and responsibility for the model's actions and outcomes.
NEW QUESTION # 43
When should the model be retrained in the ML pipeline?
- A. Concept drift is detected in the pipeline.
- B. Some outliers are detected in live data.
- C. A new monitoring component is added.
- D. More data become available for the training phase.
Answer: A
Explanation:
Explanation
When concept drift is detected in the pipeline, it means that the model performance has degraded over time due to changes in the underlying data generating process. This requires retraining the model with new data that reflects the current situation and updating the model parameters accordingly. References: Use pipeline parameters to retrain models in the designer - Azure Machine Learning | Microsoft Learn, Retraining Model During Deployment: Continuous Training and Continuous Testing
NEW QUESTION # 44
Which of the following is a common negative side effect of not using regularization?
- A. Low test accuracy
- B. Overfitting
- C. Higher compute resources
- D. Slow convergence time
Answer: B
Explanation:
Explanation
Overfitting is a common negative side effect of not using regularization. Regularization is a technique that reduces the complexity of a model by adding a penalty term to the loss function, which prevents the model from learning too many parameters that may fit the noise in the training data. Overfitting occurs when the model performs well on the training data but poorly on the test data or new data, because it has memorized the training data and cannot generalize well. References: Regularization (mathematics) - Wikipedia, Overfitting in Machine Learning: What It Is and How to Prevent It
NEW QUESTION # 45
Which of the following equations best represent an LI norm?
- A. |x|-|y|
- B. |x|+|y|^2
- C. |x|^2+|y|^2
- D. |x| + |y|
Answer: D
Explanation:
An L1 norm is a measure of distance or magnitude that is defined as the sum of the absolute values of the components of a vector. For example, if x and y are two components of a vector, then the L1 norm of that vector is |x| + |y|. The L1 norm is also known as the Manhattan distance or the taxicab distance, as it represents the shortest path between two points in a grid-like city.
NEW QUESTION # 46
An HR solutions firm is developing software for staffing agencies that uses machine learning.
The team uses training data to teach the algorithm and discovers that it generates lower employability scores for women. Also, it predicts that women, especially with children, are less likely to get a high-paying job.
Which type of bias has been discovered?
- A. Automation
- B. Emergent
- C. Technical
- D. Preexisting
Answer: D
Explanation:
Explanation
Preexisting bias is a type of bias that originates from historical or social contexts, such as stereotypes, prejudices, or discriminations. Preexisting bias can affect the data or the algorithm used for machine learning, as well as the outcomes or decisions made by machine learning. Preexisting bias can cause unfair or harmful impacts on certain groups or individuals based on their attributes, such as gender, race, age, or disability3. In this case, the software that uses machine learning generates lower employability scores for women and predicts that women, especially with children, are less likely to get a high-paying job. This indicates that the software has preexisting bias against women, which may reflect the historical or social inequalities or expectations in the labor market.
NEW QUESTION # 47
Which two techniques are used to build personas in the ML development lifecycle? (Select two.)
- A. Population triage
- B. Population estimates
- C. Population variance
- D. Population regression
- E. Population resampling
Answer: A,B
Explanation:
Explanation
Personas are fictional characters that represent the potential users or customers of an ML system. Personas can help understand the needs, goals, preferences, and behaviors of the target audience, as well as design and evaluate the system from their perspective. Some of the techniques that are used to build personas in the ML development lifecycle are:
Population estimates: Population estimates are statistical methods that estimate the size, characteristics, and distribution of a population based on a sample or a census. Population estimates can help identify and quantify the potential market segments and user groups for an ML system, as well as their demographics, locations, and behaviors.
Population triage: Population triage is a process of prioritizing and selecting the most relevant and representative personas for an ML system based on some criteria or metrics. Population triage can help focus on the key user needs and scenarios, as well as avoid creating too many or too few personas.
NEW QUESTION # 48
Which of the following pieces of AI technology provides the ability to create fake videos?
- A. Recurrent neural networks (RNN)
- B. Long short-term memory (LSTM) networks
- C. Generative adversarial networks (GAN)
- D. Support-vector machines (SVM)
Answer: C
Explanation:
Generative adversarial networks (GAN) are a type of AI technology that can create fake videos, images, audio, or text that are realistic and indistinguishable from real ones. GAN consist of two neural networks: a generator and a discriminator. The generator tries to produce fake samples from random noise, while the discriminator tries to distinguish between real and fake samples. The two networks compete against each other in a game-like scenario, where the generator tries to fool the discriminator and the discriminator tries to catch the generator. Through this process, both networks improve their abilities until they reach an equilibrium where the generator can produce convincing fakes.
NEW QUESTION # 49
Which of the following unsupervised learning models can a bank use for fraud detection?
- A. DB5CAN
- B. Anomaly detection
- C. Hierarchical clustering
- D. k-means
Answer: B
Explanation:
Explanation
Anomaly detection is an unsupervised learning technique that identifies outliers or abnormal patterns in data, which can be useful for fraud detection. Anomaly detection algorithms can learn the normal behavior of transactions and flag the ones that deviate significantly from the norm, indicating possible fraud.
NEW QUESTION # 50
Which of the following describes a typical use case of video tracking?
- A. Augmented dreaming
- B. Video composition
- C. Medical diagnosis
- D. Traffic monitoring
Answer: D
Explanation:
Explanation
Video tracking is a technique that involves detecting and following moving objects in a video sequence. Video tracking can be used for various applications, such as surveillance, security, sports analysis, and human-computer interaction. One typical use case of video tracking is traffic monitoring, where video tracking can help measure traffic flow, detect congestion, identify violations, and optimize traffic signals.
NEW QUESTION # 51
Personal data should not be disclosed, made available, or otherwise used for purposes other than specified with which of the following exceptions? (Select two.)
- A. If it was collected accidentally.
- B. If it was with consent of the person it is collected from.
- C. If the data is only collected once.
- D. If it is for a good cause.
- E. If it was requested by the authority of law.
Answer: B,E
Explanation:
Personal data is any information that relates to an identified or identifiable individual, such as name, address, email, phone number, or biometric data. Personal data should not be disclosed, made available, or otherwise used for purposes other than specified, except with:
* The consent of the person it is collected from: Consent is a clear and voluntary indication of agreement by the person to the processing of their personal data for a specific purpose. Consent can be given by a statement or a clear affirmative action, such as ticking a box or clicking a button.
* The authority of law: The authority of law is a legal basis or obligation that requires or permits the processing of personal data for a legitimate purpose. For example, the authority of law could be a court order, a subpoena, a warrant, or a statute.
NEW QUESTION # 52
Which of the following models are text vectorization methods? (Select two.)
- A. PCA
- B. Tokenization
- C. TF-IDF
- D. t-SNE
- E. Lemmatization
- F. Skip-gram
Answer: C,F
Explanation:
Explanation
Skip-gram and TF-IDF are both text vectorization methods that convert text into numerical feature vectors.
Skip-gram is a prediction-based word embedding method that learns vector representations of words from their contexts in a large corpus of text. TF-IDF is a frequency-based word weighting method that assigns scores to words based on their importance in a document and in a corpus of documents. References: Text Vectorization and Word Embedding | Guide to Master NLP (Part 5), What Is Text Vectorization? Everything You Need to Know - deepset
NEW QUESTION # 53
Workflow design patterns for the machine learning pipelines:
- A. Seek to simplify the management of machine learning features.
- B. Aim to explain how the machine learning model works.
- C. Represent a pipeline with directed acyclic graph (DAG).
- D. Separate inputs from features.
Answer: C
Explanation:
Explanation
Workflow design patterns for machine learning pipelines are common solutions to recurring problems in building and managing machine learning workflows. One of these patterns is to represent a pipeline with a directed acyclic graph (DAG), which is a graph that consists of nodes and edges, where each node represents a step or task in the pipeline, and each edge represents a dependency or order between the tasks. A DAG has no cycles, meaning there is no way to start at one node and return to it by following the edges. A DAG can help visualize and organize the pipeline, as well as facilitate parallel execution, fault tolerance, and reproducibility.
NEW QUESTION # 54
Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?
- A. Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
- B. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
- C. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
- D. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
Answer: C
Explanation:
Cloud models are service models that provide different levels of abstraction and control over computing resources in a cloud environment. Some of the common cloud models for machine learning pipelines are:
* Software as a Service (SaaS): SaaS provides ready-to-use applications that run on the cloud provider's infrastructure and are accessible through a web browser or an API. SaaS can provide AI practitioner data science services such as Jupyter notebooks, which are web-based interactive environments that allow users to create and share documents that contain code, text, visualizations, and more.
* Platform as a Service (PaaS): PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. PaaS can provide some services within an application such as payment applications to create efficient results.
* Infrastructure as a Service (IaaS): IaaS provides access to fundamental computing resources such as servers, storage, networks, and operating systems. IaaS can provide CPU, memory, disk, network and GPU resources that can be used to run machine learning models and applications.
* Data as a Service (DaaS): DaaS provides access to data sources that can be consumed by applications or users on demand. DaaS can host the databases providing backups, clustering, and high availability.
NEW QUESTION # 55
Which of the following statements are true regarding highly interpretable models? (Select two.)
- A. They are usually referred to as "black box" models.
- B. They are usually binary classifiers.
- C. They are usually easier to explain to business stakeholders.
- D. They are usually very good at solving non-linear problems.
- E. They usually compromise on model accuracy for the sake of interpretability.
Answer: C,E
Explanation:
Explanation
Highly interpretable models are models that can provide clear and intuitive explanations for their predictions, such as decision trees, linear regression, or logistic regression. Some of the statements that are true regarding highly interpretable models are:
They are usually easier to explain to business stakeholders: Highly interpretable models can help communicate the logic and reasoning behind their predictions, which can increase trust and confidence among business stakeholders. For example, a decision tree can show how each feature contributes to a decision outcome, or a linear regression can show how each coefficient affects the dependent variable.
They usually compromise on model accuracy for the sake of interpretability: Highly interpretable models may not be able to capture complex or non-linear patterns in the data, which can reduce their accuracy and generalization. For example, a decision tree may overfit or underfit the data if it is too deep or too shallow, or a linear regression may not be able to model curved relationships between variables.
NEW QUESTION # 56
A classifier has been implemented to predict whether or not someone has a specific type of disease.
Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?
- A. Precision and accuracy
- B. Recall and explained variance
- C. Precision and recall
- D. Mean squared error
Answer: C
Explanation:
Precision and recall are two measures that can evaluate the performance of a classifier, especially when the data is imbalanced. Precision is the ratio of true positives (correctly predicted positive cases) to all predicted positive cases. Recall is the ratio of true positives to all actual positive cases. Precision and recall can help assess how well the classifier can identify the positive cases (the disease) and avoid false negatives (missed diagnosis) or false positives (unnecessary treatment).
NEW QUESTION # 57
A big data architect needs to be cautious about personally identifiable information (PII) that may be captured with their new IoT system. What is the final stage of the Data Management Life Cycle, which the architect must complete in order to implement data privacy and security appropriately?
- A. Destroy
- B. De-Duplicate
- C. Detain
- D. Duplicate
Answer: A
Explanation:
The final stage of the data management life cycle is data destruction, which is the process of securely deleting or erasing data that is no longer needed or relevant for the organization. Data destruction ensures that data is disposed of in compliance with any legal or regulatory requirements, as well as any internal policies or standards. Data destruction also protects the organization from potential data breaches, leaks, or thefts that could compromise its privacy and security. Data destruction can be performed using various methods, such as overwriting, degaussing, shredding, or incinerating
NEW QUESTION # 58
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