Best AIP-210 Exam Dumps for the Preparation of Latest AIP-210 Exam Questions
Download Latest & Valid Questions For CertNexus AIP-210 exam
CertNexus AIP-210 Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
NEW QUESTION # 35
Which three security measures could be applied in different ML workflow stages to defend them against malicious activities? (Select three.)
- A. Disable logging for model access.
- B. Use Secrets Manager to protect credentials.
- C. Use max privilege to control access to ML artifacts.
- D. Monitor model degradation.
- E. Use data encryption.
- F. Launch ML Instances In a virtual private cloud (VPC).
Answer: B,E,F
Explanation:
Explanation
Security measures can be applied in different ML workflow stages to defend them against malicious activities, such as data theft, model tampering, or adversarial attacks. Some of the security measures are:
Launch ML Instances In a virtual private cloud (VPC): A VPC is a logically isolated section of a cloud provider's network that allows users to launch and control their own resources. By launching ML instances in a VPC, users can enhance the security and privacy of their data and models, as well as restrict the access and traffic to and from the instances.
Use data encryption: Data encryption is the process of transforming data into an unreadable format using a secret key or algorithm. Data encryption can protect the confidentiality, integrity, and availability of data at rest (stored in databases or files) or in transit (transferred over networks). Data encryption can prevent unauthorized access, modification, or leakage of sensitive data.
Use Secrets Manager to protect credentials: Secrets Manager is a service that helps users securely store, manage, and retrieve secrets, such as passwords, API keys, tokens, or certificates. Secrets Manager can help users protect their credentials from unauthorized access or exposure, as well as rotate them automatically to comply with security policies.
NEW QUESTION # 36
Which of the following is TRUE about SVM models?
- A. They can be used only for classification.
- B. They can take the feature space into higher dimensions to solve the problem.
- C. They can be used only for regression.
- D. They use the sigmoid function to classify the data points.
Answer: B
Explanation:
Explanation
SVM models can use kernel functions to map the input data into higher-dimensional feature spaces, where linear separation is possible. This allows SVM models to handle non-linear problems effectively.
References: CertNexus Certified Artificial Intelligence Practitioner, Support vector machine - Wikipedia
NEW QUESTION # 37
Which of the following principles supports building an ML system with a Privacy by Design methodology?
- A. Understanding, documenting, and displaying data lineage.
- B. Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
- C. Avoiding mechanisms to explain and justify automated decisions.
- D. Collecting and processing the largest amount of data possible.
Answer: A
Explanation:
Explanation
Data lineage is the process of tracking the origin, transformation, and usage of data throughout its lifecycle. It helps to ensure data quality, integrity, and provenance. Data lineage also supports the Privacy by Design methodology, which is a framework that aims to embed privacy principles into the design and operation of systems, processes, and products that involve personal data. By understanding, documenting, and displaying data lineage, an ML system can demonstrate how it collects, processes, stores, and deletes personal data in a transparent and accountable manner3 .
NEW QUESTION # 38
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 recall
- B. Recall and explained variance
- C. Mean squared error
- D. Precision and accuracy
Answer: A
Explanation:
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 # 39
Which of the following tools would you use to create a natural language processing application?
- A. NLTK
- B. DeepDream
- C. Azure Search
- D. AWS DeepRacer
Answer: A
Explanation:
Explanation
NLTK (Natural Language Toolkit) is a Python library that provides a set of tools and resources for natural language processing (NLP). NLP is a branch of AI that deals with analyzing, understanding, and generating natural language texts or speech. NLTK offers modules for various NLP tasks, such as tokenization, stemming, lemmatization, parsing, tagging, chunking, sentiment analysis, named entity recognition, machine translation, text summarization, and more .
NEW QUESTION # 40
Which of the following occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others?
- A. Sampling bias
- B. Systematic value distortion
- C. Algorithmic bias
- D. Stereotype bias
Answer: A
Explanation:
Explanation
Sampling bias occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others. This can result in a sample that is not representative of the population and may lead to inaccurate or misleading conclusions. Sampling bias can be caused by various factors, such as non-random sampling methods, non-response, self-selection, or convenience sampling. References: [Sampling bias - Wikipedia], [What is Sampling Bias? Definition, Types and Examples]
NEW QUESTION # 41
Which of the following unsupervised learning models can a bank use for fraud detection?
- A. k-means
- B. Hierarchical clustering
- C. DB5CAN
- D. Anomaly detection
Answer: D
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 # 42
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. Detain
- C. Duplicate
- D. De-Duplicate
Answer: A
Explanation:
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 # 43
Why do data skews happen in the ML pipeline?
- A. There is insufficient training data for evaluation.
- B. There Is a mismatch between live input data and offline data.
- C. Test and evaluation data are designed incorrectly.
- D. There is a mismatch between live output data and offline data.
Answer: B
Explanation:
Explanation
Data skews happen in the ML pipeline when the distribution or characteristics of the live input data differ from those of the offline data used for training and testing the model. This can lead to a degradation of the model performance and accuracy, as the model is not able to generalize well to new data. Data skews can be caused by various factors, such as changes in user behavior, data collection methods, data quality issues, or external events. References: What is training-serving skew in Machine Learning?, Data preprocessing for ML: options and recommendations
NEW QUESTION # 44
Workflow design patterns for the machine learning pipelines:
- A. Represent a pipeline with directed acyclic graph (DAG).
- B. Seek to simplify the management of machine learning features.
- C. Aim to explain how the machine learning model works.
- D. Separate inputs from features.
Answer: A
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 # 45
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. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
- C. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
- D. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
Answer: D
Explanation:
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 # 46
Which of the following pieces of AI technology provides the ability to create fake videos?
- A. Generative adversarial networks (GAN)
- B. Support-vector machines (SVM)
- C. Long short-term memory (LSTM) networks
- D. Recurrent neural networks (RNN)
Answer: A
Explanation:
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 # 47
R-squared is a statistical measure that:
- A. Is the proportion of the variance for a dependent variable thaf' s explained by independent variables.
- B. Combines precision and recall of a classifier into a single metric by taking their harmonic mean.
- C. Expresses the extent to which two variables are linearly related.
- D. Represents the extent to which two random variables vary together.
Answer: A
Explanation:
Explanation
R-squared is a statistical measure that indicates how well a regression model fits the data. R-squared is calculated by dividing the explained variance by the total variance. The explained variance is the amount of variation in the dependent variable that can be attributed to the independent variables. The total variance is the amount of variation in the dependent variable that can be observed in the data. R-squared ranges from 0 to 1, where 0 means no fit and 1 means perfect fit.
NEW QUESTION # 48
When should you use semi-supervised learning? (Select two.)
- A. A small set of labeled data is available but not representative of the entire distribution.
- B. Labeling data is challenging and expensive.
- C. There is a large amount of unlabeled data to be used for predictions.
- D. There is a large amount of labeled data to be used for predictions.
- E. A small set of labeled data is biased toward one class.
Answer: B,C
Explanation:
Explanation
Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning can be useful when:
Labeling data is challenging and expensive: Labeling data requires human intervention and domain expertise, which can be costly and time-consuming. Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model's performance.
There is a large amount of unlabeled data to be used for predictions: Unlabeled data can provide additional information and diversity to the model, which can help it learn more complex patterns and generalize better to new data. Semi-supervised learning can use various techniques, such as self-training, co-training, or generative models, to incorporate unlabeled data into the learning process.
NEW QUESTION # 49
Which two techniques are used to build personas in the ML development lifecycle? (Select two.)
- A. Population triage
- B. Population estimates
- C. Population resampling
- D. Population variance
- E. Population regression
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 # 50
Which of the following metrics is being captured when performing principal component analysis?
- A. Kurtosis
- B. Missingness
- C. Skewness
- D. Variance
Answer: D
Explanation:
Explanation
Principal component analysis (PCA) is a technique that reduces the dimensionality of a dataset by transforming it into a set of new variables called principal components. The principal components are linear combinations of the original variables that capture the maximum amount of variance in the data. The first principal component explains the most variance, the second principal component explains the second most variance, and so on. The goal of PCA is to retain as much variance as possible while reducing the number of variables.
NEW QUESTION # 51
Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?
- A. Reduction in latency
- B. Guaranteed availability of enough space
- C. Increase in data bandwidth consumption
- D. A more complex model
Answer: A
Explanation:
Explanation
Latency is the time delay between a request and a response. Latency can affect the performance and user experience of an application, especially when real-time or near-real-time responses are required. Deploying a deep learning model as an embedded model on edge devices can reduce latency, as the model can run locally on the device without relying on network connectivity or cloud servers. Edge devices are devices that are located at the edge of a network, such as smartphones, tablets, laptops, sensors, cameras, or drones.
NEW QUESTION # 52
A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?
- A. The application was migrated from on-premise to a public cloud.
- B. The training data used was inaccurate.
- C. The AI model was trained in winter and applied in summer.
- D. The team set flawed expectations when training the model.
Answer: C
Explanation:
Explanation
Emergent bias is a type of bias that arises when an AI model encounters new or different data or scenarios that were not present or accounted for during its training or development. Emergent bias can cause the model to make inaccurate or unfair predictions or decisions, as it may not be able to generalize well to new situations or adapt to changing conditions. One possible cause of emergent bias is seasonality, which means that some variables or patterns in the data may vary depending on the time of year. For example, if an AI model for merchandise sales prediction was trained in winter and applied in summer, it may produce biased results due to differences in customer behavior, demand, or preferences.
NEW QUESTION # 53
......
Exam Materials for You to Prepare & Pass AIP-210 Exam: https://certmagic.surepassexams.com/AIP-210-exam-bootcamp.html