Best Preparations of AIP-210 Exam 2023 Certified AI Practitioner Unlimited 92 Questions
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NEW QUESTION # 50
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 training data used was inaccurate.
- B. The AI model was trained in winter and applied in summer.
- C. The application was migrated from on-premise to a public cloud.
- D. The team set flawed expectations when training the model.
Answer: B
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 # 51
A market research team has ratings from patients who have a chronic disease, on several functional, physical, emotional, and professional needs that stay unmet with the current therapy. The dataset also captures ratings on how the disease affects their day-to-day activities.
A pharmaceutical company is introducing a new therapy to cure the disease and would like to design their marketing campaign such that different groups of patients are targeted with different ads. These groups should ideally consist of patients with similar unmet needs.
Which of the following algorithms should the market research team use to obtain these groups of patients?
- A. k-nearest neighbors
- B. Naive-Bayes
- C. k-means clustering
- D. Logistic regression
Answer: C
Explanation:
Explanation
k-means clustering is an algorithm that should be used by the market research team to obtain groups of patients with similar unmet needs. k-means clustering is an unsupervised learning technique that partitions the data into k clusters based on the similarity of the features. The algorithm iteratively assigns each data point to the cluster with the nearest centroid and updates the centroid until convergence. k-means clustering can help identify patterns and segments in the data that may not be obvious or intuitive. References: [K-means clustering - Wikipedia], [How to Run K-Means Clustering in Python]
NEW QUESTION # 52
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 # 53
Which database is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems?
- A. Configuration Management
- B. Code Repository
- C. Incident
- D. Asset
Answer: C
Explanation:
Explanation
An incident database is a database that is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems. An incident database collects and stores information about incidents or events where AI systems have caused or contributed to negative outcomes or harms, such as accidents, errors, biases, discriminations, or violations. An incident database can help identify patterns, trends, causes, impacts, and solutions for AI-related incidents, as well as provide guidance and best practices for preventing or mitigating future incidents.
NEW QUESTION # 54
When should the model be retrained in the ML pipeline?
- A. Some outliers are detected in live data.
- B. A new monitoring component is added.
- C. Concept drift is detected in the pipeline.
- D. More data become available for the training phase.
Answer: C
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 # 55
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. Launch ML Instances In a virtual private cloud (VPC).
- C. Use Secrets Manager to protect credentials.
- D. Use max privilege to control access to ML artifacts.
- E. Monitor model degradation.
- F. Use data encryption.
Answer: B,C,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 # 56
An AI system recommends New Year's resolutions. It has an ML pipeline without monitoring components.
What retraining strategy would be BEST for this pipeline?
- A. Periodically before New Year's Day and after New Year's Day
- B. When data drift is detected
- C. Periodically every year
- D. When concept drift is detected
Answer: C
Explanation:
Explanation
Retraining is the process of updating an existing ML model with new or updated data to maintain or improve its performance and relevance. Retraining can help address various issues or challenges in ML systems, such as data drift, concept drift, model degradation, or changing requirements. Retraining can be done using different strategies, such as periodically, continuously, or on-demand.
For an AI system that recommends New Year's resolutions, retraining periodically every year would be the best strategy for this pipeline. This is because New Year's resolutions are seasonal and time-sensitive, meaning that they may vary depending on the year or the current situation. Retraining periodically every year can help ensure that the system's recommendations are up-to-date and relevant for each new year.
NEW QUESTION # 57
Which of the following tools would you use to create a natural language processing application?
- A. Azure Search
- B. AWS DeepRacer
- C. DeepDream
- D. NLTK
Answer: D
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 # 58
Which of the following models are text vectorization methods? (Select two.)
- A. PCA
- B. Lemmatization
- C. Skip-gram
- D. TF-IDF
- E. Tokenization
- F. t-SNE
Answer: C,D
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 # 59
Which of the following is NOT a valid cross-validation method?
- A. Leave-one-out
- B. K-fold
- C. Stratification
- D. Bootstrapping
Answer: C
Explanation:
Explanation
Stratification is not a valid cross-validation method, but a technique to ensure that each subset of data has the same proportion of classes or labels as the original data. Stratification can be used in conjunction with cross-validation methods such as k-fold or leave-one-out to preserve the class distribution and reduce bias or variance in the validation results. Bootstrapping, k-fold, and leave-one-out are all valid cross-validation methods that use different ways of splitting and resampling the data to estimate the performance of a machine learning model.
NEW QUESTION # 60
You are building a prediction model to develop a tool that can diagnose a particular disease so that individuals with the disease can receive treatment. The treatment is cheap and has no side effects. Patients with the disease who don't receive treatment have a high risk of mortality.
It is of primary importance that your diagnostic tool has which of the following?
- A. High negative predictive value
- B. High positive predictive value
- C. Low false positive rate
- D. Low false negative rate
Answer: D
Explanation:
Explanation
A false negative is an error where a positive case (belonging to the target class) is incorrectly predicted as negative (not belonging to the target class). A false negative rate is the ratio of false negatives to all actual positive cases. A low false negative rate means that most of the positive cases are correctly identified by the classifier.
For a diagnostic tool that can diagnose a particular disease so that individuals with the disease can receive treatment, it is of primary importance that it has a low false negative rate. This is because false negatives can have serious consequences for patients who have the disease but do not receive treatment, such as increased risk of mortality or complications. A low false negative rate can ensure that most patients who have the disease are diagnosed correctly and receive timely treatment.
NEW QUESTION # 61
Which of the following can take a question in natural language and return a precise answer to the question?
- A. Pandas
- B. IBM Watson
- C. Databricks
- D. Spark ML
Answer: B
Explanation:
Explanation
IBM Watson is an AI technology that can take a question in natural language and return a precise answer to the question. IBM Watson is a cognitive computing system that can understand natural language, generate hypotheses, and provide evidence-based answers. IBM Watson can be applied to various domains and industries, such as healthcare, education, finance, or law.
NEW QUESTION # 62
Given a feature set with rows that contain missing continuous values, and assuming the data is normally distributed, what is the best way to fill in these missing features?
- A. Delete entire rows that contain any missing features.
- B. Delete entire columns that contain any missing features.
- C. Fill in missing features with random values for that feature in the training set.
- D. Fill in missing features with the average of observed values for that feature in the entire dataset.
Answer: D
Explanation:
Explanation
Missing values are a common problem in data analysis and machine learning, as they can affect the quality and reliability of the data and the model. There are various methods to deal with missing values, such as deleting, imputing, or ignoring them. One of the most common methods is imputing, which means replacing the missing values with some estimated values based on some criteria. For continuous variables, one of the simplest and most widely used imputation methods is to fill in the missing values with the mean (average) of the observed values for that variable in the entire dataset. This method can preserve the overall distribution and variance of the data, as well as avoid introducing bias or noise.
NEW QUESTION # 63
We are using the k-nearest neighbors algorithm to classify the new data points. The features are on different scales.
Which method can help us to solve this problem?
- A. Log transformation
- B. Standardization
- C. Square-root transformation
- D. Normalization
Answer: D
Explanation:
Explanation
Normalization is a method that can help us to solve the problem of features being on different scales when using the k-nearest neighbors algorithm. Normalization is a technique that rescales the values of features to a common range, such as [0, 1] or [-1, 1]. Normalization can help reduce the influence or dominance of some features over others, as well as improve the accuracy and performance of the algorithm2.
NEW QUESTION # 64
R-squared is a statistical measure that:
- A. Represents the extent to which two random variables vary together.
- B. Is the proportion of the variance for a dependent variable thaf' s explained by independent variables.
- C. Combines precision and recall of a classifier into a single metric by taking their harmonic mean.
- D. Expresses the extent to which two variables are linearly related.
Answer: B
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 # 65
You and your team need to process large datasets of images as fast as possible for a machine learning task.
The project will also use a modular framework with extensible code and an active developer community.
Which of the following would BEST meet your needs?
- A. Microsoft Cognitive Services
- B. Caffe
- C. TensorBoard
- D. Keras
Answer: B
Explanation:
Explanation
Caffe is a deep learning framework that is designed for speed and modularity. It can process large datasets of images efficiently and supports various types of neural networks. It also has a large and active developer community that contributes to its code base and documentation. Caffe is suitable for image processing tasks such as classification, segmentation, detection, and recognition
NEW QUESTION # 66
Why do data skews happen in the ML pipeline?
- A. There Is a mismatch between live input data and offline data.
- B. There is a mismatch between live output data and offline data.
- C. There is insufficient training data for evaluation.
- D. Test and evaluation data are designed incorrectly.
Answer: A
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 # 67
Workflow design patterns for the machine learning pipelines:
- A. Seek to simplify the management of machine learning features.
- B. Represent a pipeline with directed acyclic graph (DAG).
- C. Aim to explain how the machine learning model works.
- D. Separate inputs from features.
Answer: B
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 # 68
Which of the following is the definition of accuracy?
- A. (True Positives + False Positives) / Total Predictions
- B. True Positives / (True Positives + False Positives)
- C. True Positives / (True Positives + False Negatives)
- D. (True Positives + True Negatives) / Total Predictions
Answer: D
Explanation:
Explanation
Accuracy is a measure of how well a classifier can correctly predict the class of an instance. Accuracy is calculated by dividing the number of correct predictions (true positives and true negatives) by the total number of predictions. True positives are instances that are correctly predicted as positive (belonging to the target class). True negatives are instances that are correctly predicted as negative (not belonging to the target class).
NEW QUESTION # 69
Which of the following is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data?
- A. Sarbanes Oxley (SOX)
- B. General Data Protection Regulation (GDPR)
- C. ISO/IEC 27001
- D. PCIDSS
Answer: B
Explanation:
Explanation
The General Data Protection Regulation (GDPR) is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data. The GDPR applies to any organization that processes personal data of individuals in the European Union (EU), regardless of where the organization is located. The GDPR grants individuals rights over their personal data, such as the right to access, rectify, erase, restrict, or object to its processing. The GDPR also imposes obligations on organizations that process personal data, such as the duty to obtain consent, conduct data protection impact assessments, implement data protection by design and by default, and ensure accountability and transparency. The GDPR also addresses some specific issues related to AI, such as automated decision-making, profiling, and data portability.
NEW QUESTION # 70
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 the data is only collected once.
- B. If it is for a good cause.
- C. If it was with consent of the person it is collected from.
- D. If it was requested by the authority of law.
- E. If it was collected accidentally.
Answer: C,D
Explanation:
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 # 71
You are developing a prediction model. Your team indicates they need an algorithm that is fast and requires low memory and low processing power. Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?
- A. Deep learning neural network
- B. Support-vector machine
- C. Random forest
- D. Ridge regression
Answer: D
Explanation:
Explanation
Ridge regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Ridge regression is fast and requires low memory and low processing power, as it only involves solving a system of linear equations. Ridge regression can also handle multicollinearity (high correlation among predictors) by shrinking the coefficients of correlated predictors.
NEW QUESTION # 72
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