Which type of data is typically stored in relational databases with defined rows and columns?
- Unstructured data
- Tabular data
- Hierarchical data
- NoSQL data store
Relational databases are designed for storing structured data with well-defined rows and columns. This structured format allows for efficient storage and querying of data. Unstructured data, on the other hand, lacks a predefined structure.
In SQL, how can you prevent SQL injection in your queries?
- Use stored procedures
- Encrypt the database
- Use Object-Relational Mapping (ORM)
- Sanitize and parameterize inputs
To prevent SQL injection, you should sanitize and parameterize user inputs in your queries. This involves validating and escaping user input data to ensure that it cannot be used to execute malicious SQL commands. Other options, while important, do not directly prevent SQL injection.
In NoSQL databases, the absence of a fixed schema means that databases are _______.
- Structured
- Relational
- Schemaless
- Document-oriented
NoSQL databases are schemaless, which means they do not require a fixed schema for data storage. This flexibility allows for the storage of various types of data without predefined structure constraints.
Which ETL tool provides native integrations with Apache Hadoop, Apache Spark, and other big data technologies?
- Talend
- Informatica
- SSIS (SQL Server Integration Services)
- Apache Nifi
Talend is an ETL (Extract, Transform, Load) tool known for providing native integrations with Apache Hadoop, Apache Spark, and other big data technologies. This makes it a popular choice for organizations dealing with big data workloads, as it allows for efficient data extraction and processing from these technologies within the ETL pipeline. Other tools mentioned do not offer the same level of native integration with big data technologies.
A bank wants to segment its customers based on their credit card usage behavior. Which learning method and algorithm would be most appropriate for this task?
- Supervised Learning with Decision Trees
- Unsupervised Learning with K-Means Clustering
- Reinforcement Learning with Q-Learning
- Semi-Supervised Learning with Support Vector Machines
Unsupervised Learning with K-Means Clustering is suitable for customer segmentation as it groups customers based on similarities in credit card usage behavior without predefined labels. Supervised learning requires labeled data, reinforcement learning is used for sequential decision-making, and semi-supervised learning combines labeled and unlabeled data.
Which type of data can often be represented as a combination of structured tables with metadata or annotations?
- Time Series Data
- Geospatial Data
- Semi-Structured Data
- Categorical Data
Semi-structured data is a type of data that falls between structured and unstructured data. It can often be represented as a combination of structured tables with additional metadata or annotations. This format provides some level of organization and makes it more manageable for analysis. Examples of semi-structured data include JSON, XML, and log files, which have some inherent structure but may also contain unstructured elements.
A self-driving car company has millions of images labeled with either "pedestrian" or "no pedestrian". They want the car to automatically detect pedestrians. Which type of learning and algorithm would be optimal for this task?
- Supervised Learning with Convolutional Neural Networks
- Unsupervised Learning with Apriori Algorithm
- Reinforcement Learning with Monte Carlo Methods
- Semi-Supervised Learning with DBSCAN
Supervised Learning with Convolutional Neural Networks (CNNs) is the optimal choice for image classification tasks like pedestrian detection. CNNs are designed for such tasks, while the other options are not suitable for image classification. Apriori is used for association rule mining, reinforcement learning for decision-making, and DBSCAN for clustering.
Apache Spark offers an optimized engine that supports _______ computations, enabling faster data analytics.
- Batch
- Single-threaded
- Real-time
- Static
Apache Spark offers an optimized engine that supports real-time computations. This capability enables faster data analytics by allowing Spark to process data as it arrives, making it suitable for real-time data processing and analytics tasks. This is a key advantage of Spark over traditional batch processing systems.
Which statistical measure represents the middle value in a dataset when it's ordered from least to greatest?
- Mean
- Mode
- Median
- Range
The median is the middle value in a dataset when it's ordered. It's a measure of central tendency that's not affected by extreme values (outliers). To find the median, you arrange the data in ascending order, and if there's an even number of values, it's the average of the two middle values.
Hybrid recommender systems combine the features of both _______ and _______ methods.
- Collaborative, Clustering
- Content-Based, Matrix Factorization
- Dimensionality Reduction, Anomaly Detection
- Neural Networks, Regression
Hybrid recommender systems leverage both collaborative filtering (user-user/item-item) and content-based methods to provide more accurate recommendations. Collaborative filtering focuses on user behavior, while content-based filtering considers item attributes.