MCQs on Machine Learning with MLlib | Apache Spark MCQs Questions
Apache Spark is a powerful open-source analytics engine for large-scale data processing. Its MLlib library simplifies building machine learning models, enabling tasks such as classification, regression, clustering, and recommendation systems. This set of Apache Spark MCQs questions focuses on MLlib, covering its architecture, feature engineering, pipelines, collaborative filtering, and hyperparameter tuning. Gain insights into how Spark MLlib empowers scalable machine learning and helps solve real-world data challenges with advanced capabilities like cross-validation and parameter optimization.
Chapter 6: Machine Learning with MLlib
Topic 1: MLlib Overview and Architecture
- What is the primary purpose of MLlib in Apache Spark?
a) Data storage
b) Machine learning
c) Query optimization
d) Real-time streaming - MLlib in Apache Spark is:
a) A graph processing library
b) A distributed machine learning library
c) A data streaming component
d) A SQL query optimizer - Which data structure is commonly used in MLlib for machine learning algorithms?
a) RDDs
b) DataFrames
c) Key-Value Pairs
d) Graphs - The main advantage of using MLlib over traditional ML tools is:
a) High-performance single-node processing
b) Distributed processing for scalability
c) Reduced memory usage
d) Limited algorithm support - MLlib is tightly integrated with:
a) Apache Hadoop
b) Apache Flink
c) Apache Cassandra
d) Apache Spark core
Topic 2: Feature Engineering and Pipelines
- In MLlib, feature engineering primarily involves:
a) Data storage optimization
b) Extracting meaningful features for ML models
c) Data visualization
d) Network communication - Which MLlib method is used to normalize data?
a) Normalizer
b) StandardScaler
c) MinMaxScaler
d) VectorAssembler - What is the role of VectorAssembler in Spark MLlib?
a) To scale data
b) To combine multiple feature columns into a single vector
c) To split datasets into training and testing sets
d) To reduce dimensionality - Pipelines in MLlib are used for:
a) Streaming data processing
b) Simplifying the workflow of machine learning models
c) Managing storage
d) Running SQL queries - MLlib pipelines consist of:
a) Steps of transformations and estimators
b) Graph processing tasks
c) Data visualization modules
d) Random data generators
Topic 3: Classification, Regression, and Clustering
- Which MLlib algorithm is used for classification tasks?
a) K-Means
b) Decision Trees
c) Collaborative Filtering
d) Linear Regression - In regression analysis with MLlib, which metric is commonly used to measure model performance?
a) Logarithmic loss
b) Root Mean Squared Error (RMSE)
c) Silhouette score
d) Cluster purity - K-Means in Spark MLlib is used for:
a) Regression
b) Clustering
c) Classification
d) Recommendation systems - Which of the following algorithms in MLlib supports both regression and classification?
a) Logistic Regression
b) Random Forest
c) Gradient Boosted Trees
d) Naive Bayes - In clustering with MLlib, the Silhouette score measures:
a) Data normalization
b) Model interpretability
c) The quality of clusters
d) Training time efficiency
Topic 4: Collaborative Filtering and Recommendation Systems
- Which algorithm in Spark MLlib is used for collaborative filtering?
a) Alternating Least Squares (ALS)
b) K-Means
c) Decision Trees
d) Linear Regression - Collaborative filtering in MLlib is best suited for:
a) Predicting user preferences based on past behaviors
b) Generating clusters of data points
c) Managing big data storage
d) Reducing dimensionality - In MLlib, ALS is primarily used for:
a) Recommendation systems
b) Regression analysis
c) Clustering
d) Time-series forecasting - Which type of filtering technique does ALS in MLlib implement?
a) Content-based filtering
b) Hybrid filtering
c) Collaborative filtering
d) Dimensionality reduction - ALS in MLlib is optimized for:
a) Distributed data processing
b) Single-node processing
c) Feature selection
d) Data cleaning
Topic 5: Hyperparameter Tuning and Cross-Validation
- What is the purpose of hyperparameter tuning in MLlib?
a) Reduce dataset size
b) Optimize the performance of machine learning models
c) Perform feature scaling
d) Generate synthetic data - Which method in MLlib helps automate hyperparameter tuning?
a) CrossValidator
b) Pipeline
c) GridSearchCV
d) Estimator - Cross-validation in MLlib is used for:
a) Data preprocessing
b) Evaluating the generalization ability of a model
c) Clustering data points
d) Scaling features - During hyperparameter tuning, which of these is considered a hyperparameter?
a) Model coefficients
b) Learning rate
c) Data normalization
d) Data schema - Cross-validation in MLlib involves splitting data into:
a) Clusters and centroids
b) Training and validation sets
c) Random partitions
d) Feature subsets
Topic 6: Advanced Concepts
- What is schema drift in MLlib?
a) A change in the underlying dataset structure
b) An optimization method for scaling datasets
c) A clustering technique
d) A visualization method - MLlib supports integration with which of these libraries for advanced analytics?
a) NumPy
b) TensorFlow
c) PyTorch
d) All of the above - Apache Spark’s MLlib uses which of the following for parallel processing?
a) GPUs
b) RDDs and DataFrames
c) Cloud-only clusters
d) None of the above - Spark MLlib is designed to handle:
a) Structured data only
b) Unstructured data only
c) Both structured and unstructured data
d) Semi-structured data only - MLlib pipelines help in:
a) Writing SQL queries
b) Simplifying machine learning workflows
c) Data visualization
d) Data storage
Answers
| Qno | Answer |
|---|---|
| 1 | b) Machine learning |
| 2 | b) A distributed machine learning library |
| 3 | a) RDDs |
| 4 | b) Distributed processing for scalability |
| 5 | d) Apache Spark core |
| 6 | b) Extracting meaningful features for ML models |
| 7 | c) MinMaxScaler |
| 8 | b) To combine multiple feature columns into a single vector |
| 9 | b) Simplifying the workflow of machine learning models |
| 10 | a) Steps of transformations and estimators |
| 11 | b) Decision Trees |
| 12 | b) Root Mean Squared Error (RMSE) |
| 13 | b) Clustering |
| 14 | b) Random Forest |
| 15 | c) The quality of clusters |
| 16 | a) Alternating Least Squares (ALS) |
| 17 | a) Predicting user preferences based on past behaviors |
| 18 | a) Recommendation systems |
| 19 | c) Collaborative filtering |
| 20 | a) Distributed data processing |
| 21 | b) Optimize the performance of machine learning models |
| 22 | a) CrossValidator |
| 23 | b) Evaluating the generalization ability of a model |
| 24 | b) Learning rate |
| 25 | b) Training and validation sets |
| 26 | a) A change in the underlying dataset structure |
| 27 | d) All of the above |
| 28 | b) RDDs and DataFrames |
| 29 | c) Both structured and unstructured data |
| 30 | b) Simplifying machine learning workflows |