Databricks
Databricks Certified Professional Data Scientist
Q1:
You are working on a email spam filtering assignment, while working on this you find there is new word e.g. HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero. So which of the following algorithm can help you to avoid zero probability?
○
A
Naive Bayes○
B
Laplace Smoothing○
C
Logistic Regression○
D
All of the aboveLaplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.
Databricks
Databricks Certified Professional Data Scientist
Q2:
You are studying the behavior of a population, and you are provided with multidimensional data at the individual level. You have identified four specific individuals who are valuable to your study, and would like to find all users who are most similar to each individual. Which algorithm is the most appropriate for this study?
○
A
Association rules○
B
Decision trees○
C
Linear regression○
D
K-means clusteringkmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.
Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified,
labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.
Databricks
Databricks Certified Professional Data Scientist
Q3:
Select the correct statement which applies to logistic regression
○
A
Computationally inexpensive, easy to implement knowledge representation easy to interpret○
B
May have low accuracy○
C
Works with Numeric values○
D
Only 1 and 3 are correct○
E
All 1, 2 and 3 are correctDepending on the size of the data you are uploading, Amazon S3 offers the following options:
Logistic regression
Pros: Computationally inexpensive, easy to implement knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values^ nominal values
Databricks
Databricks Certified Professional Data Scientist
Q4:
In which lifecycle stage are test and training data sets created?
○
A
Model planning○
B
Discovery○
C
Model building○
D
Data preparationIn Phase 1, the team learns the business domain, including relevant history such as whether the organization or business unit has attempted similar projects in the past from which they can learn. The team assesses the resources available to support the project in terms of people, technology time, and data. Important activities in this phase include framing the business problem as an analytics challenge that can be addressed in subsequent phases and formulating initial hypotheses (IHs) to test and begin learning the data. Data preparation: Phase 2 requires the presence of an analytic sandbox, in which the team can work with data and perform analytics for the duration of the project. The team needs to execute extract, load, and transform (ELT) or extract, transform and load (ETL) to get data into the sandbox. The ELT and ETL are sometimes abbreviated as ETLT Data should be transformed in the ETLT process so the team can work with it and analyze it. In this phase, the team also needs to familiarize itself with the data thoroughly and take steps to condition the data Model planning: Phase 3 is model planning, where the team determines the methods, techniques, and workflow it intends to follow for the subsequent model building phase. The team explores the data to learn about the relationships between variables and subsequently selects key variables and the most suitable models.
Model building: In Phase 4, the team develops datasets for testing, training, and production purposes. In addition, in this phase the team builds and executes models based on the work done in the model planning phase. The team also considers whether its existing tools will suffice for running the models, or if it will need a more robust environment for executing models and workflows (for example, fast hardware and parallel processing, if applicable).
Communicate results: In Phase 5, the team, in collaboration with major stakeholders, determines if the results of the project are a success or a failure based on the criteria developed in Phase 1. The team should identify key findings, quantify the business value, and develop a narrative to summarize and convey findings to stakeholders.
Operationalize: In Phase 6, the team delivers final reports, briefings, code, and technical documents. In addition, the team may run a pilot project to implement the models in a production environment.
Databricks
Databricks Certified Professional Data Scientist
Q5:
Which of the following true with regards to the K-Means clustering algorithm?
☐
A
Labels are not pre-assigned to each objects in the cluster.☐
B
Labels are pre-assigned to each objects in the cluster.☐
C
It classify the data based on the labels.☐
D
It discovers the center of each cluster.☐
E
It find each objects fall in which particular clusterClustering does not require any predefined labels on the object, rather it consider the attributes on the object. Hence, option-B is out. Clustering is different than classification technique.
Hence you can discard the option-C as well. It does not use the pre-defined labels, hence it is called unsupervised learning and option-Ais correct. Main purpose of the Clustering technique is to determine the center of each Cluster and then find the distance from that center. If object is near the center than it would fall in that particular cluster. Hence, finally you will have group or clusters created and get to know that objects fall in which particular cluster.