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Databricks Certified Professional Data Scientist Exam Dumps

DCPDS Exam Format | Course Contents | Course Outline | Exam Syllabus | Exam Objectives

Exam Detail:
The DCPDS (Databricks Certified Professional Data Scientist) exam is a certification exam that validates the knowledge and skills of individuals in data science using Databricks. Here are the exam details for the DCPDS certification:

- Number of Questions: The exam typically consists of multiple-choice questions and hands-on exercises. The exact number of questions may vary, but typically, the exam includes around 60 to 80 questions.

- Time Limit: The time allocated to complete the exam is 2 hours (120 minutes).

Course Outline:
The DCPDS certification course covers various topics related to data science using Databricks. The course outline typically includes the following topics:

1. Introduction to Databricks:
- Understanding the basics of Databricks and its role in data science.
- Navigating the Databricks workspace and user interface.

2. Data Exploration and Preparation:
- Exploring and understanding data using Databricks.
- Performing data preprocessing tasks such as data cleaning, transformation, and feature engineering.

3. Machine Learning with Databricks:
- Applying machine learning algorithms and techniques using Databricks.
- Building and training machine learning models.
- Evaluating and tuning model performance.

4. Advanced Analytics and Visualization:
- Using Databricks for advanced analytics tasks such as clustering, time series analysis, and text analysis.
- Visualizing data and model results using Databricks' visualization tools.

5. Model Deployment and Monitoring:
- Deploying machine learning models in production using Databricks.
- Monitoring and evaluating model performance and making necessary adjustments.

6. Collaborative Workflows:
- Working collaboratively with other data scientists and stakeholders in Databricks.
- Sharing and presenting insights and results using Databricks' collaboration features.

Exam Objectives:
The objectives of the DCPDS exam are as follows:

- Assessing candidates' understanding of Databricks and its role in data science.
- Evaluating candidates' knowledge and proficiency in data exploration and preparation using Databricks.
- Testing candidates' skills in applying machine learning algorithms and techniques using Databricks.
- Assessing candidates' ability to perform advanced analytics tasks and visualize data in Databricks.
- Evaluating candidates' competence in deploying and monitoring machine learning models in Databricks.
- Testing candidates' understanding of collaborative workflows and effective communication in Databricks.

Exam Syllabus:
The specific exam syllabus for the DCPDS certification covers the following areas:

1. Databricks Basics: Understanding the Databricks workspace, user interface, and collaborative features.

2. Data Exploration and Preparation: Performing data exploration, cleaning, and transformation using Databricks.

3. Machine Learning with Databricks: Applying machine learning algorithms and techniques in Databricks.

4. Advanced Analytics and Visualization: Performing advanced analytics tasks and visualizing data in Databricks.

5. Model Deployment and Monitoring: Deploying and monitoring machine learning models in Databricks.

6. Collaborative Workflows: Working collaboratively and effectively communicating with stakeholders in Databricks.

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Databricks Certified Professional Data Scientist
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Question: 42
Refer to Exhibit
In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the
pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are
known to have defaulted on their loan.
Which analytical method could produce the probabilities needed to build this exhibit?
A . Linear Regression
B . Logistic Regression
C . Discriminant Analysis
D . Association Rules
Answer: B
Question: 43
Select the statement which applies correctly to the Naive Bayes
A . Works with a small amount of data
B . Sensitive to how the input data is prepared
C . Works with nominal values
Answer: A,B,C
Question: 44
A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average)
and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don�t
admit, is a binary variable.
Above is an example of
A . Linear Regression
B . Logistic Regression
C . Recommendation system
D . Maximum likelihood estimation
E . Hierarchical linear models
Answer: B
Explanation:
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
Question: 45
What describes a true limitation of Logistic Regression method?
A . It does not handle redundant variables well.
B . It does not handle missing values well.
C . It does not handle correlated variables well.
D . It does not have explanatory values.
Answer: B
Question: 46
Which of the following technique can be used to the design of recommender systems?
A . Naive Bayes classifier
B . Power iteration
C . Collaborative filtering
D . 1 and 3
E . 2 and 3
Answer: C
Explanation:
One approach to the design of recommender systems that has seen wide use is collaborative filtering. Collaborative
filtering methods are based on collecting and analyzing a large amount of information on users� behaviors, activities or
preferences and predicting what users will like based on their similarity to other users. A key advantage of the
collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of
accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many
algorithms have been used in measuring user similarity or item similarity in recommender systems. For example the k-
nearest neighbor (k-NN) approach and the Pearson Correlation
Question: 47
Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several
variables that may be��
A . Numerical
B . Categorical
C . Both 1 and 2 are correct
D . None of the 1 and 2 are correct
Answer: C
Explanation:
Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several
predictor variables that may be either numerical or categories.
Question: 48
In unsupervised learning which statements correctly applies
A . It does not have a target variable
B . Instead of telling the machine Predict Y for our data X, we�re asking What can you tell me about X?
C . telling the machine Predict Y for our data X
Answer: A,B
Explanation:
In unsupervised learning we don�t have a target variable as we did in classification and regression.
Instead of telling the machine Predict Y for our data X, we�re asking What can you tell me about X?
Things we ask the machine to tell us about X may be What are the six best groups we can make out of X? or What
three features occur together most frequently in X?
Question: 49
You are working on a problem where you have to predict whether the claim is done valid or not. And you find that
most of the claims which are having spelling errors as well as corrections in the manually filled claim forms compare
to the honest claims.
Which of the following technique is suitable to find out whether the claim is valid or not?
A . Naive Bayes
B . Logistic Regression
C . Random Decision Forests
D . Any one of the above
Answer: D
Explanation:
In this problem you have been given high-dimensional independent variables like texts, corrections, test results etc.
and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this
problem.
Support vector machines Naive Bayes Logistic regression Random decision forests
Question: 50
If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has
occurred?
A . P(E1)/P(E2)
B . P(E1+E2)/P(E1)
C . P(E2)/P(E1)
D . P(E2)/(P(E1+E2)
Answer: C
Question: 51
Which of the following statement true with regards to Linear Regression Model?
A . Ordinary Least Square can be used to estimates the parameters in linear model
B . In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and
input variables.
C . Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression
model.
D . Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of
regression model.
Answer: A,D
Explanation:
Linear regression model are represented using the below equation
Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the
plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and B(1). And similarly it
is a sum of squared distance between the observed point and the fitted line. Ordinary least squares (OLS) regression
minimizes the sum of the squared residuals. A model fits the data well if the differences between the observed values
and the model�s predicted values are small and unbiased.
Question: 52
You have data of 10.000 people who make the purchasing from a specific grocery store. You also have their income
detail in the data. You have created 5 clusters using this data. But in one of the cluster you see that only 30 people are
falling as below 30, 2400, 2600, 2700, 2270 etc."
What would you do in this case?
A . You will be increasing number of clusters.
B . You will be decreasing the number of clusters.
C . You will remove that 30 people from dataset
D . You will be multiplying standard deviation with the 100
Answer: B
Explanation:
Decreasing the number of clusters will help in adjusting this outlier cluster to get adjusted in another cluster.
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