DCPDS questions - Databricks Certified Professional Data Scientist Updated: 2024
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DCPDS Databricks Certified Professional Data Scientist
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).
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.
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.
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
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
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
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/dont
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
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
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.
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
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
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
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.
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, were asking What can you tell me about X?
C . telling the machine Predict Y for our data X
In unsupervised learning we dont have a target variable as we did in classification and regression.
Instead of telling the machine Predict Y for our data X, were 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?
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
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
Support vector machines Naive Bayes Logistic regression Random decision forests
If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has
A . P(E1)/P(E2)
B . P(E1+E2)/P(E1)
C . P(E2)/P(E1)
D . P(E2)/(P(E1+E2)
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
C . Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression
D . Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of
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 models predicted values are small and unbiased.
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
Decreasing the number of clusters will help in adjusting this outlier cluster to get adjusted in another cluster.
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Grab a partner â friend, lover or stranger â and get ready to get intimate.
With this app, drawn from a study discussed in The New York Times and designed in consultation with the study's first author, you and a partner can test if mutual vulnerability brings you closer together.
Before you begin, you or your partner should read the following instructions aloud:
For inspiration, read Mandy Len Catron's Modern Love essay, âTo Fall In Love With Anyone, Do This,â the study by Arthur Aron, Edward Melinat, Elaine N. Aron, Robert Darrin Vallone and Renee J. Bator, originally published in the Personality and Social Psychology Journal (PDF) and a blog post on how the study came to be.
Imagine you approached someone you admired, and boldly asked that person to mentor you. And the answer was âYes!â But a year into the relationship, those monthly mentoring sessions might not invigorate you like they used to, and arenât quite as energizing for the mentor, either.
4 Types Of Questions To Ask A Mentor
To break the ice, have your mentor tell a story from his or her own career. Hey, everybody likes to talk about themselves! For example, you could inquire: âHow did you get to where you are today?â or âHow did you land your current role?â But you could also ask more specific questions that address your career objectives and concerns.Â Some questions to consider:
â˘ Was there a time you messed up and felt like youâd failed? How did you bounce back?
â˘ How did you learn to embrace risk-taking?
â˘ Tell me about a recent business setback. How did you recover?
â˘ Think back to five years ago. Did you envision your career as it is today?
â˘ Was there ever a role you applied for and landed, but weren't 100% qualified to do? How did you proceed?
â˘ What do you wish you had known before taking your first management role?
â˘ Which leadership skills were the most difficult to develop?
â˘ Can you tell me about a time when you had a difficult boss? How did you handle the situation?
â˘ Whatâs the most important leadership lesson youâve learned and how has it proven invaluable?
â˘ How did you develop the skill of speaking so engagingly in front of groups?
Now that the conversation is flowing, get more granular in your requests and bring a specific situation to your mentor--one that youâd like help navigating. For example:
â˘ I tried to delegate a task last week and it did not go well. Can we work through what to do differently next time?
â˘ Who are the people I need to align with in this organization to achieve success?
â˘ My boss said I need to be more strategic. What does that mean?
â˘ How can I let my boss know that I donât need to be micromanaged?
â˘ How can I stay connected to key influencers who do not work in same office or geographical area?
â˘ When trying to gain buy-in to implement a new program, what tactics have worked for you?
â˘ My performance review is coming up. What type of preparation do you most appreciate seeing from your employees?
â˘ I have two very different career path options available to me. Can you weigh in to help me make a final decision?
â˘ I'm considering a career transition. What are some other areas of the business that might be a good fit for me?
â˘ Iâve heard that taking a stretch assignment could help my career trajectory. What are the pros and cons?
One of the greatest gifts you can give yourself is the gift of self-awareness, meaning the ability to see yourself as others view you. That way, if you like how youâre perceived, you can embrace it and take steps to strengthen that positive perception. If you donât like how you are currently perceived, you can take steps to shift that perception to a more positive one that supports, rather than undermines, your career and leadership goals.
After starting with the obvious question: âHow do you think others perceive me?â become more specific, so your mentor can assist by âholding up the mirrorâ and providing detailed feedback on how your actions and communication are impacting the way others see you. Ask questions such as:
â˘ How am I viewed? In other words, what's my personal brand in our organization?
â˘ Where do you see my strengths?
â˘ What do you see as some of my blind spots and how can I improve?
â˘ How I am viewed by leadership?
â˘ What do people say about me when Iâm not in the room?
â˘ Could you offer feedback on ways to improve my executive presence?
â˘ Do I come across as strategic or tactical in my day-to-day communication?
â˘ Am I viewed as high-maintenance when I send my boss weekly status updates?
â˘ How could I have communicated my idea more clearly?
â˘ When I presented at the last meeting, how did I do? Did my communication style support the message I intended to deliver?
Is there a skill youâre currently working to enhance, such as project management, long-term strategic planning, delegating, or public speaking? Use questions like these to ask your mentor for advice and resources to help you polish that skill:
â˘ How can I become a more assertive negotiator?
â˘ Can we role-play asking for a raise and a promotion?
â˘ How can I become better at managing people who do not report to me?
â˘ Do you have any quick tips for re-energizing an overworked team?
â˘ Can you recommend a book or resource for dealing with difficult conversations?
â˘ What practices can you recommend for dealing with nervousness when speaking to groups?
â˘ I have been asked to facilitate a team-building activity at a staff retreat. What are some keys to success?
â˘ Whatâs a good methodology or tool for project management and tracking team commitments?
â˘ Do you have a template that you use for long-range visioning and strategic planning?
â˘ What new skills do I need to move ahead?
With these four types of questions and their accompanying examples, youâll never sit through another mentoring conversation wondering if the other person is finding the discussion useful. And give this list to those whom you mentor, encouraging them to use it to maximize the value of the time you spend together.
âI got this.â
This was my go-to line when I knew what I was doing and wanted to get my boss off my back. What I couldnât see was how it highlighted a major mistake. I wasnât evaluating the situation, I wasnât asking questions. I assumed I knew all the answers, and I was usually wrong.
I didnât realize the error in my ways until a major client rollout flopped and I had no one to blame but myself â my own stubborn belief that âI got this,â even though I clearly didnât.
In a debrief with my boss, he said, âAaron, when you say 'I got this' and have no concerns about a situation, that is when I get concerned.â
What he meant was that as soon as I stop asking compelling questions, I assume I know whatâs going to work and stop evaluating potential outcomes and solutions. Itâs a tendency we all have when we want to take the quick route. Itâs what holds us back from being powerful leaders.
Why is asking powerful questions an essential leadership habit?
It provides leaders with a means to mitigate their confirmation biases and dive deep into the evaluation of a situation, a person or their team as a whole.
I had biases for how the rollout was going to play out. Iâd done this before; I knew what was going to happen, so why should I look further into it? I wish I could say this was unique to me, but we all do this. Our brains are wired to jump to outcomes, to look for shortcuts.
Not sure if this relates to you? Watch this quick video to test yourself.
Daniel Kahneman, Nobel Prize-winning behavioral economist who was the first to highlight these biases, states, âConfirmation bias comes from when you have an interpretation, and you adopt it, and then, top down, you force everything to fit that interpretation.â
This bias can be disastrous for leaders; it can hinder their decision making ability and blindside them completely. Asking powerful questions is our way around it. It can help us avoid this common miscalculation.
What does a powerful question look like?
Iâm going to share a definition of and the criteria for a powerful question, but I also want to be clear there is no script for asking a powerful question. Powerful questions evoke clarity, create greater possibility, reveal new learning and generate action. Here are a few ways to determine if a question is powerful or not.
A powerful question âŚ
â˘ Is open-ended: Ask what, when or how instead of asking a yes or no question.
â˘ Comes from a beginnerâs mindset: Start by telling yourself, âI donât know the answer.â
â˘ Is clear and succinct: Keep it simple, donât use too many words.
â˘ Is impactful: Itâs important to remember that not every question in a conversation should be powerful. In a 30-minute conversation, aim for 2-3 powerful questions.
â˘ Happens in the moment: Here is probably the most crucial point to remember about powerful questioning. You canât plan it! Formulaic questions outlined before the conversation wonât work. You have to be in the moment.
There is no script for asking powerful questions. There is, however, an often-overlooked trait that will set you up to ask powerful questions in any situation.
Whatâs the trait?
Curiosity. Want to discover a master of curiosity? Find any 3-year old and watch them for an hour. They ask what, why and how to nearly everything they see in the world around them. They want to know more and do not limit themselves to the societal expectations of whatâs right or wrong. They just ask.
As we get older, we are trained to lose our curiosity when it becomes clear itâs not acceptable to ask all the questions that come to mind. Instead, we go about our days having surface-level conversations, rarely digging more in-depth with a co-worker, client or even a friend.
The secret to asking more powerful questions is digging deeper. Itâs triggering our 3-year old selves and reconnecting with our curiosity.
I found it hard to come up with a way to share this concept with you. I realized itâs so hard to explain because, as adults, there are very few situations where we are curious. Then I remembered riddles. They are a great way to bring the curiosity right back. Try this one out.
âWhat has a head, a tail, is brown and has no legs?â
As you are reading this, trying to figure out the answer, your mind is swirling with questions and possibilities.
What kind of animal has no legs?
Is it an animal?
What else could it be?
What sorts of things have tails?
The series of questions running through your head is your curiosity showing up. Itâs the little kid inside of you wanting to understand, to know. Curiosity is the genuine desire to learn more -- to explore.
To be able to evaluate people, teams or situations with greater fidelity, go back to the curious part of you that wants to explore. Instead of restricting yourself, open yourself up and allow your mind to ask any question.
Allow yourself to ask the powerful questions. You already have them in you.
Sometimes it may take priming yourself with a riddle to get you there.
âWhat has a head, a tail, is brown and has no legs?â
Since it is colder outside, grab your favorite warm drink and spend some time learning about your future (or upcoming) application.
Visit your future application site and professional organizations (found on the Career Guides available on our Career page in the first drop-down box for that field). Also, if you have an admission test to take for your application, start learning all about it as early in the process as you can. We are happy to be here for you and to answer any questions!
We are here to support your journey to professional school! If you have pre-professional related questions, please make an appointment through BoilerConnect or email us at email@example.com
The advisors in Pre-Professional Advising strive daily to reject racism, discrimination, and systemic inequity and are committed to creating an accessible environment where everyone feels welcome and safe.
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CNN Â âÂ
ChatGPT might not be a cure-all for answers to medical questions, a new study suggests.
Researchers at Long Island University posed 39 medication-related queries to the free version of the artificial intelligence chatbot, all of which were real questions from the universityâs College of PharmacyÂ drug information service. The softwareâs answers were then compared with responses written and reviewed by trained pharmacists.
The study found that ChatGPT provided accurate responses to only about 10 of the questions, or about a quarter of the total. For the other 29 prompts, the answers were incomplete or inaccurate, or they did not address the questions.
The findings were presented Tuesday at the annual meeting of the American Society for Health-Systems Pharmacists in Anaheim, California.
ChatGPT, OpenAIâs experimental AI chatbot, was released in November 2022 and became the fastest-growing consumer application in history, with nearly 100 million people registering within two months.
Given that popularity, the researchersâ interest was sparked by concern that their students, other pharmacists and ordinary consumers would turn to resources like ChatGPT to explore questions about their health and medication plans, said Sara Grossman, an associate professor of pharmacy practice at Long Island University and one of the studyâs authors.
Those queries, they found, often yielded inaccurate â or even dangerous â responses.
In one question, for example, researchers asked ChatGPT whether the Covid-19 antiviral medication Paxlovid and the blood-pressure lowering medication verapamil would react with each other in the body. ChatGPT responded that taking the two medications together would yield no adverse effects.
In reality, people who take both medications might have a large drop in blood pressure, which can cause dizziness and fainting. For patients taking both, clinicians often create patient-specific plans, including lowering the dose of verapamil or cautioning the person to get up slowly from a sitting position, Grossman said.
ChatGPTâs guidance, she added, would have put people in harmâs way.
âUsing ChatGPT to address this question would put a patient at risk for an unwanted and preventable drug interaction,â Grossman wrote in an email to CNN.
When the researchers asked the chatbot for scientific references to support each of its responses, they found that the software could provide them for only eight of the questions they asked. And in each case, they were surprised to find that ChatGPT was fabricating references.
At first glance, the citations looked legitimate: They were often formatted appropriately, provided URLs and were listed under legitimate scientific journals. But when the team attempted to find the referenced articles, they realized that ChatGPT had given them fictional citations.
In one case, the researchers asked ChatGPT how to convert spinal injection doses of the muscle spasm medication baclofen to corresponding oral doses. Grossmanâs team could not find a scientifically established dose conversion ratio, but ChatGPT put forth a single conversion rate and cited two medical organizationsâ guidance, she said.
However, neither organization provides any official guidance on the dose conversion rate. In fact, the conversion factor that ChatGPT suggested had never been scientifically established. The software also provided an example calculation for the dose conversion but with a critical mistake: It mixed up units when calculating the oral dose, throwing off the dose recommendation by a factor of 1,000.
If that guidance was followed by a health care professional, Grossman said, they might give a patient an oral baclofen dose 1,000 times lower than required, which could cause withdrawal symptoms like hallucinations and seizures.
âThere were numerous errors and âproblemsâ with this response and ultimately, it could have a profound impact on patient care,â she wrote.
The Long Island University study is not the first to raise concerns about ChatGPTâs fictional citations. Previous research has also documented that, when asked medical questions, ChatGPT can create deceptive forgeries of scientific references, even listing the names of real authors with previous publications in scientific journals.
Grossman, who had worked little with the software before the study, was surprised by how confidently ChatGPT was able to synthesize information nearly instantaneously, answers that would take trained professionals hours to compile.
âThe responses were phrased in a very professional and sophisticated manner, and it just seemed it can contribute to a sense of confidence in the accuracy of the tool,â she said. âA user, a consumer, or others that may not be able to discern can be swayed by the appearance of authority.â
A spokesperson for OpenAI, the organization that develops ChatGPT, said it advises users not to rely on responses as a substitute for professional medical advice or treatment.
The spokesperson pointed to ChatGPTâs usage policies, which indicate that âOpenAIâs models are not fine-tuned to provide medical information.â The policy also states that the models should never be used to provide âdiagnostic or treatment services for serious medical conditions.â
Although Grossman was unsure of how many people use ChatGPT to address medication questions, she raised concerns that they could use the chatbot like they would search for medical advice on search engines like Google.
âPeople are always looking for instantaneous responses when they have this at their fingertips,â Grossman said. âI think that this is just another approach of using âDr. Googleâ and other seemingly easy methods of obtaining information.â
For online medical information, she recommended that consumers use governmental websites that provide reputable information, like the National Institutes of Healthâs MedlinePlus page.
Still, Grossman doesnât believe that online answers can replace the advice of a health care professional.
â[Websites are] maybe one starting point, but they can take their providers out of the picture when looking for information about medications that are directly applicable to them,â she said. âBut it may not be applicable to the patients themselves because of their personal case, and every patient is different. So the authority here should not be removed from the picture: the healthcare professional, the prescriber, the patientâs physicians.â
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