Cloudera Databricks Data Science Certification Questions and Answers (Dumps and Practice Questions)
Question : Select the correct option from the below
1. If you're trying to predict or forecast a target value, then you need to look into supervised learning.
2. If you've chosen supervised learning, with discrete target value like Yes/No, 1/2/3, A/B/C, or Red/Yellow/Black, then look into classification.
3. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999, or +_ to -_, then you need to look unsupervised learning
4. If you're not trying to predict a target value, then you need to look into unsupervised learning
5. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering.
1. 1, 2,3,4,5
2. 2,3,4,5
3. 1,2,4,5
4. 1,2,3,5
5. 2,3,4,5
Correct Answer : 3
If you're trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No, 1/2/3, A/B/C, or Red/Yellow/Black? If so, then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999, or +_ to -_, then you need to look into regression. If you're not trying to predict a target value, then you need to look into unsupervised learning. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering. Do you need to have some numerical estimate of how strong the fit is into each group? If you answer yes, then you probably should look into a density estimation algorithm.
Question : Select the sequence of the developing machine learning applications
A. Analyze the input data
B. Prepare the input data
C. Collect data
D. Train the algorithm
E. Test the algorithm
F. Use It
1. A,B,C,D,E,F
2. C,B,A,D,E,F
3. C,A,B,D,E,F
4. C,B,A,D,E,F
Correct Answer : 4
1 Collect data. You could collect the samples by scraping a website and extracting data, or you could get information from an RSS feed or an API. You could have a device collect wind speed measurements and send them to you, or blood glucose levels, or anything you can measure. The number of options is endless. To save some time and effort, you could use publicly available data.
2 Prepare the input data. Once you have this data, you need to make sure it's in a useable format. The format we'll be using in this book is the Python list. We'll talk about Python more in a little bit, and lists are reviewed in appendix A. The benefit of having this standard format is that you can mix and match algorithms and data sources. You may need to do some algorithm-specific formatting here. Some algorithms need features in a special format, some algorithms can deal with target variables and features as strings, and some need them to be integers. We'll get to this later, but the algorithm-specific formatting is usually trivial compared to collecting data.
3 Analyze the input data. This is looking at the data from the previous task. This could be as simple as looking at the data you've parsed in a text editor to make sure steps 1 and 2 are actually working and you don't have a bunch of empty values. You can also look at the data to see if you can recognize any patterns or if there's anything obvious, such as a few data points that are vastly different from the rest of the set. Plotting data in one, two, or three dimensions can also help. But most of the time you'll have more than three features, and you can't easily plot the data across all features at one time. You could, however, use some advanced methods we'll talk about later to distill multiple dimensions down to
two or three so you can visualize the data.
4 If you're working with a production system and you know what the data should look like, or you trust its source, you can skip this step. This step takes human involvement, and for an automated system you don't want human involvement. The value of this step is that it makes you understand you don't have garbage coming in.
5 Train the algorithm. This is where the machine learning takes place. This step and the next step are where the "core" algorithms lie, depending on the algorithm.You feed the algorithm good clean data from the first two steps andextract knowledge or information. This knowledge you often store in a formatthat's readily useable by a machine for the next two steps.In the case of unsupervised learning, there's no training step because youdon't have a target value. Everything is used in the next step.
6 Test the algorithm. This is where the information learned in the previous step isput to use. When you're evaluating an algorithm, you'll test it to see how well itdoes. In the case of supervised learning, you have some known values you can use to evaluate the algorithm. In unsupervised learning, you may have to use some other metrics to evaluate the success. In either case, if you're not satisfied, you can go back to step 4, change some things, and try testing again. Often thecollection or preparation of the data may have been the problem, and you'll have to go back to step 1.
7 Use it. Here you make a real program to do some task, and once again you see if all the previous steps worked as you expected. You might encounter some new data and have to revisit steps 1-5.
Question :
Select the correct statement which applies to K-Nearest Neighbors
1. No Assumption about the data
2. Computationaly expensive
3. Require less memory
4. Works with Numeric Values
1. 1,2,3,4
2. 2,3,4
3. 1,3,4
4. 1,2,4
Correct Answer : 4
k-Nearest Neighbors
Pros: High accuracy, insensitive to outliers, no assumptions about data
Cons: Computationally expensive, requires a lot of memory
Works with: Numeric values, nominal values
Related Questions
Question :
Which of the following conditions should be satisfies with Poisson random variable
1. The number of successes in two disjoint time intervals is independent.
2. The probability of a success during a small time interval is proportional to the entire length of the time interval.
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Question :
A company makes electric motors. The probability an electric motor is defective is 0.01.
Which is the best model to calculate the probability that a sample of 300 electric motors will contain exactly 5 defective motors?
1. Binomial
2. Poisson
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4. Any of the above
Question : Which of the following properties A binomial experiment possesses?
1. The experiment consists of n repeated trials
2. Each trial results in an outcome that may be classified as a success or a failure.
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4. Only 1 and 2
5. All 1,2 and 3
Question :
A website is opened 3 times by a user. What is the probability of he clicks 2 times the advertisement, is best calculated by
1. Binomial
2. Poisson
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4. Any of the above
Question :
A problem statement is given as below
Hospital records show that of patients suffering from a certain disease, 75% die of it. What is the probability that of 6 randomly selected patients, 4 will recover?
Which of the following model will you use to solve it.
1. Binomial
2. Poisson
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4. Any of the above
Question : Which of the following problem you can solve using binomial distribution
1. A manufacturer of metal pistons finds that on the average, 12% of his pistons are rejected
because they are either oversize or undersize. What is the probability that a batch of 10 pistons will contain no more than 2 rejects?
2. A life insurance salesman sells on the average 3 life insurance policies per week.
Use Poisson's law to calculate the probability that in a given week he will sell Some policies
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Find the probability that none passes in a given minute
4. It was found that the mean length of 100 parts produced by a lathe was 20.05 mm with a standard
deviation of 0.02 mm. Find the probability that a part selected at random would have a length between 20.03 mm and 20.08 mm