Databricks Certified Machine Learning
Associate
MACHINE LEARNING BASIC PART EXAM
Which of the following evaluation metrics is not suitable to evaluate run in AutoML
experiment to regression problem?
A. F1
B. R-Squared
C. RMSE
D. MAE
E. MSE - answerA. F1
F1 is not a suitable evaluation metric for a regression problem. F1 is a metric that is
used to evaluate the performance of a classifier, and it is not appropriate to use in the
context of a regression problem.
R-squared, RMSE, MAE, and MSE are all suitable evaluation metrics for a regression
problem.
R-squared is a measure of the goodness of fit of a regression model, RMSE is a
measure of the average error of the model, MAE is a measure of the average absolute
error of the model, and MSE is a measure of the average squared error of the model.
MACHINE LEARNING BASIC PART EXAM
A health organization is developing a classification model to determine whether or not a
patient currently has a specific type of infection. The organization's leaders want to
maximize the number of cases identified by the model.
Which of the following classification metrics should be used to evaluate the model?
A. Accuracy
B. RMSE
C. Area under the residual operating curve
D. Precision
E. Recall - answerE. Recall
It is important to consider multiple evaluation metrics when evaluating a classification
model, rather than just focusing on one metric.
However, if the organization's leaders want to maximize the number of cases identified
by the model, they might be particularly interested in the recall of the model.
Recall measures the proportion of actual positive cases that were correctly identified by
the model. It is calculated as the number of true positives divided by the sum of the true
positives and false negatives. A model with high recall is able to identify a large
proportion of positive cases, even if it also has a large number of false positives.
Accuracy, RMSE, and the area under the residual operating curve are not appropriate
metrics for evaluating a classification model.
MACHINE LEARNING BASIC PART EXAM
,What is the name of the method that transforms categorical feature into a series of
binary indicator feature variables?
A. Leave-one-out encoding
B. String indexing
C. Target encoding
D. One-hot encoding
E. Categorical embeddings - answerD. One-hot encoding
The method that transforms categorical feature into a series of binary indicator feature
variables is called one-hot encoding.
One-hot encoding is a way to represent categorical variables as numerical data so that
it can be used in machine learning algorithms. It involves creating a new binary column
for each unique category in the categorical feature.
For example, if a categorical feature has three categories, A, B, and C, then three new
columns, one for each category, would be created. If a given data point belongs to
category A, then the value in the A column would be 1, and the values in the B and C
columns would be 0.
One-hot encoding allows the model to treat each category as a separate entity, rather
than as a numeric value that can be compared or ordered.
MACHINE LEARNING BASIC PART EXAM
In which of the following situations is it preferable to impute missing feature values with
their median value over the mean value?
A. When the features are of the boolean type
B. When the features contain no outliers
C. When the features are of the categorical type
D. When the features contain a lot of extreme outliers
E. When the features contain no missing values - answerD. When the features contain
a lot of extreme outliers
It is generally preferable to impute missing feature values with the median value rather
than the mean value in situations where the feature contains a lot of extreme outliers.
This is because the median is not as sensitive to outliers as the mean, and so it can
give a more accurate representation of the central tendency of the data.
In other situations, such as when the features are of the boolean type or categorical
type, it is not necessary to impute missing values at all, as these types of features do
not have a numerical representation and do not lend themselves to statistical
calculations like mean and median.
If the features contain no outliers and no missing values, then it would not be necessary
to impute any values at all.
MACHINE LEARNING BASIC PART EXAM
A data scientist uses 3-fold cross-validation when optimizing model hyper parameters
for a regression problem.
The following root-mean-squared-error values are calculated on each of the validation
fold:
10.0
, 12.0
17.0
Which of the following values represents the overall cross-validation root-mean-squared
error?
A. 17.0
B. 12.0
C. 39.0
D. 10.0
E. 13.0 - answerE. 13.0
The overall cross-validation root-mean-squared error is the average of the root-mean-
squared errors calculated on each of the validation folds. Therefore, the correct answer
is E. 13.0.
To calculate this value, we first add the root-mean-squared errors from all three folds:
10.0 + 12.0 + 17.0 = 39.0. Then, we divide this sum by the number of folds to get the
average root-mean-squared error: 39. = 13.0. This is the correct answer, so E. 13.0
is the correct choice.
MACHINE LEARNING BASIC PART EXAM
A data scientist has replaced missing values in their feature set with each respective
feature variables median value.
A colleague suggests that the data scientist is throwing away valuable information by
doing this.
Which of the following approaches can they take to include as much information as
possible in the feature set?
A. Create a binary feature variable for each feature that contained missing values
indicating where each row's value has been imputed.
B. Refrain from imputing the missing values in favor of letting the machine learning
algorithm determine how to handle them.
C. Create a constant feature variable for each feature that contained missing values
indicating the percentage of row from the feature that was originally missing.
D. Impute the missing values using each respective feature variable's mean value
instead of the median value
E. Remove all feature variables that - answerA ??
B. Refrain from imputing the missing values in favor of letting the machine learning
algorithm determine how to handle them.
All the machine learning algorithms don't support missing values but some ML
algorithms are robust to missing values in the dataset.
The k-NN algorithm can ignore a column from a distance measure when a value is
missing. Naive Bayes can also support missing values when making a prediction.
These algorithms can be used when the dataset contains null or missing values.
MACHINE LEARNING BASIC PART EXAM
A data scientist wants to parallelize the training of trees in a gradient boosted tree to
speed up the training process. A colleague suggests that parallelizing a boosted tree
algorithm can be difficult.
Associate
MACHINE LEARNING BASIC PART EXAM
Which of the following evaluation metrics is not suitable to evaluate run in AutoML
experiment to regression problem?
A. F1
B. R-Squared
C. RMSE
D. MAE
E. MSE - answerA. F1
F1 is not a suitable evaluation metric for a regression problem. F1 is a metric that is
used to evaluate the performance of a classifier, and it is not appropriate to use in the
context of a regression problem.
R-squared, RMSE, MAE, and MSE are all suitable evaluation metrics for a regression
problem.
R-squared is a measure of the goodness of fit of a regression model, RMSE is a
measure of the average error of the model, MAE is a measure of the average absolute
error of the model, and MSE is a measure of the average squared error of the model.
MACHINE LEARNING BASIC PART EXAM
A health organization is developing a classification model to determine whether or not a
patient currently has a specific type of infection. The organization's leaders want to
maximize the number of cases identified by the model.
Which of the following classification metrics should be used to evaluate the model?
A. Accuracy
B. RMSE
C. Area under the residual operating curve
D. Precision
E. Recall - answerE. Recall
It is important to consider multiple evaluation metrics when evaluating a classification
model, rather than just focusing on one metric.
However, if the organization's leaders want to maximize the number of cases identified
by the model, they might be particularly interested in the recall of the model.
Recall measures the proportion of actual positive cases that were correctly identified by
the model. It is calculated as the number of true positives divided by the sum of the true
positives and false negatives. A model with high recall is able to identify a large
proportion of positive cases, even if it also has a large number of false positives.
Accuracy, RMSE, and the area under the residual operating curve are not appropriate
metrics for evaluating a classification model.
MACHINE LEARNING BASIC PART EXAM
,What is the name of the method that transforms categorical feature into a series of
binary indicator feature variables?
A. Leave-one-out encoding
B. String indexing
C. Target encoding
D. One-hot encoding
E. Categorical embeddings - answerD. One-hot encoding
The method that transforms categorical feature into a series of binary indicator feature
variables is called one-hot encoding.
One-hot encoding is a way to represent categorical variables as numerical data so that
it can be used in machine learning algorithms. It involves creating a new binary column
for each unique category in the categorical feature.
For example, if a categorical feature has three categories, A, B, and C, then three new
columns, one for each category, would be created. If a given data point belongs to
category A, then the value in the A column would be 1, and the values in the B and C
columns would be 0.
One-hot encoding allows the model to treat each category as a separate entity, rather
than as a numeric value that can be compared or ordered.
MACHINE LEARNING BASIC PART EXAM
In which of the following situations is it preferable to impute missing feature values with
their median value over the mean value?
A. When the features are of the boolean type
B. When the features contain no outliers
C. When the features are of the categorical type
D. When the features contain a lot of extreme outliers
E. When the features contain no missing values - answerD. When the features contain
a lot of extreme outliers
It is generally preferable to impute missing feature values with the median value rather
than the mean value in situations where the feature contains a lot of extreme outliers.
This is because the median is not as sensitive to outliers as the mean, and so it can
give a more accurate representation of the central tendency of the data.
In other situations, such as when the features are of the boolean type or categorical
type, it is not necessary to impute missing values at all, as these types of features do
not have a numerical representation and do not lend themselves to statistical
calculations like mean and median.
If the features contain no outliers and no missing values, then it would not be necessary
to impute any values at all.
MACHINE LEARNING BASIC PART EXAM
A data scientist uses 3-fold cross-validation when optimizing model hyper parameters
for a regression problem.
The following root-mean-squared-error values are calculated on each of the validation
fold:
10.0
, 12.0
17.0
Which of the following values represents the overall cross-validation root-mean-squared
error?
A. 17.0
B. 12.0
C. 39.0
D. 10.0
E. 13.0 - answerE. 13.0
The overall cross-validation root-mean-squared error is the average of the root-mean-
squared errors calculated on each of the validation folds. Therefore, the correct answer
is E. 13.0.
To calculate this value, we first add the root-mean-squared errors from all three folds:
10.0 + 12.0 + 17.0 = 39.0. Then, we divide this sum by the number of folds to get the
average root-mean-squared error: 39. = 13.0. This is the correct answer, so E. 13.0
is the correct choice.
MACHINE LEARNING BASIC PART EXAM
A data scientist has replaced missing values in their feature set with each respective
feature variables median value.
A colleague suggests that the data scientist is throwing away valuable information by
doing this.
Which of the following approaches can they take to include as much information as
possible in the feature set?
A. Create a binary feature variable for each feature that contained missing values
indicating where each row's value has been imputed.
B. Refrain from imputing the missing values in favor of letting the machine learning
algorithm determine how to handle them.
C. Create a constant feature variable for each feature that contained missing values
indicating the percentage of row from the feature that was originally missing.
D. Impute the missing values using each respective feature variable's mean value
instead of the median value
E. Remove all feature variables that - answerA ??
B. Refrain from imputing the missing values in favor of letting the machine learning
algorithm determine how to handle them.
All the machine learning algorithms don't support missing values but some ML
algorithms are robust to missing values in the dataset.
The k-NN algorithm can ignore a column from a distance measure when a value is
missing. Naive Bayes can also support missing values when making a prediction.
These algorithms can be used when the dataset contains null or missing values.
MACHINE LEARNING BASIC PART EXAM
A data scientist wants to parallelize the training of trees in a gradient boosted tree to
speed up the training process. A colleague suggests that parallelizing a boosted tree
algorithm can be difficult.
