Databricks Machine Learning Associate Exam Dumps 2026
Databricks Machine Learning Associate Exam Dumps 2026
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Question 1:
Why is it recommended to include an additional field specifying that a feature was imputed if any imputation techniques are applied?
A. It does not impact the model‘s performance.
B. It helps decrease the number of null values.
C. It is a good practice for transparency and interpretation of the model.
D. It is not necessary to include such a field.
Answer: C
Explanation:
Including an additional field specifying that a feature was imputed is a recommended practice for several reasons related to transparency and interpretation of the model, not necessarily its performance:
Transparency: Documenting imputation clearly indicates that data manipulation has occurred. This is crucial for understanding the potential impact of imputation on the data and the model‘s results.
Interpretation: Knowing which features were imputed helps you interpret the model‘s behavior. For instance, a feature with a high imputation rate might be less reliable or informative compared to features with complete data.
Reproducibility: Having a clear record of imputation steps allows you or others to reproduce your analysis and understand the data preparation process.
While imputation itself might not directly affect model performance, understanding how missing data was handled is essential for proper model interpretation and future improvements.
Here‘s why the other options aren‘t the primary reasons for including an imputation flag:
It does not impact the model‘s performance: While imputation can affect the distribution of your data and potentially the model‘s results, the decision to include an imputation flag is more about understanding those potential effects than directly optimizing performance.
It helps decrease the number of null values: Imputation does address missing values, but the flag serves a different purpose. It documents that imputation occurred, not simply that null values are no longer present.
It is not necessary to include such a field: Technically, it might not be strictly mandatory in every situation. However, it‘s a recommended best practice for the reasons mentioned above regarding transparency, interpretation, and reproducibility.
Question 2:
How can null values be handled in numeric features?
A. Replace them with a special category.
B. Drop any records containing nulls.
C. Use techniques like ALS for imputation.
D. Replace them with the mode.
Answer: C
Explanation:
There are several ways to handle null values in numeric features, and the best approach depends on the specific situation. Here‘s a breakdown of the options you mentioned:
Replace them with a special category: This approach can be useful if you have a meaningful way to represent missing values as a specific category within your data. However, it‘s important to consider how this category will be interpreted by subsequent analysis techniques.
Drop any records containing nulls: This is a simple solution, but it can lead to data loss, especially if you have a high percentage of missing values. Dropping data can introduce bias and affect the generalizability of your results.
Use techniques like ALS for imputation: Correct! Imputation techniques like Alternating Least Squares (ALS) can be used to estimate missing values based on other features in the data. This approach can be effective, but it‘s important to choose the appropriate imputation method and be aware of potential assumptions made during the process.
Replace them with the mode: This can be a reasonable option for some datasets, especially if the missing values are randomly distributed and the mode is a representative value. However, it‘s important to consider if using the most frequent value might introduce bias into your analysis.
Here are some additional techniques to consider:
Mean/Median imputation: Replacing missing values with the mean or median of the existing data can be a straightforward approach, but it can be problematic if the data has outliers.
Random sampling: Imputing missing values with random samples from the existing data can be an option, but it can introduce noise into your data.
Ultimately, the best way to handle null values in numeric features depends on the characteristics of your data, the assumptions of your analysis, and the potential impact on your results. It‘s important to carefully evaluate your options and choose the most appropriate strategy for your specific situation.
Question 3:
When using cross-validation in machine learning pipelines, where should the pipeline and cross-validator be placed based on the presence of estimators or transformers?
A. Always put the cross-validator inside the pipeline.
B. If the pipeline includes estimators, put the entire pipeline inside the cross-validator.
C. If there is concern about data leakage, put the pipeline inside the cross-validator.
D. Always put the pipeline inside the cross-validator.
Answer: B
Explanation:
It depends if there are estimators or transformers in the pipeline. If you have things like StringIndexer (an estimator) in the pipeline, then you have to refit it every time if you put the entire pipeline in the cross-validator. However, if there is any concern about data leakage from the earlier steps, the safest thing is to put the pipeline inside the CV, not the other way. CV first splits the data and then .fit() the pipeline. If it is placed at the end of the pipeline, we potentially can leak the info from the hold-out set to the train set.
Question 4:
What is the role of tpe.suggest in Hyperopt‘s Tree of Parzen Estimators (TPE) algorithm?
A. It evaluates the objective function.
B. It specifies the external experiment for MLflow integration.
C. It suggests new hyperparameter configurations based on previous results.
D. It parallelizes the hyperparameter tuning process.
Answer: C
Explanation:
In Hyperopt‘s Tree of Parzen Estimators (TPE) algorithm, tpe.suggest plays a crucial role in suggesting new hyperparameter configurations to explore.
Suggests new hyperparameter configurations: TPE is a sequential hyperparameter optimization algorithm. Based on the performance of previously evaluated configurations (objective function values), tpe.suggest recommends new sets of hyperparameter values to try. It aims to identify promising areas of the hyperparameter space that might lead to better results.
Here‘s how the other options differ:
Evaluates the objective function: This is the responsibility of the user-defined objective function, which Hyperopt calls to assess the performance of each hyperparameter configuration. tpe.suggest doesn‘t directly evaluate the objective function.
Specifies the external experiment for MLflow integration: While Hyperopt can integrate with MLflow for experiment tracking, tpe.suggest itself doesn‘t handle that configuration. It‘s focused on suggesting hyperparameters, not managing external experiment details.
Parallelizes the hyperparameter tuning process: Parallelization can be achieved with additional functionalities in Hyperopt, but tpe.suggest operates within a sequential TPE algorithm. It doesn‘t directly handle parallelization.
Question 5:
In what scenarios is the Train-Test Split considered a more efficient alternative to Cross-Validation for model evaluation?
A. When a high level of model interpretability is required.
B. When computation time and resources are limited, especially for large datasets or complex models.
C. When the training algorithm is highly parallelizable.
D. When computational resources are unlimited.
Answer: B
Explanation:
Train Test Split is better than Cross-Validation when computation time and resources are limited. Cross-validation requires the training algorithm to be rerun k times, which means it takes k times as much computation to make an evaluation. This can be expensive and time-consuming, especially for large datasets or complex models. In such cases, the Train Test Split can be a more efficient alternative.
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SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
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Question 6:
Which of the following techniques can be employed to mitigate overfitting in a machine-learning model?
A. Feature engineering and dimensionality reduction.
B. Increasing the complexity of the model.
C. Utilizing only one specific technique, such as Data augmentation.
D. All of them
Answer: A
Explanation:
Feature engineering and dimensionality reduction are the techniques that can be employed to mitigate overfitting in a machine-learning model.
Here’s a breakdown of why:
Feature engineering: This involves creating new features from existing ones that are more informative and relevant to the task. This can help the model generalize better to new data.
Dimensionality reduction: This involves reducing the number of features in the dataset while preserving the most important information. This can help prevent the model from overfitting to noise in the data.
Increasing the complexity of the model can actually make it more prone to overfitting. This is because a more complex model has more parameters, which can make it easier for the model to fit the training data too closely and fail to generalize well to new data.
Utilizing only one specific technique, such as data augmentation, may not be sufficient to address overfitting. Data augmentation can help increase the diversity of the training data, but it may not be enough to prevent the model from overfitting if the underlying model is too complex or if there are other issues with the data.
Therefore, the most effective way to mitigate overfitting is to combine feature engineering and dimensionality reduction with other techniques, such as regularization or early stopping.
Question 7:
What is a primary advantage of using Hyperopt for hyperparameter optimization compared to manual tuning or grid search?
A. Hyperopt efficiently finds optimal hyperparameter combinations, exploring the search space more effectively with advanced algorithms like TPE search.
B. Manual tuning and grid search are faster than Hyperopt.
C. Manual tuning and grid search are more reliable in finding optimal hyperparameters.
D. Hyperopt can only find hyperparameter combinations with more evaluations.
Answer: A
Explanation:
The main advantage of using Hyperopt for hyperparameter optimization over manual tuning or grid search is that Hyperopt can find optimal hyperparameter combinations more efficiently. By using advanced search algorithms like TPE search, Hyperopt can explore the search space more effectively and often find better hyperparameter combinations with fewer evaluations than manual tuning or grid search.
Question 8:
When might you use StringIndexer in a machine-learning pipeline?
A. To handle missing values in a dataset.
B. To convert numerical data into categorical variables.
C. When identifying a column as a categorical variable or converting textual data to numeric data while preserving categorical context.
D. StringIndexer is exclusively used for text processing and not applicable in machine learning scenarios.
Answer: C
Explanation:
StringIndexer is used when you want the machine learning algorithm to identify a column as a categorical variable or when you want to convert textual data to numeric data while preserving the categorical context (e.g., converting days of the week to numeric representation).
Question 9:
In what scenarios is the F1 score considered a more useful metric than accuracy, and why?
A. When classes are balanced precision is the primary concern.
B. When classes are imbalanced, there is a need to reduce false positives.
C. When accuracy is the sole focus, regardless of class distribution.
D. When classes are imbalanced, and there is a need to reduce false negatives.
Answer: D
Explanation:
The F1 score is particularly valuable when dealing with imbalanced classes and when there is a significant cost associated with false negatives. It strikes a balance between precision and recall, making it suitable for scenarios where both false positives and false negatives need to be considered, such as in medical diagnosis or fraud detection. Accuracy, on the other hand, may not be a reliable metric in imbalanced datasets, as it can be dominated by the majority class.
Question 10:
What is a significant advantage of using a training-validation split over k-fold cross-validation in certain situations?
A. It requires testing fewer models, making it advantageous when training time or computational resources are limited.
B. It reduces the number of hyperparameter values that need testing.
C. It guarantees the reproducibility of results.
D. It ensures bias-free model training.
Answer: A
Explanation:
When using a training-validation split as opposed to k-fold cross-validation, fewer models need to be trained. This can be a significant advantage in situations where training time is a factor or computational resources are limited. The other options do not necessarily hold.
Bias may still be present with a train-validation split, reproducibility depends on how the split is done and is not inherently linked to train-validation splits, the number of hyperparameter values needing testing does not depend on the type of cross-validation used, and a holdout set may still be useful for final model evaluation even when using a train-validation split.
For a full set of 350 questions. Go to
https://skillcertpro.com/product/databricks-machine-learning-associate-practice-tests/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.