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Databricks Databricks-Machine-Learning-Professional Exam Questions

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Databricks Certified Machine Learning Professional

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Databricks Databricks-Machine-Learning-Professional Sample Questions – Free Practice Test & Real Exam Prep

Question #1

Which statement describes a Spark ML transformer? 

  • A. A transformer is an algorithm which can transform one DataFrame into another DataFrame 
  • B. A transformer is a hyperparameter grid that can be used to train a model 
  • C. A transformer chains multiple algorithms together to transform an ML workflow 
  • D. A transformer is a learning algorithm that can use a DataFrame to train a model
Answer: A
Explanation: In Spark ML, a transformer is an algorithm that can transform one DataFrame into another DataFrame. It takes a DataFrame as input and produces a new DataFrame as output. This transformation can involve adding new columns, modifying existing ones, or applying feature transformations. Examples of transformers in Spark MLlib include feature transformers like StringIndexer, VectorAssembler, and StandardScaler. Reference: Databricks documentation on transformers: Transformers in Spark ML 
Question #2

Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines? 

  • A. Keras 
  • B. Scikit-learn 
  • C. PyTorch 
  • D. Spark ML 
Answer: D
Explanation: Spark MLlib is a machine learning library within Apache Spark that provides scalable and distributed machine learning algorithms. It is designed to work with Spark DataFrames and leverages Sparks distributed computing capabilities to perform large-scale feature engineering and model training without the need for user-defined functions (UDFs) or the pandas Function API. Spark MLlib provides built-in transformations and algorithms that can be applied directly to large datasets. Reference: Databricks documentation on Spark MLlib: Spark MLlib 
Question #3

A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases. Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?

  • A. PySpark DataFrame API 
  • B. pandas API on Spark 
  • C. Spark SQL 
  • D. Feature Store 
Answer: B
Explanation: The pandas API on Spark provides a way to scale pandas operations to big data while minimizing the need for refactoring existing pandas code. It allows users to run pandas operations on Spark DataFrames, leveraging Sparks distributed computing capabilities to handle large datasets more efficiently. This approach requires minimal changes to the existing code, making it a convenient option for scaling pandas-based feature engineering notebooks. Reference: Databricks documentation on pandas API on Spark: pandas API on Spark 
Question #4

Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?

  • A. Random Search 
  • B. Halving Random Search 
  • C. Tree of Parzen Estimators 
  • D. Grid Search 
Answer: C
Explanation: Tree of Parzen Estimators (TPE) is a sequential model-based optimization algorithm that selects hyperparameter values based on the outcomes of previous trials. It models the probability density of good and bad hyperparameter values and makes informed decisions about which hyperparameters to try next. This approach contrasts with methods like random search and grid search, which do not use information from previous trials to guide the search process. Reference: Hyperopt and TPE 
Question #5

A data scientist learned during their training to always use 5-fold cross-validation in their model development workflow. A colleague suggests that there are cases where a train-validation split could be preferred over k-fold cross-validation when k > 2. Which of the following describes a potential benefit of using a train-validation split over k-fold crossvalidation in this scenario?

  • A. A holdout set is not necessary when using a train-validation split 
  • B. Reproducibility is achievable when using a train-validation split 
  • C. Fewer hyperparameter values need to be tested when using a train-validation split 
  • D. Bias is avoidable when using a train-validation split 
  • E. Fewer models need to be trained when using a train-validation split 
Answer: E
Explanation: A train-validation split is often preferred over k-fold cross-validation (with k > 2) when computational efficiency is a concern. With a train-validation split, only two models (one on the training set and one on the validation set) are trained, whereas k-fold cross-validation requires training k models (one for each fold). This reduction in the number of models trained can save significant computational resources and time, especially when dealing with large datasets or complex models. Reference: Model Evaluation with Train-Test Split
Question #6

A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process. Which change could the data scientist make to improve their model accuracy over the course of their tuning process?

  • A. Change the number of compute nodes to be half or less than half of the number of evaluations. 
  • B. Change the number of compute nodes and the number of evaluations to be much larger but equal.
  • C. Change the iterative optimization algorithm used to facilitate the tuning process. 
  • D. Change the number of compute nodes to be double or more than double the number of evaluations
Answer: C
Explanation: The lack of improvement in model accuracy across evaluations suggests that the optimization algorithm might not be effectively exploring the hyperparameter space. Iterative optimization algorithms like Tree-structured Parzen Estimators (TPE) or Bayesian Optimization can adapt based on previous evaluations, guiding the search towards more promising regions of the hyperparameter space. Changing the optimization algorithm can lead to better utilization of the information gathered during each evaluation, potentially improving the overall accuracy. Reference: Hyperparameter Optimization with Hyperopt 
Question #7

A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame that contains only the rows from spark_df where the value in column discount is less than or equal 0. Which of the following code blocks will accomplish this task?

  • A. spark_df.loc[:,spark_df["discount"] <= 0] 
  • B. spark_df[spark_df["discount"] <= 0] 
  • C. spark_df.filter (col("discount") <= 0) 
  • D. spark_df.loc(spark_df["discount"] <= 0, :] 
Answer: C
Explanation: To filter rows in a Spark DataFrame based on a condition, the filter method is used. In this case, the condition is that the value in the "discount" column should be less than or equal to 0. The correct syntax uses the filter method along with the col function from pyspark.sql.functions. Correct code: from pyspark.sql.functions import col filtered_df = spark_df.filter(col("discount") <= 0) Option A and D use Pandas syntax, which is not applicable in PySpark. Option B is closer but misses the use of the col function. Reference: PySpark SQL Documentation 
Question #8

A data scientist has created a linear regression model that uses log(price) as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFrame preds_df. They are using the following code block to evaluate the model: regression_evaluator.setMetricName("rmse").evaluate(preds_df) Which of the following changes should the data scientist make to evaluate the RMSE in a way that is comparable with price?

  • A. They should exponentiate the computed RMSE value 
  • B. They should take the log of the predictions before computing the RMSE 
  • C. They should evaluate the MSE of the log predictions to compute the RMSE 
  • D. They should exponentiate the predictions before computing the RMSE 
Answer: D
Explanation: When evaluating the RMSE for a model that predicts log-transformed prices, the predictions need to be transformed back to the original scale to obtain an RMSE that is comparable with the actual price values. This is done by exponentiating the predictions before computing the RMSE. The RMSE should be computed on the same scale as the original data to provide a meaningful measure of error. Reference: Databricks documentation on regression evaluation: Regression Evaluation 
Question #9

An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be onehot encoded within the feature repository. Which of the following explanations justifies this suggestion?

  • A. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
  • B. One-hot encoding is dependent on the target variables values which differ for each apaplication. 
  • C. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems
  • D. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
Answer: A
Explanation: The suggestion not to one-hot encode categorical feature variables within the feature repository is justified because one-hot encoding can be problematic for some machine learning algorithms. Specifically, one-hot encoding increases the dimensionality of the data, which can be computationally expensive and may lead to issues such as multicollinearity and overfitting. Additionally, some algorithms, such as tree-based methods, can handle categorical variables directly without requiring one-hot encoding. Reference: Databricks documentation on feature engineering: Feature Engineering 
Question #10

A machine learning engineer has grown tired of needing to install the MLflow Python library on eachof their clusters. They ask a senior machine learning engineer how their notebooks can load theMLflow library without installing it each time. The senior machine learning engineer suggests thatthey use Databricks Runtime for Machine Learning.Which of the following approaches describes how the machine learning engineer can begin usingDatabricks Runtime for Machine Learning?

  • A. They can add a line enabling Databricks Runtime ML in their init script when creating theirclusters.
  • B. They can check the Databricks Runtime ML box when creating their clusters.
  • C. They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdownwhen creating their clusters.
  • D. They can set the runtime-version variable in their Spark session to œml .
Answer: C
Explanation:
The Databricks Runtime for Machine Learning includes pre-installed packages and libraries essential
for machine learning and deep learning, including MLflow. To use it, the machine learning engineer
can simply select an appropriate Databricks Runtime ML version from the "Databricks Runtime
Version" dropdown menu while creating their cluster. This selection ensures that all necessary
machine learning libraries, including MLflow, are pre-installed and ready for use, avoiding the need
to manually install them each time.
Reference
Databricks documentation on creating clusters: https://docs.databricks.com/clusters/create.html

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