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Databricks Machine Learning Associate Databricks Certified Machine Learning Associate Exam exam dumps questions are the best material for you to test all the related Databricks exam topics. By using the Databricks Machine Learning Associate exam dumps questions and practicing your skills, you can increase your confidence and chances of passing the Databricks Machine Learning Associate exam. Features of Dumpsinfo’s products Instant Download Free Update in 3 Months Money back guarantee PDF and Software 24/7 Customer Support Besides, Dumpsinfo also provides unlimited access. You can get all Dumpsinfo files at lowest price. Databricks Certified Machine Learning Associate Exam Databricks Machine Learning Associate exam free dumps questions are available below for you to study. Full version: Databricks Machine Learning Associate Exam Dumps Questions 1.A machine learning engineer is using the following code block to scale the inference of a single- node model on a Spark DataFrame with one million records: 1 / 8 https://www.dumpsinfo.com/unlimited-access/ https://www.dumpsinfo.com/exam/databricks-machine-learning-associate Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator? A. The data will be limited to a single executor preventing the model from being loaded multiple times B. The model will be limited to a single executor preventing the data from being distributed C. The model only needs to be loaded once per executor rather than once per batch during the inference process D. The data will be distributed across multiple executors during the inference process Answer: C Explanation: Using an iterator in the pandas_udf ensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process. Reference: Databricks documentation on pandas UDFs: Pandas UDFs 2.A team is developing guidelines on when to use various evaluation metrics for classification problems. The team needs to provide input on when to use the F1 score over accuracy. Which of the following suggestions should the team include in their guidelines? A. The F1 score should be utilized over accuracy when the number of actual positive cases is identical to the number of actual negative cases. B. The F1 score should be utilized over accuracy when there are greater than two classes in the target variable. C. The F1 score should be utilized over accuracy when there is significant imbalance between positive and negative classes and avoiding false negatives is a priority. D. The F1 score should be utilized over accuracy when identifying true positives and true negatives are equally important to the business problem. Answer: C Explanation: The F1 score is the harmonic mean of precision and recall and is particularly useful in situations where there is a significant imbalance between positive and negative classes. When there is a class imbalance, accuracy can be misleading because a model can achieve high accuracy by simply predicting the majority class. The F1 score, however, provides a better measure of the test's accuracy in terms of both false positives and false negatives. Specifically, the F1 score should be used over accuracy when: 2 / 8 https://www.dumpsinfo.com/ There is a significant imbalance between positive and negative classes. Avoiding false negatives is a priority, meaning recall (the ability to detect all positive instances) is crucial. In this scenario, the F1 score balances both precision (the ability to avoid false positives) and recall, providing a more meaningful measure of a model’s performance under these conditions. Reference: Databricks documentation on classification metrics: Classification Metrics 3.A machine learning engineer wants to parallelize the training of group-specific models using the Pandas Function API. They have developed the train_model function, and they want to apply it to each group of DataFrame df. They have written the following incomplete code block: Which of the following pieces of code can be used to fill in the above blank to complete the task? A. applyInPandas B. mapInPandas C. predict D. train_model E. groupedApplyIn Answer: B Explanation: The function mapInPandas in the PySpark DataFrame API allows for applying a function to each partition of the DataFrame. When working with grouped data, groupby followed by applyInPandas is the correct approach to apply a function to each group as a separate Pandas DataFrame. However, if the function should apply across each partition of the grouped data rather than on each individual group, mapInPandas would be utilized. Since the code snippet indicates the use of groupby, the intent seems to be to apply train_model on each group specifically, which aligns with applyInPandas. Thus, applyInPandas is a better fit to ensure that each group generated by groupby is processed through the train_model function, preserving the partitioning and grouping integrity. Reference PySpark Documentation on applying functions to grouped data: https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.GroupedData.applyInPa ndas.html 4.Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames? A. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata B. pandas API on Spark DataFrames are more performant than Spark DataFrames C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata D. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames Answer: C 3 / 8 https://www.dumpsinfo.com/ Explanation: The pandas API on Spark DataFrames are made up of Spark DataFrames with additional metadata. The pandas API on Spark aims to provide the pandas-like experience with the scalability and distributed nature of Spark. It allows users to work with pandas functions on large datasets by leveraging Spark’s underlying capabilities. Reference: Databricks documentation on pandas API on Spark: pandas API on Spark 5.A data scientist has developed a random forest regressor rfr and included it as the final stage in a Spark MLPipeline pipeline. They then set up a cross-validation process with pipeline as the estimator in the following code block: Which of the following is a negative consequence of including pipeline as the estimator in the cross- validation process rather than rfr as the estimator? A. The process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode B. The process will leak data from the training set to the test set during the evaluation phase C. The process will be unable to parallelize tuning due to the distributed nature of pipeline D. The process will leak data prep information from the validation sets to the training sets for each model Answer: A Explanation: Including the entire pipeline as the estimator in the cross-validation process means that all stages of the pipeline, including data preprocessing steps like string indexing and vector assembling, will be refit or retransformed for each fold of the cross-validation. This results in a longer runtime because each fold requires re-execution of these preprocessing steps, which can be computationally expensive. If only the random forest regressor (rfr) were included as the estimator, the preprocessing steps would be performed once, and only the model fitting would be repeated for each fold, significantly reducing the computational overhead. Reference: Databricks documentation on cross-validation: Cross Validation 6.A data scientistis using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML. 4 / 8 https://www.dumpsinfo.com/ Which of the following compute tools is best suited for this use case? A. Single Node cluster B. Standard cluster C. SQL Warehouse D. None of these compute tools support this task Answer: B Explanation: For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations. Reference: Databricks documentation on clusters: Clusters in Databricks 7.Which of the following tools can be used to parallelize the hyperparameter tuning process for single- node machine learning models using a Spark cluster? A. MLflow Experiment Tracking B. Spark ML C. Autoscaling clusters D. Autoscaling clusters E. Delta Lake Answer: B Explanation: Spark ML (part of Apache Spark's MLlib) is designed to handle machine learning tasks across multiple nodes in a cluster, effectively parallelizing tasks like hyperparameter tuning. It supports various machine learning algorithms that can be optimized over a Spark cluster, making it suitable for parallelizing hyperparameter tuning for single-node machine learning models when they are adapted to run on Spark. Reference Apache Spark MLlib Guide: https://spark.apache.org/docs/latest/ml-guide.html Spark ML is a library within Apache Spark designed for scalable machine learning. It provides tools to handle large-scale machine learning tasks, including parallelizing the hyperparameter tuning process for single-node machine learning models using a Spark cluster. Here’s a detailed explanation of how Spark ML can be used: Hyperparameter Tuning with CrossValidator: Spark ML includes the CrossValidator and TrainValidationSplit classes, which are used for hyperparameter tuning. These classes can evaluate multiple sets of hyperparameters in parallel using a Spark cluster. from pyspark.ml.tuning import CrossValidator, ParamGridBuilder from pyspark.ml.evaluation import BinaryClassificationEvaluator # Define the model model = ... # Create a parameter grid paramGrid = ParamGridBuilder() \ .addGrid(model.hyperparam1, [value1, value2]) \ .addGrid(model.hyperparam2, [value3, value4]) \ .build() # Define the evaluator evaluator = BinaryClassificationEvaluator() # Define the CrossValidator crossval = CrossValidator(estimator=model, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=3) Parallel Execution: Spark distributes the tasks of training models with different hyperparameters 5 / 8 https://www.dumpsinfo.com/ across the cluster’s nodes. Each node processes a subset of the parameter grid, which allows multiple models to be trained simultaneously. Scalability: Spark ML leverages the distributed computing capabilities of Spark. This allows for efficient processing of large datasets and training of models across many nodes, which speeds up the hyperparameter tuning process significantly compared to single-node computations. Reference Apache Spark MLlib Documentation Hyperparameter Tuning in Spark ML 8.A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning. Which of the following steps will the data scientist need to perform outside of their AutoML experiment? A. Model tuning B. Model evaluation C. Model deployment D. Exploratory data analysis Answer: D Explanation: AutoML platforms, such as the one available in Databricks Machine Learning, streamline various stages of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and model evaluation. However, exploratory data analysis (EDA) is typically performed outside the AutoML process. EDA involves understanding the dataset, visualizing distributions, identifying anomalies, and gaining insights into data before feeding it into a machine learning pipeline. This step is crucial for ensuring that the data is clean and suitable for model training but is generally done manually by the data scientist. Reference Databricks documentation on AutoML: https://docs.databricks.com/applications/machine- learning/automl.html 9.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 10.A data scientist is using the following code block to tune hyperparameters for a machine learning model: 6 / 8 https://www.dumpsinfo.com/ Which change can they make the above code block to improve the likelihood of a more accurate model? A. Increase num_evals to 100 B. Change fmin() to fmax() C. Change sparkTrials() to Trials() D. Change tpe.suggest to random.suggest Answer: A Explanation: To improve the likelihood of a more accurate model, the data scientist can increase num_evals to 100. Increasing the number of evaluations allows the hyperparameter tuning process to explore a larger search space and evaluate more combinations of hyperparameters, which increases the chance of finding a more optimal set of hyperparameters for the model. Reference: Databricks documentation on hyperparameter tuning: Hyperparameter Tuning 11.Which of the following approaches can be used to view the notebook that was run to create an MLflow run? A. Open the MLmodel artifact in the MLflow run paqe B. Click the "Models" link in the row corresponding to the run in the MLflow experiment paqe C. Click the "Source" link in the row corresponding to the run in the MLflow experiment page D. Click the "Start Time" link in the row corresponding to the run in the MLflow experiment page Answer: C Explanation: To view the notebook that was run to create an MLflow run, you can click the "Source" link in the row corresponding to the run in the MLflow experiment page. The "Source" link provides a direct reference to the source notebook or script that initiated the run, allowing you to review the code and methodology used in the experiment. This feature is particularly useful for reproducibility and for understanding the context of the experiment. Reference: MLflow Documentation (Viewing Run Sources and Notebooks). 7 / 8 https://www.dumpsinfo.com/ 12.A data scientist is working with a feature set with the following schema: The customer_id column is the primary key in the feature set. Each of the columns in the feature set has missing values. They want to replace the missing values by imputing a common value for each feature. Which of the following lists all of the columns in the feature set that need to be imputed using the most common value of the column? A. customer_id, loyalty_tier B. loyalty_tier C. units D. spend E. customer_id Answer: B Explanation: For the feature set schema provided, the columns that need to be imputed using the most common value (mode) are typically the categorical columns. In this case, loyalty_tier is the only categorical columnthat should be imputed using the most common value. customer_id is a unique identifier and should not be imputed, while spend and units are numerical columns that should typically be imputed using the mean or median values, not the mode. Reference: Databricks documentation on missing value imputation: Handling Missing Data If you need any further clarification or additional questions answered, please let me know! Powered by TCPDF (www.tcpdf.org) 8 / 8 https://www.dumpsinfo.com/ http://www.tcpdf.org
