Databricks Databricks-Machine-Learning-Professional Exam Dumps

Databricks Databricks-Machine-Learning-Professional Sample Question Answers

Databricks Databricks-Machine-Learning-Professional Sample Questions

Question # 1

Which of the following machine learning algorithms typically uses bagging?

A. IGradient boosted trees 
B. K-means 
C. Random forest 
D. Decision tree

Answer: Answer:C

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Question # 2

The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables. Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

A. Logistic regression 
B. Singular value decomposition 
C. Iterative optimization 
D. Least-squares method

Answer: Answer:C

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Question # 3

A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference. In which situation will the machine learning engineer be correct? 

A. When the new solution requires if-else logic determining which model to use to compute each prediction 
B. When the new solution's models have an average latency that is larger than the size of the original model 
C. When the new solution requires the use of fewer feature variables than the original model 
D. When the new solution requires that each model computes a prediction for every record E. When the new solution's models have an average size that is larger than the size of the original model 

Answer: Answer:D

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Question # 4

A data scientist has developed a machine learning pipeline with a static input data set using Spark ML, but the pipeline is taking too long to process. They increase the number of workers in the cluster to get the pipeline to run more efficiently. They notice that the number of rows in the training set after reconfiguring the cluster is different from the number of rows in the training set prior to reconfiguring the cluster. Which of the following approaches will guarantee a reproducible training and test set for each model?  

A. Manually configure the cluster
B. Write out the split data sets to persistent storage 
C. Set a speed in the data splitting operation 
D. Manually partition the input data

Answer: Answer:B

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Question # 5

A data scientist is developing a single-node machine learning model. They have a large number of model configurations to test as a part of their experiment. As a result, the model tuning process takes too long to complete. Which of the following approaches can be used to speed up the model tuning process?

A. Implement MLflow Experiment Tracking 
B. Scale up with Spark ML 
C. Enable autoscaling clusters 
D. Parallelize with Hyperopt 

Answer: Answer:D

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Question # 6

A machine learning engineer is trying to scale a machine learning pipeline by distributing its singlenode model tuning process. After broadcasting the entire training data onto each core, each core in the cluster can train one model at a time. Because the tuning process is still running slowly, the engineer wants to increase the level of parallelism from 4 cores to 8 cores to speed up the tuning process. Unfortunately, the total memory in the cluster cannot be increased. In which of the following scenarios will increasing the level of parallelism from 4 to 8 speed up the tuning process? 

A. When the tuning process in randomized  
B. When the entire data can fit on each core 
C. When the model is unable to be parallelized 
D. When the data is particularly long in shape E. When the data is particularly wide in shape

Answer: Answer:B

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Question # 7

A machine learning engineer has identified the best run from an MLflow Experiment. They havestored the run ID in the run_id variable and identified the logged model name as "model". They nowwant to register that model in the MLflow Model Registry with the name "best_model".Which lines of code can they use to register the model associated with run_id to the MLflow ModelRegistry?

A. mlflow.register_model(run_id, "best_model")
B. mlflow.register_model(f"runs:/{run_id}/model , "best_model )
C. millow.register_model(f"runs:/{run_id)/model")
D. mlflow.register_model(f"runs:/{run_id}/best_model", "model")

Answer: B

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Question # 8

A machine learning engineer has been notified that a new Staging version of a model registered tothe MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants toput this model into production by transitioning it to the Production stage in the Model Registry.From which of the following pages in Databricks Machine Learning can the machine learningengineer accomplish this task?

A. The home page of the MLflow Model Registry
B. The experiment page in the Experiments observatory
C. The model version page in the MLflow Model Registry
D. The model page in the MLflow Model Registry

Answer: C

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Question # 9

A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API. Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?

A. import pyspark.pandas as ps df = ps.DataFrame(spark_df)
B. import pyspark.pandas as psdf = ps.to_pandas(spark_df)
C. spark_df.to_pandas() 
D. import pandas as pd df = pd.DataFrame(spark_df

Answer: Answer:A

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Question # 10

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 
C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata 

Answer: Answer:C

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Question # 11

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: Answer:A

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Question # 12

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: Answer:D

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Question # 13

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: Answer:B

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Question # 14

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: Answer:C

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Question # 15

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: Answer:E

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