LATEST RELEASED DATABRICKS-MACHINE-LEARNING-ASSOCIATE TEST CENTRES - DATABRICKS ANSWERS DATABRICKS CERTIFIED MACHINE LEARNING ASSOCIATE EXAM FREE

Latest Released Databricks-Machine-Learning-Associate Test Centres - Databricks Answers Databricks Certified Machine Learning Associate Exam Free

Latest Released Databricks-Machine-Learning-Associate Test Centres - Databricks Answers Databricks Certified Machine Learning Associate Exam Free

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
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 cross-validation in this scenario?

  • A. Reproducibility is achievable when using a train-validation split
  • B. Fewer models need to be trained 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. A holdout set is not necessary when using a train-validation split

Answer: B

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


NEW QUESTION # 53
A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model.
The Spark DataFrame train_df has the following schema:

The machine learning engineer shares the following code block:

Which of the following changes does the machine learning engineer need to make to complete the task?

  • A. They need to utilize a Pipeline to fit the model
  • B. They need to split the features column out into one column for each feature
  • C. They need to convert the features column to be a vector
  • D. They do not need to make any changes
  • E. They need to call the transform method on train df

Answer: C

Explanation:
In Spark ML, the linear regression model expects the feature column to be a vector type. However, if the features column in the DataFrame train_df is not already in this format (such as being a column of type UDT or a non-vectorized type), the engineer needs to convert it to a vector column using a transformer like VectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column.
Reference
Spark MLlib documentation for LinearRegression: https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression


NEW QUESTION # 54
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 leak data prep information from the validation sets to the training sets for each model
  • B. The process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode
  • C. The process will be unable to parallelize tuning due to the distributed nature of pipeline
  • D. The process will leak data from the training set to the test set during the evaluation phase

Answer: B

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


NEW QUESTION # 55
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. Autoscaling clusters
  • B. Spark ML
  • C. Autoscaling clusters
  • D. MLflow Experiment Tracking
  • 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 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


NEW QUESTION # 56
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. spark_df.to_sql()
  • B. import pyspark.pandas as ps
    df = ps.DataFrame(spark_df)
  • C. import pyspark.pandas as ps
    df = ps.to_pandas(spark_df)
  • D. import pandas as pd
    df = pd.DataFrame(spark_df)
  • E. spark_df.to_pandas()

Answer: B

Explanation:
To use the pandas API on Spark, which is designed to bridge the gap between the simplicity of pandas and the scalability of Spark, the correct approach involves importing the pyspark.pandas (recently renamed to pandas_api_on_spark) module and converting a Spark DataFrame to a pandas-on-Spark DataFrame using this API. The provided syntax correctly initializes a pandas-on-Spark DataFrame, allowing the data scientist to work with the familiar pandas-like API on large datasets managed by Spark.
Reference
Pandas API on Spark Documentation: https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html


NEW QUESTION # 57
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