Important Databricks Machine Learning Associate Exam Questions

Answer: E

Explanation:

The data scientist can refactor their notebook to utilize the pandas API on Spark (now known as pandas on Spark, formerly Koalas). This allows for the least amount of changes to the existing pandas-based code while scaling to handle big data using Spark's distributed computing capabilities. pandas on Spark provides a similar API to pandas, making the transition smoother and faster compared to completely rewriting the code to use PySpark DataFrame API, Scala Dataset API, or Spark SQL. Reference:

Databricks documentation on pandas API on Spark (formerly Koalas).

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.

PySpark SQL Documentation

Answer: E

Explanation:

When the goal is to maximize the identification of positive cases in a classification task, the metric of interest is Recall. Recall, also known as sensitivity, measures the proportion of actual positives that are correctly identified by the model (i.e., the true positive rate). It is crucial for scenarios where missing a positive case (false negative) has serious implications, such as in medical diagnostics. The other metrics like Precision, RMSE, and Accuracy serve different aspects of performance measurement and are not specifically focused on maximizing the detection of positive cases alone. Reference:

Classification Metrics in Machine Learning (Understanding Recall).

Answer: C

Explanation:

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging involves training multiple models independently on different random subsets of the data and then combining their predictions. Random Forests consist of many decision trees trained on random subsets of the training data and features, and their predictions are averaged to improve accuracy and control overfitting. This method enhances model robustness and predictive performance. Reference:

Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).

Answer: E

Explanation:

One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called 'quantile binning' to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees. Reference:

Spark MLlib Documentation (Decision Trees and Quantile Binning).