Question # 1
| A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run.
Which of the following approaches can the team use to identify which task is the cause of the failure? | | A. Run each notebook interactively | | B. Review the matrix view in the Job's runs | | C. Migrate the Job to a Delta Live Tables pipeline | | D. Change each Task’s setting to use a dedicated cluster |
B. Review the matrix view in the Job's runs
Explanation:
To identify which task is causing the failure in the job, the team should review the matrix view in the Job's runs. The matrix view provides a clear and detailed overview of each task's status, allowing the team to quickly identify which task failed. This approach ismore efficient than running each notebook interactively, as it provides immediate insights into the job's execution flow and any issues that occurred during the run.
References:
Databricks documentation on Jobs: Jobs in Databricks
Question # 2
| A machine learning engineer has created a Feature Table new_table using Feature Store Client fs. When creating the table, they specified a metadata description with key information about the Feature Table. They now want to retrieve that metadata programmatically.
Which of the following lines of code will return the metadata description? | | A. There is no way to return the metadata description programmatically. | | B. fs.create_training_set("new_table") | | C. fs.get_table("new_table").description | | D. fs.get_table("new_table").load_df() | | E. fs.get_table("new_table") |
C. fs.get_table("new_table").description
Explanation:
To retrieve the metadata description of a feature table created using the Feature Store Client (referred here asfs), the correct method involves callingget_tableon thefsclient with the table name as an argument, followed by accessing thedescriptionattribute of the returned object. The code snippetfs.get_table("new_table").descriptioncorrectly achieves this by fetching the table object for "new_table" and then accessing its description attribute, where the metadata is stored. The other options do not correctly focus on retrieving the metadata description.
References:
Databricks Feature Store documentation (Accessing Feature Table Metadata).
Question # 3
| Which of the following machine learning algorithms typically uses bagging? | | A. IGradient boosted trees | | B. K-means | | C. Random forest | | D. Decision tree |
C. Random forest
Explanation:
Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging is a technique that involves training multiple base models (such as decision trees) on different subsets of the data and then combining their predictions to improve overall model performance. Each subset is created by randomly sampling with replacement from the original dataset. The Random Forest algorithm builds multiple decision trees and merges them to get a more accurate and stable prediction.
References:
Databricks documentation on Random Forest: Random Forest in Spark ML
Question # 4
| In which of the following situations is it preferable to impute missing feature values with their median value over the mean value? | | A. When the features are of the categorical type | | B. When the features are of the boolean type | | C. When the features contain a lot of extreme outliers | | D. When the features contain no outliers | | E. When the features contain no missingno values |
C. When the features contain a lot of extreme outliers
Explanation:
Imputing missing values with the median is often preferred over the mean in scenarios where the data contains a lot of extreme outliers. The median is a more robust measure of central tendency in such cases, as it is not as heavily influenced by outliers as the mean. Using the median ensures that the imputed values are more representative of the typical data point, thus preserving the integrity of the dataset's distribution. The other options are not specifically relevant to the question of handling outliers in numerical data.
References:
Data Imputation Techniques (Dealing with Outliers).
Question # 5
| 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 |
C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
Explanation:
Pandas API on Spark (previously known as Koalas) provides a pandas-like API on top of Apache Spark. It allows users to perform pandas operations on large datasets using Spark's distributed compute capabilities. Internally, it uses Spark DataFrames and adds metadata that facilitates handling operations in a pandas-like manner, ensuring compatibility and leveraging Spark's performance and scalability.
References
pandas API on Spark documentation:https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html
Question # 6
| A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration.
Which of the following lines of code can the data scientist run to accomplish the task? | | A. spark_df.describe() | | B. dbutils.data(spark_df).summarize() | | C. This task cannot be accomplished in a single line of code. | | D. spark_df.summary() | | E. dbutils.data.summarize (spark_df) |
E. dbutils.data.summarize (spark_df)
Explanation:
To display visual histograms and summaries of the numeric features in a Spark DataFrame, the Databricks utility functiondbutils.data.summarizecan be used. This function provides a comprehensive summary, including visual histograms.
Correct code:
dbutils.data.summarize(spark_df)
Other options likespark_df.describe()andspark_df.summary()provide textual statistical summaries but do not include visual histograms.
References:
Databricks Utilities Documentation
Question # 7
| A machine learning engineer is converting a decision tree from sklearn to Spark ML. They notice that they are receiving different results despite all of their data and manually specified hyperparameter values being identical.
Which of the following describes a reason that the single-node sklearn decision tree and the Spark ML decision tree can differ? | | A. Spark ML decision trees test every feature variable in the splitting algorithm | | B. Spark ML decision trees automatically prune overfit trees | | C. Spark ML decision trees test more split candidates in the splitting algorithm | | D. Spark ML decision trees test a random sample of feature variables in the splitting algorithm | | E. Spark ML decision trees test binned features values as representative split candidates |
E. Spark ML decision trees test binned features values as representative split candidates
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.
References:
Spark MLlib Documentation (Decision Trees and Quantile Binning).
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