Question # 1
| A new data scientist has started working on an existing machine learning project. The project is a scheduled Job that retrains every day. The project currently exists in a Repo in Databricks. The data scientist has been tasked with improving the feature engineering of the pipeline’s preprocessing stage. The data scientist wants to make necessary updates to the code that can be easily adopted into the project without changing what is being run each day.
Which approach should the data scientist take to complete this task? | | A. They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider. | | B. They can clone the notebooks in the repository into a Databricks Workspace folder and make the necessary changes. | | C. They can create a new Git repository, import it into Databricks, and copy and paste the existing code from the original repository before making changes. | | D. They can clone the notebooks in the repository into a new Databricks Repo and make the necessary changes. |
A. They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider.
Explanation:
The best approach for the data scientist to take in this scenario is to create a new branch in Databricks, commit their changes, and push those changes to the Git provider. This approach allows the data scientist to make updates and improvements to the feature engineering part of the preprocessing pipeline without affecting the main codebase that runs daily. By creating a new branch, they can work on their changes in isolation. Once the changes are ready and tested, they can be merged back into the main branch through a pull request, ensuring a smooth integration process and allowing for code review and collaboration with other team members.
References:
Databricks documentation on Git integration: Databricks Repos
Question # 2
| Which of the following statements describes a Spark ML estimator? | | A. An estimator is a hyperparameter grid that can be used to train a model
| | B. An estimator chains multiple algorithms together to specify an ML workflow
| | C. An estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions
| | D. An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer
| | E. An estimator is an evaluation tool to assess to the quality of a model |
D. An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer
Question # 3
| Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems? | | A. F1 | | B. R-squared | | C. MAE | | D. MSE |
A. F1
Explanation:
The code block provided by the machine learning engineer will perform the desired inference when the Feature Store feature set was logged with the model at model_uri. This ensures that all necessary feature transformations and metadata are available for the model to make predictions. The Feature Store in Databricks allows for seamless integration of features and models, ensuring that the required features are correctly used during inference.
References:
Databricks documentation on Feature Store: Feature Store in Databricks
Question # 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 |
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 # 5
| A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult.
Which of the following describes why? | | A. Gradient boosting is not a linear algebra-based algorithm which is required for parallelization. | | B. Gradient boosting requires access to all data at once which cannot happen during parallelization. | | C. Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization. | | D. Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step. | | E. Gradient boosting uses decision trees in each iteration which cannot be parallelized. |
D. Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step.
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 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 ps
df = ps.to_pandas(spark_df) | | C. spark_df.to_sql() | D. import pandas as pd
df = pd.DataFrame(spark_df) | | E. spark_df.to_pandas() |
A. import pyspark.pandas as ps
df = ps.DataFrame(spark_df)
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