ML-ASSOC practice questions 41–45

1. Question 41 · 45 in bank

   A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

   

   The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df. Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

   • A//IMG//
   • B//IMG//
   • C//IMG//
   • Dfeatures_df.write.mode("feature").path("new_table")

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   Answer: C

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2. Question 42 · 45 in bank

   A machine learning engineer working for an Energy Distributor wants to perform a time-series forecast of critical turbines sensor measures. Here is the feature set under consideration:

   

   The machine learning engineer creates a first model and gets a higher than expected RMSE. They believe the model can be improved using data imputation. They will use data science best practices for imputation. Which data imputation strategy is most likely to enhance the dataset's quality and predictive performance?

   • AFor sensor type use mode, for temperature and vibration use mean (per day).
   • BThey can just remove all out of range values and missing values from the training set.
   • CFor sensor type use mode, for temperature use median (per day) and for vibration use mean (per day).
   • DFor sensor type use mode, for temperature use mean (per day) and for vibration use median (per day).

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   Answer: D

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3. Question 43 · 45 in bank

   A data scientist is developing a machine learning model to predict house prices in a competitive real estate market. They initially select a loss function that heavily penalizes large errors, hoping it will improve the model’s performance. However, after training, they observe that the model struggles to converge and produces unstable predictions, with large variations in price predictions for similar houses. The data scientist suspects that the chosen loss function is causing these issues. Why is it crucial to select the right loss function in this situation?

   • AThe loss faction ensures that the data is balanced.
   • BThe loss function directly influences how the model's parameters are updated during training.
   • CThe loss function controls the size of the training set.
   • DThe loss function determines the computational efficiency of the model.

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   Answer: B

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4. Question 44 · 45 in bank

   A data scientist in an investment banking company has been asked to accelerate their model development process on certain tasks of their everyday job so they can free up time for other ones. Which task is typically automated by AutoML in Databricks for that purpose?

   • AMonitoring model performance for inference.
   • BTraining and optimizing different models.
   • CDeploying the model on a serving endpoint.
   • DCreating a feature engineering set for model development.

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   Answer: B

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5. Question 45 · 45 in bank

   A data scientist wants to train a model using an existing Delta table as a feature table, in a workspace with Unity Catalog enabled. As this Delta table already contains data that is constantly being updated, they want to avoid duplicating data or having to keep another table synchronized. This table was created with the following code, with the customer_id column as the unique identifier:

   

   Which SQL code statement will enable them to use this Delta table as a feature table?

   • ACREATE FEATURE TABLE store.rfv_analysis.customer_features USING PRIMARY KEY (customer_id);
   • B//IMG//
   • CALTER TABLE store.rfv_analysis.customer_features ADD CONSTRAINT customer_features_pk PRIMARY KEY(customer id);
   • DALTER TABLE store.rfv_analysis.customer_features ALTER COLUMN customer_id SET NOT NULL; ALTER TABLE store.rfv_analysis.customer_features ADD CONSTRAINT customer_features_pk PRIMARY KEY (customer_id);

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   Answer: A

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ML-ASSOC practice, question 41. 45 questions available.