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Reference: https://www.databricks.com/learn/certification/machine-learning-professional
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Databricks Certified Machine Learning Professional Sample Questions:
A machine learning engineer needs to deliver predictions of a machine learning model in real- time. However, the feature values needed for computing the predictions are available one week before the query time. Which feature is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?
- A. Computing predictions in real-time provides more up-to-date results
- B. Batch serving has built-in capabilities in Databricks Machine Learning
- C. Querying stored predictions can be faster than computing predictions in real-time
- D. There is no advantage to using batch serving deployments over real-time serving deployments
- E. Testing is not possible in real-time serving deployments
Correct Answer: B 🗳️
A machine learning engineer needs a python_model to access a collection of files using its load_context operation. The collection of files being accessed by python_model.load_context needs to be saved when the model is being logged. A dictionary of the names and paths of these files is properly stored in my_dict.
The machine learning engineer has written the following incomplete block of code:
Which lines of code can be used to fill in the blank to successfully complete the code block to accomplish the task?
- A. model_context
- B. artifacts
- C. None of these lines of code can be used to successfully complete the code block
- D. context
Correct Answer: B 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.
Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?
- A. There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt
- B. Startinq a manual parent run before calling fmin
- C. Starting a manual child run within the objective function
- D. Ensuring that a built-in model flavor is used for the model logging
- E. MLflow Autoloqqinq will automatically accomplish this task with Hyperopt
Correct Answer: B 🗳️
A Machine Learning Engineer is training a large-scale gradient boosting model using SparkML on a cluster of machines. The training job fails due to memory overflow on a single executor node after processing several iterations. The cluster resources are limited to executor nodes with 16 CPU cores and 64 GB RAM each. The engineer wants to continue training the model without changing hyperparameters or reducing the dataset size. They know Spark's architecture well and want to take advantage of its benefits. Which approach will allow the Machine Learning Engineer to solve this issue?
- A. Increase the number of executor nodes and replicate the entire model on each node, then implement data parallelism to train on different mini-batches simultaneously.
- B. Increase the number of executor nodes and implement data parallelism by partitioning the dataset across executors so each node trains the model on a subset of data.
- C. Increase the number of executor nodes and implement model parallelism by splitting the gradient boosting model across executors so each node trains a part of the model.
- D. Increase the number of executor nodes and replicate the entire model on each node, then apply model parallelism to train different parts of the model in parallel.
Correct Answer: B 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?
- A. There is no way to accomplish this task with fs
- B. fs.read_table
- C. fs.create_table
- D. fs.get_table
- E. fs.write_table
Correct Answer: C 🗳️
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