Question # 1
| An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion? | | A. One-hot encoding is not supported by most machine learning libraries.
| | B. One-hot encoding is dependent on the target variable’s values which differ for each application.
| | C. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
| | D. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
| | E. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms. |
E. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
Question # 2
| What is the name of the method that transforms categorical features into a series of binary indicator feature variables?
| | A. Leave-one-out encoding | | B. Target encoding | | C. One-hot encoding | | D. Categorical embeddings | | E. String indexing |
C. One-hot encoding
Question # 3
| Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?
| | A. TrainValidationSplit
| | B. DataFrame.where
| | C. CrossValidator
| | D. TrainValidationSplitModel
| | E. DataFrame.randomSplit |
E. DataFrame.randomSplit
Question # 4
| A data scientist has replaced missing values in their feature set with each respective feature variable’s median value. A colleague suggests that the data scientist is throwing away valuable information by doing this.
Which of the following approaches can they take to include as much information as possible in the feature set? | | A. Impute the missing values using each respective feature variable’s mean value instead of the median value
| | B. Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them | | C. Remove all feature variables that originally contained missing values from the feature set | | D. Create a binary feature variable for each feature that contained missing values indicating whether each row’s value has been imputed | | E. Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing |
D. Create a binary feature variable for each feature that contained missing values indicating whether each row’s value has been imputed
Question # 5
| 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 # 6
| 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 # 7
A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem. The following root-mean-squared-error values are calculated on each of the validation folds:
Which of the following values represents the overall cross-validation root-mean-squared error? | | A. 13.0 | | B. 17.0 | | C. 12.0 | | D. 39.0 | | E. 10.0 |
A. 13.0
Databricks Databricks-Machine-Learning-Associate Exam Dumps
5 out of 5
Pass Your Databricks Certified Machine Learning Associate Exam in First Attempt With Databricks-Machine-Learning-Associate Exam Dumps. Real ML Data Scientist Exam Questions As in Actual Exam!
— 74 Questions With Valid Answers
— Updation Date : 28-Mar-2025
— Free Databricks-Machine-Learning-Associate Updates for 90 Days
— 98% Databricks Certified Machine Learning Associate Exam Passing Rate
PDF Only Price 49.99$
19.99$
Buy PDF
Speciality
Additional Information
Testimonials
Related Exams
- Number 1 Databricks ML Data Scientist study material online
- Regular Databricks-Machine-Learning-Associate dumps updates for free.
- Databricks Certified Machine Learning Associate Practice exam questions with their answers and explaination.
- Our commitment to your success continues through your exam with 24/7 support.
- Free Databricks-Machine-Learning-Associate exam dumps updates for 90 days
- 97% more cost effective than traditional training
- Databricks Certified Machine Learning Associate Practice test to boost your knowledge
- 100% correct ML Data Scientist questions answers compiled by senior IT professionals
Databricks Databricks-Machine-Learning-Associate Braindumps
Realbraindumps.com is providing ML Data Scientist Databricks-Machine-Learning-Associate braindumps which are accurate and of high-quality verified by the team of experts. The Databricks Databricks-Machine-Learning-Associate dumps are comprised of Databricks Certified Machine Learning Associate questions answers available in printable PDF files and online practice test formats. Our best recommended and an economical package is ML Data Scientist PDF file + test engine discount package along with 3 months free updates of Databricks-Machine-Learning-Associate exam questions. We have compiled ML Data Scientist exam dumps question answers pdf file for you so that you can easily prepare for your exam. Our Databricks braindumps will help you in exam. Obtaining valuable professional Databricks ML Data Scientist certifications with Databricks-Machine-Learning-Associate exam questions answers will always be beneficial to IT professionals by enhancing their knowledge and boosting their career.
Yes, really its not as tougher as before. Websites like Realbraindumps.com are playing a significant role to make this possible in this competitive world to pass exams with help of ML Data Scientist Databricks-Machine-Learning-Associate dumps questions. We are here to encourage your ambition and helping you in all possible ways. Our excellent and incomparable Databricks Databricks Certified Machine Learning Associate exam questions answers study material will help you to get through your certification Databricks-Machine-Learning-Associate exam braindumps in the first attempt.
Pass Exam With Databricks ML Data Scientist Dumps. We at Realbraindumps are committed to provide you Databricks Certified Machine Learning Associate braindumps questions answers online. We recommend you to prepare from our study material and boost your knowledge. You can also get discount on our Databricks Databricks-Machine-Learning-Associate dumps. Just talk with our support representatives and ask for special discount on ML Data Scientist exam braindumps. We have latest Databricks-Machine-Learning-Associate exam dumps having all Databricks Databricks Certified Machine Learning Associate dumps questions written to the highest standards of technical accuracy and can be instantly downloaded and accessed by the candidates when once purchased. Practicing Online ML Data Scientist Databricks-Machine-Learning-Associate braindumps will help you to get wholly prepared and familiar with the real exam condition. Free ML Data Scientist exam braindumps demos are available for your satisfaction before purchase order.
Send us mail if you want to check Databricks Databricks-Machine-Learning-Associate Databricks Certified Machine Learning Associate DEMO before your purchase and our support team will send you in email.
If you don't find your dumps here then you can request what you need and we shall provide it to you.
Bulk Packages
$50
- Get 3 Exams PDF
- Get $33 Discount
- Mention Exam Codes in Payment Description.
Buy 3 Exams PDF
$70
- Get 5 Exams PDF
- Get $65 Discount
- Mention Exam Codes in Payment Description.
Buy 5 Exams PDF
$100
- Get 5 Exams PDF + Test Engine
- Get $105 Discount
- Mention Exam Codes in Payment Description.
Buy 5 Exams PDF + Engine
 Jessica Doe
ML Data Scientist
We are providing Databricks Databricks-Machine-Learning-Associate Braindumps with practice exam question answers. These will help you to prepare your Databricks Certified Machine Learning Associate exam. Buy ML Data Scientist Databricks-Machine-Learning-Associate dumps and boost your knowledge.
|
