Exam Name: Databricks Certified Machine Learning Associate Exam
Certification Provider: Databricks
Corresponding Certification: ML Data Scientist
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Promotion and growth follow demonstrated skill, and demonstrated skill starts with preparation. PrepAwayPDF's Databricks-Machine-Learning-Associate question bank builds it methodically: 76 practice questions covering the Databricks Certified Machine Learning Associate objectives, every answer expert-verified.
Databricks Databricks-Machine-Learning-Associate Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Machine Learning Associate Exam |
| Exam Number: | Databricks-Machine-Learning-Associate |
| Available Languages: | English, Brazilian Portuguese, Korean, Japanese |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple Choice, Multiple Selection |
| Related Certifications: | Databricks Certified Machine Learning Professional Databricks Certified Data Engineer Associate |
| Passing Score: | 70% |
| Exam Price: | $200 USD |
| Real Exam Qty: | 45-48 |
| Exam Duration: | 90 minutes |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored exam or authorized testing center. |
| Pre Condition: | No formal prerequisite. Databricks recommends at least 6 months of hands-on experience with Databricks Machine Learning, Python, SQL, MLflow, AutoML, Feature Store, and machine learning workflows. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/machine-learning-associate |
Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Databricks Machine Learning | 38% | - Databricks ML Capabilities
|
| Topic 2: ML Workflows | 19% | - Machine Learning Lifecycle
|
| Topic 3: Model Deployment | 12% | - Production Deployment
|
| Topic 4: Model Development | 31% | - Building and Training Models
|
From Anxiety to Action: Databricks-Machine-Learning-Associate Facts You Need
Per the latest exam information, the Databricks-Machine-Learning-Associate exam presents 45-48 questions in 90 minutes minutes. Knowing the format precisely removes one major source of exam-day anxiety.
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The Databricks Certified Machine Learning Associate blueprint covers these core domains:
- Model Development (31%)
- Databricks Machine Learning (38%)
- ML Workflows (19%)
The official outline lists further domains, all covered in our question bank.
Databricks publishes these prerequisites for the Databricks Certified Machine Learning Associate: No formal prerequisite. Databricks recommends at least 6 months of hands-on experience with Databricks Machine Learning, Python, SQL, MLflow, AutoML, Feature Store, and machine learning workflows..
Check the authoritative wording on the official certification page before registering.
At present, passing the Databricks-Machine-Learning-Associate exam requires 70%, and the registration fee is $200 USD. Both are set by Databricks and can change — confirm them on the official site when you book.
Written, clear, and honored. If you fail the corresponding exam within 60 days of purchase, send us a scanned copy of your enrollment slip and your official Score Report PDF within two days of the exam date; verified claims are refunded in full within seven days. Exclusions: exams taken within three days of purchase, candidate names that differ from the payer, and free or expired products. If you prefer, exchange your product for two others of equal value at no charge.
Databricks Certified Machine Learning Associate Sample Questions:
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. spark_df.to_sql()
- B. import pyspark.pandas as ps
df = ps.to_pandas(spark_df) - C. import pyspark.pandas as ps
df = ps.DataFrame(spark_df) - D. import pandas as pd
df = pd.DataFrame(spark_df) - E. spark_df.to_pandas()
Correct Answer: C 🗳️
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What is the name of the method that transforms categorical features into a series of binary indicator feature variables?
- A. Categorical
- B. Leave-one-out encoding
- C. Target encoding
- D. One-hot encoding
- E. String indexing
Correct Answer: D 🗳️
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Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?
- A. Scikit-learn
- B. Spark ML
- C. Keras
- D. PyTorch
Correct Answer: B 🗳️
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A data scientist uses 3-fold cross-validation and the following hyperparameter grid when optimizing model hyperparameters via grid search for a classification problem:
* Hyperparameter 1: [2, 5, 10]
* Hyperparameter 2: [50, 100]
Which of the following represents the number of machine learning models that can be trained in parallel during this process?
- A. 6
- B. 3
- C. 5
- D. 18
Correct Answer: D 🗳️
Explanation: Only visible for PrepAwayPDF members. You can sign-up / login (it's free).
A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases.
Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?
- A. Feature Store
- B. Spark SQL
- C. PySpark DataFrame API
- D. pandas API on Spark
Correct Answer: D 🗳️
Explanation: Only visible for PrepAwayPDF members. You can sign-up / login (it's free).
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