Databricks · DCMLEA
Databricks Certified Machine Learning Associate Practice Tests
Validates foundational knowledge of machine learning on the Databricks platform, covering AutoML, Feature Store, ML workflows and experiment tracking with MLflow, model development with Spark ML, and model deployment and serving.
Exam Details
Practice Questions
630
≈ 14 practice exams
Duration
90 minutes
Passing Score
70%
Difficulty
AssociateLast Updated
Feb 2026
Databricks Certified Machine Learning Associate Practice Exam Preparation
Use this DCMLEA practice exam to prepare for Databricks Certified Machine Learning Associate with realistic questions, detailed explanations, and focused study modes. The practice bank includes 630 questions for Databricks DCMLEA, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Spark ML33%
Databricks Machine Learning29%
ML Workflows29%
Scaling ML Models9%
Exam Overview
The Databricks Certified Machine Learning Associate certification validates foundational knowledge and practical ability to perform core machine learning tasks on the Databricks Lakehouse Platform. The exam covers the full ML lifecycle, including exploratory data analysis, feature engineering, model training, hyperparameter tuning, evaluation, and deployment using Databricks-native tooling such as AutoML, the Feature Store, Unity Catalog integration, and Managed MLflow for experiment tracking and model registry. Candidates are expected to demonstrate proficiency with both single-node and distributed machine learning approaches, including Spark ML APIs, Hyperopt with SparkTrials, and Pandas UDFs.
The certification was updated on October 28, 2024, to reflect current platform capabilities including real-time, batch, and streaming inference patterns as well as MLOps best practices such as model metadata tagging. All machine learning code on the exam is in Python; data manipulation code outside ML-specific tasks may appear in SQL. The exam is administered online through Databricks' exam delivery platform and costs $200 USD, with local taxes potentially applicable.
Who Should Take This Exam
This certification is designed for data scientists, machine learning engineers, and ML-adjacent data engineers who perform machine learning workflows on Databricks and want to validate their skills at an associate level. Candidates are typically early-to-mid career practitioners with approximately 6 or more months of hands-on experience using Databricks for machine learning tasks including model training, tuning, and deployment.
The exam is also well-suited for analytics consultants and data engineers who collaborate closely with ML teams and want to deepen their understanding of the Databricks ML platform. It serves as a prerequisite stepping stone for the Databricks Certified Machine Learning Professional certification.
Prerequisites
There are no formal prerequisites required to sit for this exam. However, Databricks recommends at least 6 months of hands-on experience performing machine learning tasks on the Databricks platform as outlined in the official exam guide. Candidates should have practical familiarity with Databricks workspaces, clusters, Repos, and Jobs, as well as the Databricks Runtime for Machine Learning and its bundled libraries.
A foundational understanding of machine learning concepts—including supervised learning, feature engineering, model evaluation metrics, and hyperparameter tuning—is expected. Familiarity with Python and a working knowledge of Apache Spark concepts (DataFrames, distributed computation) are strongly recommended, as Spark ML accounts for the largest share of exam content.
Exam Format
The Databricks Certified Machine Learning Associate exam consists of 48 scored multiple-choice and multiple-response questions to be completed within 90 minutes. The passing score is 70%. The exam may include a small number of unscored items used to gather statistical data for future exam development; these items are not identified on the form, do not count toward the final score, and are accounted for in the total allotted time.
The exam is delivered online through Databricks' exam delivery platform and can be taken remotely. All ML code presented in questions is written in Python; SQL may appear for non-ML data manipulation scenarios. The certification is valid for two years from the date of passing, after which recertification is required to maintain certified status. The exam fee is $200 USD (local taxes may apply).
Skills Measured
- 1.Spark ML (33%): Covers distributed machine learning concepts and the Spark ML modeling APIs including Pipelines, Transformers, and Estimators. Includes hyperparameter optimization with Hyperopt and SparkTrials for distributed tuning, use of the Pandas API on Spark, and Pandas UDFs and Function APIs for applying Python ML logic at scale.
- 2.Databricks Machine Learning (29%): Covers the Databricks ML platform including cluster configuration (driver/worker nodes, access modes), Repos, and Jobs. Includes Databricks Runtime for Machine Learning basics and bundled libraries, AutoML for classification, regression, and forecasting, the Databricks Feature Store for feature creation and serving, and Managed MLflow for experiment tracking, model registry, and lifecycle management.
- 3.ML Workflows (29%): Covers the end-to-end machine learning workflow from exploratory data analysis (EDA) through feature engineering techniques (missing value imputation, outlier handling, feature scaling, encoding, selection, and transformation) to model training, hyperparameter tuning, evaluation metrics, and model selection best practices.
- 4.Scaling ML Models (9%): Covers strategies for distributing model training and inference across a Spark cluster, including model parallelism, data parallelism, and ensembling distribution techniques for scaling production ML workloads on Databricks.
Study Tips
- Prioritize Spark ML since it carries the highest domain weight (33%). Focus specifically on the Pipeline API, Hyperopt with SparkTrials for distributed hyperparameter search, and the differences between Pandas UDFs and the Pandas API on Spark—these topics appear frequently in exam questions.
- Download and study the official Databricks Certified Machine Learning Associate Exam Guide PDF, available from the official exam page. It lists every testable objective and includes sample questions that demonstrate how objectives are translated into actual test items—this is the single most important study document.
- Complete the Databricks Academy learning path aligned to this exam. Relevant free self-paced courses include 'Data Preparation for Machine Learning,' 'Machine Learning Model Development,' 'Machine Learning Model Deployment,' and 'Machine Learning Ops'—all available on Databricks Academy.
- Get hands-on with AutoML and the Feature Store in a Databricks workspace. The exam tests practical knowledge of initiating AutoML runs, interpreting generated notebooks, creating and reading from Feature Store tables, and linking features to MLflow models using the FeatureStoreClient API.
- Practice with MLflow end-to-end: logging parameters, metrics, and artifacts; using the Model Registry to transition models through Staging and Production stages; and loading registered models for batch and real-time inference. Understand how MLflow integrates with Unity Catalog in newer workspace configurations.
- Use Udemy practice exams (search for Databricks Machine Learning Associate practice tests) to simulate exam conditions. Target scoring above 85% consistently on practice tests before scheduling the real exam, which requires a 70% passing score.
- Study the post-October 2024 exam guide version specifically, as the exam was updated in late October 2024 to include real-time vs. batch vs. streaming inference distinctions and MLOps best practices such as model metadata tagging—topics not covered in older study materials.
Career Benefits
Holding the Databricks Certified Machine Learning Associate credential signals verified proficiency with the Databricks Lakehouse Platform for ML—a platform widely adopted across enterprises using the Azure Databricks, AWS, and Google Cloud ecosystems. It is recognized by employers hiring for data scientist, ML engineer, and MLOps roles where Databricks is part of the production stack. The certification is particularly valuable at organizations that have standardized on Databricks for unified data and AI workloads, as it demonstrates readiness to contribute to ML pipelines without extensive onboarding.
While Databricks does not publish official salary data tied to this specific credential, practitioners with Databricks ML certifications and associated skills (Spark, MLflow, cloud ML platforms) command salaries broadly in the $110,000–$160,000+ USD range for ML engineer and data scientist roles in the US market, depending on seniority and location. The Associate-level certification serves as a recognized stepping stone to the Databricks Certified Machine Learning Professional exam, which tests advanced topics such as model monitoring, feature engineering at scale, and custom MLflow integrations.
Sample Questions
5 sample questions with answers and explanations. The full bank has 630 questions, enough for 14 full-length practice exams.
Preview — answers shown1. An ML platform team manages model serving endpoints that need to support gradual rollout of new model versions. They want to send 95% of traffic to the current production model (version 3) and 5% to a new candidate model (version 4) for evaluation. How should they configure the endpoint? (Select one!)
ACreate two separate endpoints and use a load balancer with 95/5 traffic split
BConfigure two served_entities with traffic_config specifying 95% and 5% traffic percentages
CSet scale_to_zero_enabled to true on the candidate model entity
DUse the workload_size parameter to allocate different compute resources
Explanation
Databricks model serving endpoints support multiple served_entities with traffic_config that specifies traffic percentages for A/B testing and gradual rollouts. This is the native, correct approach. Creating separate endpoints with external load balancers adds unnecessary complexity and operational overhead. The scale_to_zero_enabled parameter controls auto-scaling behavior but does not control traffic distribution between model versions. The workload_size parameter controls compute capacity for individual entities but does not distribute traffic between them.
2. An ML engineer uses mlflow.register_model to register a model from run_id xyz789 with registered model name credit_model. The registration succeeds and creates version 1. The team later discovers the run_id xyz789 artifacts are deleted due to experiment retention policies. What happens when attempting to load the registered model using mlflow.pyfunc.load_model for models:/credit_model/1? (Select one!)
AThe model loads successfully because Model Registry stores a copy of model artifacts independent of the run
BMLflow automatically re-trains the model using logged parameters from the run
CThe model fails to load because the source run artifacts no longer exist
DThe model loads from a cached version on the local filesystem
Explanation
When a model is registered in MLflow Model Registry, the registry creates a copy of the model artifacts in its own storage location separate from the source run. This copy is independent of the original run artifacts. Even if the source run is deleted or its artifacts are removed due to retention policies, the registered model version remains accessible through the Model Registry. This separation ensures production models are protected from experiment cleanup policies. The model does not rely on cached versions or re-training.
3. A healthcare ML team builds a binary classification model to detect a rare disease that affects 2% of the population. False negatives (missing actual disease cases) have severe health consequences, while false positives (incorrectly flagging healthy patients) result in additional testing costs. The team needs to select the evaluation metric that best aligns with the objective of identifying as many true disease cases as possible. Which metric should they prioritize? (Select one!)
ARecall
BPrecision
CF1 score
DAccuracy
Explanation
Recall (sensitivity or true positive rate) measures the proportion of actual positive cases that the model correctly identifies. Maximizing recall minimizes false negatives, which is critical when missing disease cases has severe health consequences. In medical diagnosis scenarios where identifying all positive cases is paramount, recall is the appropriate metric. Precision measures the proportion of predicted positives that are actually positive, which is important for minimizing false positives but does not directly optimize for finding all disease cases. Accuracy is misleading for imbalanced datasets; a model predicting all negatives achieves 98% accuracy but misses all disease cases. F1 score balances precision and recall but does not specifically prioritize minimizing false negatives as required by the healthcare scenario.
4. A data science team logs training runs using MLflow and needs to query experiments to find the best performing model. They want to find all runs where accuracy exceeds 0.85 and the model type parameter equals RandomForest, sorted by F1 score. Which MLflow query syntax should they use? (Select one!)
Amlflow.search_runs(filter_string="metrics.accuracy > 0.85 OR params.model_type = 'RandomForest'", order_by=["metrics.f1 DESC"])
Bmlflow.search_runs(filter_string="accuracy > 0.85 AND model_type = 'RandomForest'", order_by=["f1 DESC"])
Cmlflow.search_runs(filter_string="metrics.accuracy >= 0.85 AND params.model_type LIKE 'RandomForest'", order_by=["f1"])
Dmlflow.search_runs(filter_string="metrics.accuracy > 0.85 AND params.model_type = 'RandomForest'", order_by=["metrics.f1 DESC"])
Explanation
The correct syntax requires metrics prefix for metric filters (metrics.accuracy), params prefix for parameter filters (params.model_type), equality operator for exact parameter matches, and metrics prefix in order_by with DESC for descending sort. MLflow search_runs only supports AND operator, not OR. The filter string must include proper prefixes for all attributes. Order by requires DESC keyword for descending sort and must include the metrics prefix.
5. A machine learning team creates a training set using FeatureEngineeringClient with FeatureLookup objects that join features from a customer_profile table. The customer_profile feature table has primary keys defined as customer_id (string) and region_code (integer) in that specific order. The base DataFrame contains these columns in the order region_code, customer_id. When creating the training set, the join operation fails. What is the cause of this failure? (Select one!)
AFeatureLookup requires the output_name parameter when joining multiple tables
BThe base DataFrame must include timestamp_lookup_key for all feature tables
CThe base DataFrame must have at least 50 feature tables to create a training set
DThe type and order of lookup_key columns must match the primary keys of the feature table
Explanation
The type and order of lookup_key columns in the DataFrame must match the type and order of the primary keys in the feature table. The feature table has primary keys in order customer_id (string), region_code (integer), but the base DataFrame has them in order region_code, customer_id, causing the join to fail. The 50-table limit is the maximum number of feature tables allowed, not a minimum requirement. The output_name parameter is optional and used to rename features, not required for joins. The timestamp_lookup_key is only required for point-in-time correctness with time series features, not for all feature table joins.
More Databricks Practice Exams
Databricks Certified Data Engineer Associate
DCDEA · 628 questions
Databricks Certified Data Engineer Professional
DCDEP · 628 questions
Databricks Certified Generative AI Engineer Associate
DCGAE · 620 questions
Databricks Certified Machine Learning Professional
DCMLEP · 622 questions
Databricks Certified Associate Developer for Apache Spark
DCASD · 604 questions
Databricks Certified Data Analyst Associate
DCDAA · 627 questions
$17.99
One-time access to this exam
630 questions (14 practice exams' worth)
Unlimited timed exam simulations
Yours forever — no subscription
Detailed explanations
Free preview stays available