Exam Information

See more info here.

Objectives

• Describe the learning context, format, and structure behind the exam.
• Describe the topics covered in the exam.
• Recognize the different types of questions provided on the exam.
• Identify resources to learn the material covered in the exam.

Target Audience

• Data scientist
• Beginner-level, practitioner certification
• Assess candidates at a level equivalent to six months of experience with machine learning with Databricks

Expectations

• Use Databricks Machine Learning and its capabilities within machine learning workflows
• Implement correct decisions in machine learning workflows
• Implement machine learning solutions at scale using Spark ML and other tools
• Understand advanced scaling characteristics of classical machine learning models

Not expected

• Advanced ML Operations: Webhooks, Automation, Deployment, Monitoring, CLI/REST APIs
• Advanced ML Workflows: Target Encoding, Embeddings, Deep Learning, Natural Language Processing

Notes

• Through Kryterion
• Automatically graded

Basic Details

• Time allotted to complete exam = 1.5 hours (90 minutes)
• Passing scores = At least 70% on the overall exam
• Exam fee = $200
• Retake policy = As many times as you want, whenever you want
• Number of Ouestions = 45
• More info. on the Databricks Academy FAQ: http://files.training.databricks.com/Ims/docebo/databricks-academy-fag.odf Select all of the following resources that will be available during the exam.

1. Databricks documentation
2. Paper and pencil
3. A single, index card of pre-written notes
4. A running Spark session
5. None of the above

Certification topics

Use Databricks Machine Learning and its capabilities within machine learning workflows (29%), including:

• Databricks Machine Learning (clusters, Repos, Jobs)
  • Clusters
    • Types of clusters
    • Single-node use cases
    • Standard cluster use cases
  • Repos
    • Connect a repo from an external git provider
    • Commit changes
    • Create a new branch
    • Pull changes from an external Git provider
  • Jobs
    • Orchestrate multi-task ML workflows
• Databricks Runtime for Machine Learning (basics, libraries)
  • Basics
    • Create a cluster with MLR
    • Identify differences between the standard DBR and MLR
  • Library Usage
    • Install a Python library in notebook scope
    • Install a Python library in cluster scope
• AutoML (classification, regression, forecasting)
  • Classification
    • Common steps in the workflow
    • How to locate source code
    • Evaluation metrics
    • Data exploration
  • Regression
    • Common steps in the workflow
    • How to locate source code
    • Evaluation metrics
    • Data exploration
  • Forecasting
    • Common steps in the workflow
    • How to locate source code
• Feature Store (basics)
  • Basics
    • Benefits
    • Create a feature store table
    • Write data to a feature store table
  • Pipelines
    • Train a model with features from a feature store table
    • Score a model using features from a feature store table
• MLflow (Tracking, Model Registry)
  • Experiment tracking
    • Querying past runs
    • Logging runs
    • UI information
  • Model registry
    • Register a model
    • Transition a model across stages

Note, there are self assessment questions to check your understanding.

ML Workflows (29%) Implement correct decisions in machine learning workflows, including:

• Exploratory data analysis (summary statistics, outlier removal)
  • Summary stats
    • Compute summary statistics for a Spark DataFrame
    • DataFrame.summary)
    • dbutils.data.summarize()
  • Outlier removal
    • Removing outlier features
    • Filtering records in a Spark DataFrame
• Feature engineering (missing value imputation, one-hot-encoding)
  • Missing Values
    • Binary indicator features
    • Identifying the optimal replacement value
    • Imputer with Spark ML
  • One-hot-encoding
    • Complications with certain algorithms
    • OHE with Spark ML
• Tuning (hyperparameter basics, hyperparameter parallelization)
  • Hyperparameter basics
    • Grid Search vs. Random Search
    • Tree of Parzen Estimators
    • Scikit-learn
  • Hyperparamter parallelization
    • Hyperopt applications
    • Relationship between selection algorithm and parallelization
• Evaluation and selection (cross-validation, evaluation metrics)
  • Cross-validation
    • Number of trials
    • Train-validation split vs. cross-validation
    • Using Spark ML to accomplish the above
  • Evaluation metrics
    • Recall
    • Precision
    • F1
    • Log-scale interpretation

Note, there are self assessment questions to check your understanding.

Usage of SparkML (33%) Implement machine learning solutions at scale using Spark ML and other tools, including:

• Distributed ML Concepts
  • Difficulties
    • Data location and shuffling
    • Data fitting on each core for parallellization
  • Spark ML
    • No UDF requirement - when to use UDF; when to use Spark ML. Relationship with other libraries.
• Spark ML Modeling APIs (data splitting, training, evaluation, estimators vs. transformers, pipelines)
  • Prep
    • Splitting data
    • Reproducible splits
  • Modeling
    • Fitting
    • Feature vector columns
    • Evaluators
    • Estimators vs. transformers
  • Pipelines
    • Relationship with Cross validation (e.g. inside or outside pipeline)
    • Relationship with training and test data
• Hyperopt
  • Basics
    • Bayesian hyperparameter abilities
    • Parallelization abilities
  • Applications
    • SparkTrials sv Trials
    • Relationship between number of evaluations and level of parallelization
• Pandas API on Spark
  • Concepts
    • InternalFrame
    • Metadata storage
  • Benefits
    • Easy refactoring for scale
    • Pandas API
  • Usage
    • Importing
    • Converting between DF types
• Pandas UDFs and Pandas Function APIs
  • Conversion - Apache arrow (why is this efficient?) - Vectorization
  • Pandas UDFs - why would you use them?
    • Iterator UDF benefits
    • For scaled prediction
  • Pandas Function APIs
    • Group-specific training
    • Group-specific inference

Note, there are self assessment questions to check your understanding.

**Scaling ML models (9%) - Understand advanced scaling characteristics of classical machine learning models, including **:

• Distributed Linear Regression
  • Identify what type of solver is used for big data and linear regression
  • Identify the family of techniques used to distribute linear regression
• Distributed Decision Trees
  • Describe the binning strategy used by Spark ML for distributed decision trees
  • Describe the purpose of the maxBins parameter
• Ensembling Methods (bagging, boosting)
  • Basics
    • Combining models
    • Implications of multi-model solutions
  • Types
    • Bagging
    • Boosting
    • Stacking

Note, there are self assessment questions to check your understanding.

Practice & preparation

• Practice exam is coming soon
• Some practice questions are shown at the end of the course.