100+ Free Cloudera Machine Learning Engineer Practice Questions

Cloudera Machine Learning

Provision and use Cloudera AI workspaces and projects; run interactive sessions, experiments, jobs, and applications; select and customize ML runtimes and editors (Workbench, PBJ Workbench, JupyterLab); deploy Accelerators for ML Projects (AMPs) driven by .project-metadata.yaml; build Cloudera Data Visualization dashboards; and configure GPUs with Nvidia GPU Edition runtimes and workload accelerator labels.

Spark MLlib

Build spark.ml pipelines from transformers (VectorAssembler, StringIndexer, OneHotEncoder, HashingTF, IDF) and estimators; perform model selection and tuning with ParamGridBuilder, CrossValidator, and TrainValidationSplit; and fit and evaluate models using BinaryClassificationEvaluator, MulticlassClassificationEvaluator, RegressionEvaluator, and ClusteringEvaluator.

Spark

Work with schema-aware DataFrames and their lazy transformations versus actions; choose efficient file types such as Parquet and ORC (columnar) versus Avro (row-based, schema-evolving) and CSV; and apply window functions with PARTITION BY, ORDER BY, frame clauses, and rank, dense_rank, row_number, lag, and lead.

Deploying a Machine Learning Model

Expose a function as a REST Model endpoint using cml.models_v1 and the @models.cml_model decorator; protect endpoints with access keys; configure replicas and autoscaling for performance; capture model metrics for monitoring and drift detection; and use MLflow experiment tracking with the Cloudera AI Model Registry for versioned, governed deployment.

Deep Learning and General Machine Learning

Distinguish supervised from unsupervised learning; recognize common algorithms (logistic regression, random forest, KMeans, PCA); manage overfitting, the bias-variance tradeoff, and imbalanced-data metrics (precision, recall, F1); and understand deep learning fundamentals including neural networks, activation functions, and GPU-accelerated training.