Domain 1: Data Lakehouse Fundamentals

IBM Certified watsonx Data Lakehouse Engineer v1 - Associate · 67 questions

• Which characteristic most clearly distinguishes a data lakehouse from a traditional data warehouse?
• What capability does a data lakehouse provide that a traditional data lake typically lacks?
• In a data lakehouse architecture; what does separating storage from compute primarily enable?
• Which feature of Apache Iceberg enables querying historical versions of a table without duplicating data?
• How does Apache Iceberg provide ACID transaction support on object storage?
• What does schema evolution in Apache Iceberg allow you to do safely?
• Which statement best describes a key differentiator of Delta Lake compared to plain Parquet files on object storage?
• Apache Hudi is particularly well-suited for which data ingestion pattern?
• A team needs to query their data lake tables from multiple query engines including Presto; Spark; and Trino. Which open table format provides the broadest multi-engine support?
• Why is Apache Parquet the predominant file format for analytical workloads in data lakehouses?
• In which scenario might Apache ORC be preferred over Apache Parquet?
• For which use case is Apache Avro most commonly chosen over Parquet?
• What is the primary design goal of the Presto distributed SQL query engine?
• What role does Apache Spark most commonly play in a data lakehouse architecture?
• What is the primary function of the Hive Metastore in a data lakehouse?
• What role does MinIO play in a data lakehouse architecture?
• In the medallion architecture; what type of data is stored in the Bronze layer?
• What transformation occurs when data moves from the Bronze layer to the Silver layer in the medallion architecture?
• What characterizes data in the Gold layer of the medallion architecture?
• What is the primary purpose of a data catalog in a lakehouse environment?
• Why is data lineage tracking important in a data lakehouse?
• Which approach best implements column-level security in a data lakehouse?
• What does partition evolution in Apache Iceberg allow without breaking existing queries?
• What is the purpose of expiring snapshots in an Apache Iceberg table?
• Which statement accurately compares transaction models of Apache Iceberg and Delta Lake?
• How does a data lakehouse typically reduce total cost of ownership compared to a traditional data warehouse?
• Why is file compaction important in a data lakehouse using Apache Iceberg?
• How do column-level min/max statistics in Parquet files help query engines?
• In a data lakehouse; which component ensures that multiple query engines see a consistent view of table schemas and file locations?
• Which property of ACID transactions in a data lakehouse prevents a query from reading a partially written table?
• Which limitation of a traditional data lake is directly addressed by the data lakehouse architecture?
• What is the advantage of Apache Iceberg's hidden partitioning compared to Hive-style partitioning?
• A team migrating from a NAS-based data platform assumes they can keep their existing pattern of appending to files in place and renaming directories to reorganise data. Why does this assumption fail on object storage?
• An organisation runs a proprietary warehouse and is evaluating a lakehouse. Leadership asks what they gain architecturally by moving to open table formats on object storage. What is the strongest answer?
• A team writing directly to object storage relies on file naming - a final suffix - to signal that a batch is complete. What does an open table format provide instead?
• A lakehouse holds seven years of transaction history, of which only the last quarter is queried regularly. How should the data be placed?
• An architect asks why watsonx.data uses object storage as its primary storage layer rather than a high-performance shared filesystem. What is the strongest justification?
• A team stores its data as raw Parquet files in a bucket with no table metadata, and queries them by listing paths. What capability do they lose compared with using an open table format?
• A source system adds a new column and the downstream team needs it in the lakehouse table without rebuilding history. Which table-format capability addresses this?
• Why is keeping all historical data on the fastest available storage tier a poor default?
• A developer proposes encoding the database password into the object key prefix so that ingestion scripts can read it back at runtime without a separate configuration store. How should this be handled?
• A proposal creates a separate data mart for each query engine so that every engine has data laid out optimally for it. What is the objection?
• What problem arises if each query engine is allowed to rewrite the underlying files of a shared table independently?
• A cost-reduction exercise moves the tables underpinning daily operational dashboards to cold archival storage. What is the likely outcome?
• A stakeholder objects that object storage cannot be used for a SQL platform because SQL requires a database. What is the correct response?
• Which layering correctly describes a data lakehouse?
• A team proposes disabling metadata tracking on their tables to reduce write overhead. What do they lose?
• Why is choosing storage tiers based on file extension unsound?
• A design places lakehouse data on object storage and states that access will be controlled entirely through bucket permissions granted to each analyst. What is the flaw?
• A customer asks whether adopting a lakehouse means abandoning SQL and BI tooling in favour of a file-based workflow. What is the correct response?
• Which set of properties is the reason a lakehouse uses an open table format rather than plain files on object storage?
• Which requirement most directly forces data to be retained even though it is almost never queried?
• Which combination correctly describes how lakehouse data on object storage is made usable and safe?
• Why is a catalog necessary in a lakehouse when the data files themselves already contain schema information?
• While a long analytical query is running, a pipeline commits new rows to the same table. What does the table format guarantee for the running query?
• Which set of factors should drive storage tier decisions in a lakehouse?
• A team is deciding how to organise objects within a lakehouse bucket. Which consideration is most relevant to query performance?
• A team asks whether it is acceptable to keep some data in the lakehouse and continue running an existing warehouse for other workloads. How should this be assessed?
• What is the practical consequence of relying on a naming convention such as a date suffix instead of table-format partitioning and metadata?
• When older partitions of a table are moved to a lower-cost tier, what happens to queries that reference them?
• During a review, someone notes that object storage offers eleven nines of durability and concludes that no backup or retention strategy is needed for lakehouse tables. Is this reasoning sound?
• What does storing lakehouse data in an open table format most directly protect an organisation against?
• A batch load fails halfway through writing to a table managed by an open table format. What do readers see?
• Which statement about storage tiering in a lakehouse is correct?
• Which statement best describes the role of object storage in a watsonx.data deployment?
• Which combination best describes the lakehouse approach to serving multiple analytical workloads?
• Which statement about table formats in a lakehouse is correct?