C1000-190 Sample Questions & Answers
Core lakehouse concepts and architecture carry the most weight, just ahead of watsonx.data's components and multi-engine deployment choices, integrating and ingesting data, day-to-day operation, and predictive versus exploratory analytics use cases.
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- Question 1BeginnerSelect 2
Data Lakehouse Fundamentals · Cost, openness, and governance benefits
Which TWO of the following are core benefits of utilizing a data lakehouse architecture compared to maintaining separate data lakes and data warehouses? (Select TWO)
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Correct answers: A, D
A lakehouse reduces data duplication by maintaining a single source of truth in object storage, eliminating the need to copy data from a lake into a warehouse for analytics.
A key benefit of a lakehouse is its ability to support diverse workloads—from traditional SQL-based BI to advanced data science and ML—on the same underlying data platform without silos.
- Question 2Intermediate
Data Lakehouse Fundamentals · Lakehouse architectural layers and components
True or False: In a data lakehouse architecture utilizing open table formats like Apache Iceberg, schema evolution (such as adding, renaming, or dropping columns) requires a full rewrite of the underlying data files.
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Correct answer: B
False. One of the primary benefits of open table formats like Apache Iceberg is in-place schema evolution. Changes to the schema are recorded in the metadata layer, meaning the underlying Parquet or ORC data files do not need to be rewritten when columns are added, dropped, or renamed.
- Question 3Beginner
Data Lakehouse Fundamentals · Object storage fundamentals for a lakehouse
When configuring the storage layer for a watsonx.data implementation, which protocol must the underlying Cloud Object Storage support to ensure seamless integration?
Select the correct protocol to complete the sentence: The object storage must be _____-compatible.
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Correct answer: B
watsonx.data uses the S3 API standard for connecting to object storage. Whether using IBM Cloud Object Storage, AWS S3, or an on-premises object store like Ceph, it must be S3-compatible to integrate properly as the storage layer.
- Question 4Intermediate
Data Lakehouse Fundamentals · Lakehouse architectural layers and components
During a performance review, an engineer notices that queries against the data lakehouse are slower than expected. Upon investigation, they find that the metadata catalog is experiencing severe latency.
What role does the metadata/catalog layer play in a lakehouse architecture that makes it critical for query performance?
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Correct answer: B
The metadata layer (like Hive Metastore or Iceberg catalogs) is crucial because it tracks exactly where files are located in object storage and holds statistics about the data. This allows the query engine to perform 'file pruning'—skipping irrelevant files during query execution, which vastly improves performance. If the catalog is slow, query planning bottlenecks.
- Question 5Beginner
Data Lakehouse Fundamentals · Hot, warm, and cold data tiering strategies
A systems administrator is mapping out storage tiers for a new watsonx.data deployment. They have a dataset consisting of application logs from the previous quarter. These logs are queried occasionally for compliance audits but do not require sub-second response times.
Which storage tier classification best describes this dataset?
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Correct answer: B
Warm data is typically accessed less frequently than hot data (which requires high performance/sub-second responses) but more frequently than cold data (which is strictly archival). Occasional queries for audits where slight latency is acceptable perfectly describe the warm tier.
- Question 6Intermediate
Data Lakehouse Fundamentals · Data lakehouse definition and relationship to data lakes and data warehouses
A traditional data warehouse typically enforces 'schema-on-write', requiring data to be transformed before loading. How does a data lakehouse handle schema enforcement differently?
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Correct answer: B
A lakehouse balances flexibility and structure. While raw data can be dumped into object storage (schema-on-read flexibility), the lakehouse utilizes an open table format (like Iceberg) as a metadata layer to enforce schema rules, data types, and structural integrity without requiring rigid transformation before initial storage.
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