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| Section | Weight | Objectives |
|---|---|---|
| Data Transformation, Cleansing, and Quality | ~12% | - Enforce data quality and quarantine bad data - Apply advanced Spark transformations |
| Monitoring, Logging, and Troubleshooting | ~8% | - Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables |
| Cost and Performance Optimization | ~13% | - Leverage system tables and observability tools - Optimize queries, clusters, and storage |
| Developing Code for Data Processing using Python and SQL | ~22% | - Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Manage dependencies, libraries, and UDFs |
| Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| CI/CD, Testing, and Deployment | ~6% | - Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API |
| Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
| Streaming Workloads and Change Data Capture | ~11% | - Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics |
| Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
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NEW QUESTION # 151
An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?
Answer: B
Explanation:
This is the correct answer because it efficiently updates the account current table with only the most recent value for each user id. The code filters records in account history using the last updated field and the most recent hour processed, which means it will only process the latest batch of data. It also filters by the max last login by user id, which means it will only keep the most recent record for each user id within that batch. Then, it writes a merge statement to update or insert the most recent value for each user id into account current, which means it will perform an upsert operation based on the user id column.
NEW QUESTION # 152
A data engineer is working on a Databricks notebook that requires several third-party Python libraries. Some of these are available on PyPI, while others are custom-developed and stored as local.wheel (.whl) and source (.tar.gz) files in an S3 bucket. The goal is to ensure all dependencies are installed and correctly available across multiple jobs running on any automated cluster in a Unity Catalog-enabled workspace. The engineer needs to install the required dependencies in a way that ensures a consistent environment setup across interactive notebooks and jobs and complies with workspace security policies (no internet access). Which approach should the engineer use to install and manage these dependencies while also ensuring reproducibility and compliance?
Answer: C
Explanation:
Packaging dependencies as wheel files and managing them through Databricks-supported artifacts ensures consistent, reproducible environments across interactive and job clusters.
Storing the wheels in Workspace Files or Unity Catalog Volumes complies with no-internet security constraints, while installing them via cluster libraries or Asset Bundles standardizes dependency management across all jobs and pipelines.
NEW QUESTION # 153
The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer's suggestion is correct?
Answer: A
Explanation:
Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lake collects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping data on free text fields. Moreover, Delta Lake collects statistics on the first 32 columns by default, which may not include the review column if the table has more columns. Therefore, the junior data engineer's suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data.
NEW QUESTION # 154
A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data. They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline. Which configuration should the team use when writing data to the Delta table?
Answer: D
Explanation:
Setting mergeSchema to true allows Delta Lake to automatically evolve the table schema by incorporating new columns from the source data during writes. This enables append-only pipelines to handle schema changes seamlessly in both batch and streaming workloads without breaking the pipeline.
NEW QUESTION # 155
A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?
Answer: A
Explanation:
Auto Loader is designed to efficiently and incrementally process new files as they arrive in cloud object storage. It provides scalable file discovery, supports schema inference and evolution, and minimizes overhead compared to traditional batch or manual streaming approaches.
NEW QUESTION # 156
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