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Question 61:
Which of the following is the primary use case for Databricks Delta Lake’s MERGE operation?
A) To delete outdated data from a Delta table based on a condition.
B) To insert, update, or delete data in a Delta table based on a condition, in a single atomic operation.
C) To improve the performance of batch jobs by merging small files into larger ones.
D) To optimize the schema of a Delta table.
Answer: B) To insert, update, or delete data in a Delta table based on a condition, in a single atomic operation.
Explanation:
A) To delete outdated data from a Delta table based on a condition.
Deleting outdated data can be done with the DELETE command in Delta Lake, but the MERGE operation is more versatile. MERGE supports inserts, updates, and deletes in one atomic operation based on conditions. So, while deleting is part of the MERGE functionality, it is not the primary use case.
B) To insert, update, or delete data in a Delta table based on a condition, in a single atomic operation.
This is the correct answer. The MERGE statement in Delta Lake enables you to perform insertions, updates, and deletions on a Delta table in a single transaction, based on a matching condition (like joining with another dataset). This is highly useful for handling data changes in ETL pipelines and maintaining data consistency during concurrent operations.
C) To improve the performance of batch jobs by merging small files into larger ones.
This is the function of the OPTIMIZE command, not MERGE. The OPTIMIZE command helps reduce the overhead caused by small files and improves query performance by merging smaller Parquet files into larger ones.
D) To optimize the schema of a Delta table.
Schema optimization is done through schema evolution or enforcement in Delta Lake, but the MERGE operation does not optimize the schema itself.
Question 62:
Which feature of Databricks allows for automated execution of a sequence of jobs based on a schedule or other conditions?
A) Delta Lake
B) Databricks Jobs
C) Cluster Pools
D) MLflow
Answer: B) Databricks Jobs
Explanation:
A) Delta Lake is a storage layer that brings ACID transactions to Apache Spark and enables features like schema evolution and time travel. It does not handle the automated execution of jobs or workflows.
B) Databricks Jobs is the correct answer. Databricks Jobs enable the automated execution of data pipelines or workflows. You can schedule these jobs to run periodically or trigger them based on specific events or conditions. Databricks Jobs allow you to manage the scheduling, dependencies, and execution of multiple tasks in a streamlined manner.
C) Cluster Pools are designed to reduce the time it takes to start up a cluster by reusing pre-configured resources. They help improve the efficiency of cluster resource allocation, but they are not designed to handle job execution or scheduling.
D) MLflow is used to manage the lifecycle of machine learning models (including tracking experiments, versioning models, and model deployment). While MLflow can be integrated into Databricks Jobs, it does not itself manage job scheduling or execution.
Question 63:
Which of the following is the best approach to manage large-scale data processing in Databricks?
A) Use a single large cluster to handle all data processing tasks.
B) Use Databricks Cluster Pools to reduce cluster startup time and enhance job execution speed.
C) Use multiple smaller clusters to run independent jobs in parallel, and manually manage dependencies.
D) Use auto-scaling clusters to dynamically adjust the number of nodes based on workload demands.
Answer: D) Use auto-scaling clusters to dynamically adjust the number of nodes based on workload demands.
Explanation:
A) Using a single large cluster may seem like a good idea for handling all tasks, but it can lead to inefficiencies. A large cluster might be underutilized for some tasks, and overloaded for others. It’s not flexible enough to adjust to different workloads and can result in resource contention.
B) While Cluster Pools can reduce the startup time for clusters by reusing existing resources, they don’t provide automatic scaling based on workload demand. Cluster Pools are primarily useful for cost savings and performance optimization during cluster provisioning, but they don’t manage workload demands automatically.
C) Managing multiple clusters manually can be complex and inefficient, especially when you have many interdependent jobs. It also introduces the overhead of managing cluster configurations and scaling. Auto-scaling clusters provide a more streamlined solution.
D) This is the best approach. Auto-scaling clusters automatically adjust the number of nodes based on the size of the workload, ensuring that the job gets the resources it needs when it needs them. This leads to efficient use of resources and cost savings, as clusters scale up when needed and scale down when the workload decreases.
Question 64:
Which of the following is a key benefit of using Databricks Delta for streaming data processing?
A) It automatically converts streaming data into batch data for easier processing.
B) It provides transactional consistency by allowing both batch and streaming workloads to access the same table without conflicts.
C) It only supports real-time data processing and does not support batch processing.
D) It eliminates the need for partitioning in Delta Lake tables.
Answer: B) It provides transactional consistency by allowing both batch and streaming workloads to access the same table without conflicts.
Explanation:
A) Delta Lake does not convert streaming data into batch data. Instead, it allows for real-time processing of data with the ability to handle both batch and streaming workloads simultaneously. This is one of the key advantages of Delta Lake for unified data processing.
B) This is the correct answer. One of the key features of Databricks Delta is its ability to provide transactional consistency in streaming and batch processing. Delta Lake handles ACID transactions, meaning that both batch and streaming jobs can read from and write to the same Delta table without conflicts, ensuring data consistency and reliability.
C) Delta Lake supports both real-time (streaming) and batch processing. It does not limit you to one type of workload; instead, it provides flexibility to use both types on the same data. This is a key advantage for many modern data architectures.
D) Partitioning in Delta Lake is still important for optimizing query performance, especially when dealing with large datasets. However, Delta Lake does not eliminate the need for partitioning, although it does help optimize read and write operations by efficiently managing files and ensuring consistency in both streaming and batch jobs.
Question 65:
In Databricks, what is the most effective way to optimize a Spark job that is running slowly due to excessive shuffling?
A) Increase the size of the cluster to add more compute resources.
B) Use broadcast joins to reduce the amount of data shuffled during joins.
C) Use Databricks Auto-scaling to adjust the number of nodes dynamically.
D) Split the job into smaller tasks and run them sequentially.
Answer: B) Use broadcast joins to reduce the amount of data shuffled during joins.
Explanation:
A) While increasing the size of the cluster can provide more compute resources, it doesn’t directly address the issue of shuffling. Shuffling occurs when Spark has to exchange data between nodes, and this is generally caused by inefficient joins, not necessarily a lack of compute resources. Simply adding more nodes may not solve the problem of excessive shuffling.
B) Broadcast joins are a powerful technique to optimize joins in Spark when one of the tables is small enough to fit into memory. By broadcasting the smaller table to all worker nodes, Spark can avoid the need for shuffling the larger table, significantly reducing the amount of data moved around and improving performance.
C) While auto-scaling can adjust the cluster size based on workload demands, it does not directly solve the problem of excessive shuffling. The root cause of shuffling often lies in inefficient join strategies or other factors, and auto-scaling only helps with resource allocation, not query optimization.
D) Splitting a job into smaller tasks may lead to longer total execution times, as each task may introduce its own shuffling and processing delays. Broadcast joins and shuffling optimizations are better strategies for reducing overhead and improving overall performance.
Question 66:
Which of the following is the most effective way to prevent data duplication when performing upserts in a Delta Lake table?
A) Use partitioning to ensure that only unique data is inserted.
B) Use the MERGE operation with a condition to match and update records based on a unique identifier.
C) Use the INSERT INTO statement without a condition to overwrite existing records.
D) Increase the number of partitions to minimize data duplication.
Answer: B) Use the MERGE operation with a condition to match and update records based on a unique identifier.
Explanation:
A) While partitioning can improve query performance by limiting the amount of data read, it does not directly prevent data duplication during upserts. Partitioning is a way to organize data physically in the Delta table, but it doesn’t handle how data is written, especially in upsert scenarios.
B) This is the correct answer. The MERGE operation in Delta Lake is specifically designed for upsert operations. It allows you to perform inserts, updates, and deletes based on a matching condition (e.g., using a unique identifier). This ensures that records are updated if they already exist or inserted if they do not, effectively preventing data duplication.
C) The INSERT INTO statement without a condition would insert new data into the table but would not handle upserts (i.e., it won’t update existing records). This could lead to duplicate entries if the same data is inserted multiple times.
D) Increasing the number of partitions can improve query performance but does not prevent duplicate records from being inserted during an upsert operation. Data duplication is handled by using the MERGE operation, not by partitioning the data.
Question 67:
In a Databricks environment, which of the following should you do to optimize a Spark job that uses shuffles in joins and has high execution time?
A) Increase the spark.sql.shuffle.partitions configuration to a higher value.
B) Use broadcast joins when one of the tables is small enough to fit in memory.
C) Use more partitions for the input data to distribute the shuffle load.
D) Use non-partitioned joins to reduce the number of shuffle operations.
Answer: B) Use broadcast joins when one of the tables is small enough to fit in memory.
Explanation:
A) While increasing spark.sql.shuffle.partitions may reduce the number of tasks during a shuffle, it does not necessarily improve performance. In fact, increasing this value can lead to over-partitioning and further performance degradation. The optimal number of shuffle partitions should be chosen based on the size of the data and the workload.
B) This is the correct answer. Broadcast joins are an effective way to optimize joins in Spark. If one of the tables in the join is small enough to fit in memory, broadcasting it to all worker nodes eliminates the need for shuffling, significantly improving performance. This is particularly effective when one table is much smaller than the other.
C) While partitioning can improve the performance of large datasets, increasing the number of partitions without a proper strategy can lead to inefficient shuffling. It can create too many small tasks and add overhead rather than optimizing performance.
D) Non-partitioned joins may actually increase the shuffle load, as Spark will need to redistribute the data across different nodes. Partitioned joins can be more efficient when the data is properly partitioned, so avoiding partitioning is not a recommended strategy for reducing shuffle operations.
Question 68:
What is the main advantage of using Delta Lake for time travel?
A) It allows you to query data as it existed at a specific point in time.
B) It enables automatic recovery from data corruption without any user intervention.
C) It prevents data from being overwritten, ensuring data is always in its latest form.
D) It integrates with Apache Kafka to enable real-time data streaming.
Answer: A) It allows you to query data as it existed at a specific point in time.
Explanation:
A) This is the correct answer. Time travel in Delta Lake is a feature that allows you to query historical versions of data. This can be done by using the version or timestamp to specify the state of the data at a particular point in time. This is especially useful for auditing, data recovery, and debugging issues in production data pipelines.
B) While Delta Lake provides robust data consistency and recovery mechanisms, it is not a tool for automatic recovery from all types of data corruption. Time travel can help restore data from a previous version if corruption occurs, but users may need to manually specify the previous state using the time travel feature.
C) This is incorrect because Delta Lake allows for data overwriting. The advantage of Delta Lake is its ability to manage versioned data and provide time travel, not that it prevents overwriting. The MERGE and INSERT operations in Delta Lake can still modify the data.
D) While Delta Lake can handle both batch and streaming workloads, its time travel feature is not specifically related to Apache Kafka integration. Kafka is a real-time streaming service, but Delta Lake provides time travel by versioning data in its storage layer.
Question 69:
What is the purpose of Databricks Cluster Pools?
A) To improve the performance of highly parallel jobs.
B) To reduce the time spent on provisioning clusters by reusing pre-configured resources.
C) To handle real-time streaming data more efficiently.
D) To manage job dependencies and execution schedules.
Answer: B) To reduce the time spent on provisioning clusters by reusing pre-configured resources.
Explanation:
A) While Cluster Pools can improve performance indirectly by reducing cluster start times, they are not specifically designed for improving the performance of parallel jobs. They primarily help with resource provisioning efficiency.
B) This is the correct answer. Cluster Pools are designed to reduce the time spent on provisioning clusters in Databricks. By reusing pre-configured worker nodes that are already running, Cluster Pools make it possible to quickly allocate resources and improve the speed at which Databricks clusters are available for running jobs.
C) Cluster Pools are not specifically designed for real-time streaming. They are used for reducing the time spent in provisioning clusters, but real-time streaming data handling in Databricks is typically done using structured streaming and is not related to Cluster Pools.
D) Job dependencies and scheduling are handled by Databricks Jobs rather than Cluster Pools. Cluster Pools only optimize cluster provisioning and are not responsible for managing job dependencies.
Question 70:
Which of the following actions will help improve Spark job performance when working with large datasets in Databricks?
A) Use CACHE on small tables to reduce query time.
B) Use Delta Lake’s OPTIMIZE command to compact small files before running the job.
C) Increase the number of spark.sql.shuffle.partitions to a higher number.
D) Use JOIN without partitioning to minimize data shuffling.
Answer: B) Use Delta Lake’s OPTIMIZE command to compact small files before running the job.
Explanation:
A) Caching small tables can improve performance by keeping them in memory, but it may not be effective for larger datasets or when there is a lot of data shuffle. Caching is typically used to speed up repeated queries on small datasets.
B) This is the correct answer. The OPTIMIZE command in Delta Lake compacts small files into larger ones, reducing the overhead caused by file I/O. This is especially helpful for large datasets and improves query performance by reducing the number of files Spark has to read.
C) While increasing the number of shuffle.partitions can improve parallelism, it is not always the best approach. A higher number of partitions may lead to too many small tasks and introduce overhead. It’s more efficient to adjust the partitioning strategy based on the data size and the job requirements.
D) Partitioned joins are more efficient when the data is partitioned based on the join keys, as it minimizes the shuffle. Not using partitions may cause excessive shuffling, which degrades performance.
Question 71:
Which of the following is a key feature of Delta Lake’s ACID transactions in Databricks?
A) Ensures that all operations (insert, update, delete) are performed atomically and consistently.
B) Allows for real-time data streaming without requiring schema enforcement.
C) Automatically partitions data based on the number of nodes in the cluster.
D) Enforces data compression for both streaming and batch jobs.
Answer: A) Ensures that all operations (insert, update, delete) are performed atomically and consistently.
Explanation:
A) This is the correct answer. Delta Lake brings ACID transactions to Apache Spark, meaning it ensures that all operations (insert, update, delete) are atomic (executed as a single unit of work), consistent (maintains integrity of the data), isolated (no interference between transactions), and durable (changes are persistent and recoverable).
B) While Delta Lake supports streaming workloads, schema enforcement is a key feature of Delta Lake. Schema enforcement ensures that the data being written to a Delta table adheres to the specified schema. Real-time data streaming in Delta Lake is supported but with schema enforcement and validation.
C) Delta Lake does not automatically partition data based on the number of nodes in the cluster. Partitioning is typically done based on data characteristics (e.g., date or category), and the number of partitions is configurable, but it does not directly depend on the cluster size.
D) Delta Lake does not enforce compression. However, data compression can be applied to Delta tables by using formats like Parquet. It’s not an automatic enforcement, and users can configure compression settings.
Question 72:
What is the purpose of using Delta Lake’s OPTIMIZE command in Databricks?
A) To increase the parallelism of batch jobs by splitting large files into smaller partitions.
B) To reorganize and compact small files into larger ones, improving read performance.
C) To migrate data from Delta Lake tables to Apache Hudi for more efficient storage.
D) To merge duplicate records in a Delta table during ETL processing.
Answer: B) To reorganize and compact small files into larger ones, improving read performance.
Explanation:
A) The OPTIMIZE command does not split large files into smaller partitions. Instead, it helps compact small files into larger ones. Splitting files is generally done during partitioning or file splitting processes, but not through the OPTIMIZE command.
B) This is the correct answer. The OPTIMIZE command in Delta Lake is used to compact small files into larger ones. This can improve read performance by reducing the overhead caused by reading many small files. It’s particularly important for workloads that involve frequent queries or streaming data.
C) This is incorrect. The OPTIMIZE command does not migrate data to other formats or storage systems. Delta Lake and Apache Hudi are both storage frameworks for managing large-scale data, but they are separate technologies and OPTIMIZE is specific to Delta Lake.
D) While Delta Lake’s MERGE operation is used to handle upserts and deal with duplicate records, the OPTIMIZE command is focused on file optimization and does not directly merge duplicate records.
Question 73:
Which of the following best describes Databricks’ Auto-scaling clusters?
A) They automatically adjust the cluster size based on the number of jobs running in the workspace.
B) They automatically increase the number of nodes when the workload grows, and scale down when the workload decreases.
C) They scale the number of executors on each node depending on the number of partitions in the Spark job.
D) They automatically adjust the number of jobs based on available memory and CPU resources.
Answer: B) They automatically increase the number of nodes when the workload grows, and scale down when the workload decreases.
Explanation:
A) While Databricks Auto-scaling adjusts the cluster size based on workload, it does not specifically scale based on the number of jobs running in the workspace. Instead, it scales based on the workload demand for resources (e.g., CPU, memory, etc.).
B) This is the correct answer. Databricks Auto-scaling clusters dynamically adjust the number of worker nodes based on the workload. If the workload increases (e.g., more data to process), Databricks will add more nodes to the cluster. Similarly, when the workload decreases, Databricks will scale down the cluster to save on resources.
C) Auto-scaling adjusts the number of worker nodes in the cluster, but it does not directly control the number of executors on each node. Executors are typically managed based on Spark configuration and job settings rather than by auto-scaling.
D) Databricks Auto-scaling focuses on scaling the number of nodes in the cluster, not the number of jobs. The number of jobs is managed by the Databricks Jobs scheduler and does not depend on the auto-scaling feature.
Question 74:
What is the primary benefit of using Databricks Cluster Pools?
A) They allow you to run jobs faster by adding more compute resources to a cluster.
B) They reduce the time it takes to provision clusters by reusing pre-configured resources.
C) They automatically optimize data partitioning during job execution.
D) They allow you to manage multiple independent clusters in a single job pipeline.
Answer: B) They reduce the time it takes to provision clusters by reusing pre-configured resources.
Explanation:
A) While Cluster Pools can improve the overall performance of job provisioning, they do not directly add more compute resources to a cluster. They are focused on reducing cluster start times rather than improving execution speed by adding resources.
B) This is the correct answer. Cluster Pools reduce the overhead of provisioning new clusters by maintaining a pool of pre-configured worker nodes. These nodes are ready to be assigned to jobs, which significantly reduces the time it takes to start a cluster and helps optimize resource usage.
C) Cluster Pools do not automatically optimize data partitioning. Partitioning is generally done during the data processing phase (e.g., when working with large datasets in Spark), not during cluster provisioning.
D) Cluster Pools help optimize the provisioning of clusters, but they are not used for managing multiple independent clusters in a pipeline. Managing multiple clusters is done through Databricks Jobs, where you can define job dependencies and scheduling.
Question 75:
In Databricks, what is the most efficient way to perform real-time analytics on streaming data?
A) Use Delta Lake with structured streaming to process and analyze data as it arrives in near real-time.
B) Use Apache Kafka to stream data and store the results in Delta tables for periodic batch processing.
C) Use Databricks Jobs to run batch jobs on streaming data at regular intervals.
D) Use Apache Hive to store and manage streaming data for real-time querying.
Answer: A) Use Delta Lake with structured streaming to process and analyze data as it arrives in near real-time.
Explanation:
A) This is the correct answer. Delta Lake provides robust support for structured streaming, allowing you to process and analyze streaming data in real-time while also taking advantage of ACID transactions, schema enforcement, and time travel. It allows you to handle real-time analytics while maintaining the reliability of batch processing.
B) While Apache Kafka is a powerful tool for streaming data, Delta Lake’s structured streaming offers a more efficient way to process streaming data directly. Storing results in Delta tables for batch processing is not the most efficient method for real-time analytics.
C) Databricks Jobs is more suitable for batch processing, not real-time streaming analytics. While you can schedule batch jobs to process streaming data, this approach introduces latency and does not offer real-time performance.
D) Apache Hive is primarily designed for batch processing and is not optimized for real-time streaming data. Delta Lake with structured streaming is a more efficient and scalable solution for real-time analytics.
Question 76:
A job task fails due to “Cluster terminated unexpectedly.” What is the MOST likely cause?
A) The notebook contains a syntax error, so the cluster automatically shuts down.
B) The cluster reached its maximum execution time based on the job’s timeout setting.
C) The cluster node failed a health check and was replaced, interrupting the job.
D) The job was paused due to low DBU quota limits.
E) The job tried to write to a non-mounted volume.
Answer: C) The cluster node failed a health check and was replaced, interrupting the job.
Explanation:
A) The notebook contains a syntax error, so the cluster automatically shuts down.A syntax error in a notebook will only cause the execution of the notebook to fail. It will not cause the entire cluster to shut down. The cluster will continue running, and other tasks or jobs can proceed. The error message typically indicates issues with the specific code, not with the cluster as a whole. Therefore, this option is incorrect.
B) The cluster reached its maximum execution time based on the job’s timeout setting.When a cluster runs beyond its designated timeout, it would result in a timeout error, not a “cluster terminated unexpectedly” message. Timeout errors are specifically indicated as such, and the cluster would remain active unless manually stopped or reconfigured. Therefore, this option is incorrect.
C) The cluster node failed a health check and was replaced, interrupting the job.This is the correct answer. If a worker node in the cluster fails a health check or crashes due to issues like resource exhaustion, hardware failure, or connectivity problems, the system may automatically replace the faulty node to maintain the integrity of the cluster. When this happens, any job that is running on the affected node will be interrupted. Databricks will then report the error as “Cluster terminated unexpectedly.” This is a common scenario when there are issues at the node level (e.g., memory leaks, CPU overload) that require Databricks to replace the problematic node, causing the job to fail.
D) The job was paused due to low DBU quota limits.DBU (Databricks Unit) quota limits are primarily concerned with managing resources for compute power, and they could delay the start of a job or cause jobs to be queued. However, they would not directly terminate a running cluster or cause the “Cluster terminated unexpectedly” error. Instead, a different error related to quota limits would be shown. Hence, this option is incorrect.
E) The job tried to write to a non-mounted volume.While attempting to write to a non-mounted or unavailable volume could result in an error related to file paths or storage, it would not cause the cluster to terminate unexpectedly. The system would instead throw a path or storage-related error. Therefore, this option is incorrect.
Question 77:
You need to ingest JSON data every hour, and the schema evolves. Which option ensures the table stays in sync?
A) Use COPY INTO with schema mismatch enforcement.
B) Use Auto Loader with schema evolution enabled.
C) Use a standard readStream with a manually defined schema.
D) Use Delta Live Tables but disable schema evolution.
E) Use dbutils.fs.put for data ingestion.
Answer: B) Use Auto Loader with schema evolution enabled.
Explanation:
A) Use COPY INTO with schema mismatch enforcement.While COPY INTO can be used for data ingestion, it is not the most effective solution for handling schema evolution in a continuous or incremental fashion, especially when new fields are added over time. If a new field is introduced in the JSON schema, COPY INTO may fail or require manual intervention to adjust the schema. Schema mismatch enforcement primarily checks for issues between the source and destination schema but does not handle continuous schema evolution. Therefore, this option is incorrect.
B) Use Auto Loader with schema evolution enabled.This is the correct answer.Auto Loader is designed for efficient and scalable ingestion of data, particularly from cloud storage such as AWS S3 or Azure Blob Storage. It supports incremental processing, meaning it can efficiently ingest new data as it arrives. Furthermore, when schema evolution is enabled, Auto Loader automatically adapts to changes in the schema of the incoming JSON files. For example, if new columns are added to the JSON data, Auto Loader can automatically detect and incorporate them into the target Delta table without needing manual schema updates. This makes Auto Loader with schema evolution the best choice for continuously evolving JSON data.
C) Use a standard readStream with a manually defined schema.Using a standard readStream with a manually defined schema may work initially, but it fails to account for schema evolution. If the incoming JSON schema changes (e.g., a new column is added), you would need to manually update the schema to reflect these changes. This requires constant manual intervention, making it a less scalable and less flexible solution. Therefore, this option is incorrect.
D) Use Delta Live Tables but disable schema evolution.Delta Live Tables provides an easy way to manage ETL pipelines and ensure data quality, but if you disable schema evolution, you are explicitly preventing the system from adapting to changes in the schema. Given that the schema of your JSON data is evolving, disabling schema evolution would contradict the requirement of automatically handling new fields. Hence, this option is incorrect.
E) Use dbutils.fs.put for data ingestion.The dbutils.fs.put command is used for writing small amounts of data to DBFS (Databricks File System), but it is not intended for continuous data ingestion or streaming. It lacks support for automatic schema management, incremental ingestion, and schema evolution, making it unsuitable for this scenario. Therefore, this option is incorrect.
Question 78:
What happens when two simultaneous write operations modify overlapping data ranges in Delta Lake?
A) Databricks automatically merges both writes.
B) The second operation fails with an “OPTIMISTIC CONCURRENCY EXCEPTION.”
C) Both writes succeed, but one is marked as stale.
D) Delta retries until transaction succeeds.
E) The table becomes corrupted until a VACUUM is run.
Answer: B) The second operation fails with an “OPTIMISTIC CONCURRENCY EXCEPTION.”
Explanation:
A) Delta Lake does not automatically merge conflicting writes because that could result in data corruption. Automatic merging of conflicting writes could lead to inconsistent data, which is why Delta Lake uses a more controlled mechanism to handle concurrent writes. Therefore, this option is incorrect.
B) Delta Lake uses Optimistic Concurrency Control (OCC) to handle conflicts between simultaneous write operations. When two operations attempt to modify the same data ranges, the first operation succeeds. The second operation detects a conflict and fails, triggering an “OPTIMISTIC CONCURRENCY EXCEPTION.” This mechanism ensures that only one write succeeds, preserving transactional consistency and preventing data corruption. By preventing conflicting writes, Delta Lake guarantees data integrity.
C) Delta Lake does not allow conflicting writes to succeed, so this scenario is not possible. If two writes conflict, one will fail entirely to prevent inconsistent data. Delta doesn’t mark writes as “stale” in this situation. Therefore, this option is incorrect.
D) Delta Lake does not automatically retry transactions that conflict. Instead, the conflicting operation will fail immediately, and the user can handle the conflict (for example, by retrying the operation or handling the exception). Therefore, this option is incorrect.
E) Delta Lake ensures data consistency and does not allow a table to become corrupted due to concurrency issues. Conflicting writes are blocked, and the table remains in a consistent state. A VACUUM operation is used for cleaning up obsolete data but is unrelated to concurrency conflicts. Therefore, this option is incorrect.
Question:79
A streaming job shows high latency and increasing small files. What is the best fix?
A) Switch to batch writes.
B) Enable schema inference for every micro-batch.
C) Compact files using OPTIMIZE and ZORDER.
D) Manually delete old checkpoint files.
E) Increase shuffle partitions.
Answer: C) Compact files using OPTIMIZE and ZORDER.
Explanation:
A) Switching to batch writes would break the requirement for real-time streaming, which is the main purpose of the streaming job. Batch writes process data in bulk, but they don’t address the issue of high latency or small file creation in streaming scenarios. Therefore, this option is incorrect.
B) Enabling schema inference for every micro-batch can actually increase latency. Each micro-batch would need to infer the schema of incoming data, which can add overhead. This is counterproductive to reducing latency. Therefore, this option is incorrect.
C) Compact files using OPTIMIZE and ZORDER. This is the correct answer.
In streaming jobs, especially those using Delta Lake, many small files can accumulate over time. This happens because every micro-batch typically writes a new set of files, leading to a large number of small files. These small files increase the overhead for reads and affect performance, particularly with latency.
OPTIMIZE is a Delta Lake operation that compacts small files into larger, more efficient ones, improving read and write performance.
ZORDER is an additional optimization that helps by co-locating data based on column values, further improving performance for queries that filter on those columns.
Together, OPTIMIZE and ZORDER help reduce the number of small files and improve the overall performance of the streaming job, addressing both latency and file size issues. Therefore, this option is correct.
D) Deleting old checkpoint files manually can be dangerous and might corrupt the streaming job. Checkpoints are critical for maintaining the state of the streaming job and recovering from failures. Deleting them would compromise the integrity of the streaming pipeline. Therefore, this option is incorrect.
E) Increasing shuffle partitions could improve performance in terms of parallelism, but it does not directly address the issue of accumulating small files. The small file issue is more related to how data is written and compacted, not how partitions are shuffled. Therefore, this option is incorrect.
Question: 80
You want analysts to see masked customer data while engineers see full values. What should you use?
A) Unity Catalog dynamic views with masked columns.
B) Table ACLs with row-level filtering only.
C) Grant SELECT permission but revoke DESCRIBE.
D) Create separate tables for each user group.
E) Rewrite the table with hashed values using Auto Loader.
Answer: A) Unity Catalog dynamic views with masked columns.
Explanation:
A) Unity Catalog in Databricks allows you to manage data access and security in a unified way. With dynamic views, you can implement column-level security, such as masking specific columns based on user roles. For example, analysts can be granted access to a view where sensitive customer data is masked (e.g., showing only the last four digits of a customer’s phone number), while engineers can access the same data but see the full, unmasked values. This approach provides role-based access control and conditional visibility, ensuring that the right users see the right level of data without duplicating or creating unnecessary tables. Therefore, this option is correct.
B) Row-level filtering restricts access to entire rows based on certain conditions (e.g., user role), but it does not handle column-level security or data masking. While row-level filtering might be helpful for some cases, it doesn’t solve the problem of showing masked data in some contexts and unmasked data in others. Therefore, this option is incorrect.
C) The DESCRIBE command reveals metadata about the table, but revoking DESCRIBE doesn’t provide a solution for masking data at the column level. You’d still have to control access to specific data in a more granular way, and this approach doesn’t specifically address the need for data masking. Therefore, this option is incorrect.
D This approach is inefficient and cumbersome. It requires maintaining duplicate data—one version with masked columns for analysts and another with unmasked data for engineers. This method increases maintenance overhead and doesn’t provide dynamic control over data visibility based on roles. Therefore, this option is incorrect.
E) Hashing the data would permanently alter it and destroy the original values, which is unacceptable when you need to preserve full data access for engineers. Masking data dynamically based on user roles is a far better approach than permanently altering the data. Therefore, this option is incorrect.
