CCNA Pde Designing Data Systems Questions

A data pipeline is built with Cloud Dataflow that reads from Pub/Sub, applies transformations, and writes to BigQuery. The pipeline is experiencing high latency and occasional data loss during worker failures. The engineer wants to improve reliability and performance. Which two actions should they take?

A company wants to use BigQuery materialized views to accelerate queries on a table that is updated every hour. Which statement about materialized views is true?

A data engineer needs to design a BigQuery dataset for a multi-team environment. Each team should have read access only to specific tables, and the data must be protected from accidental deletion. Which THREE steps should they take?

A data engineer needs to create a BigQuery table that is optimized for queries that filter on a 'customer_id' column and sort by 'transaction_date'. The table will be used for interactive analysis. Which combination of table features should be used?

You need to choose a messaging service for a real-time streaming application that requires low cost and can tolerate occasional message loss. Which service is MOST suitable?

You are migrating on-premises Hadoop jobs to Google Cloud. The existing jobs use Spark for ETL and Hive for querying. You want to minimize changes to the existing code and maintain the ability to use Hive queries with the same metastore across multiple clusters. Which service combination should you use?

You need to transform and clean messy CSV data using a visual interface without writing code. The transformation should be scheduled to run weekly. Which Google Cloud service should you use?

A company uses Cloud Pub/Sub for event ingestion. They want to ensure that if a subscriber fails to process a message after 5 attempts, the message is sent to a dead letter topic for analysis. Which TWO configurations are needed?

A financial services company has a BigQuery dataset containing sensitive customer data. They need to share a subset of this data (excluding PII columns) with an external analytics partner. The partner should be able to query the data using their own BigQuery account, but the company must maintain full control over the underlying table and ensure the partner cannot see or access the original table. Which approach should they use?

An organization runs periodic Apache Spark jobs on Dataproc to process data from Cloud Storage. They want to reduce costs by using preemptible instances for worker nodes. What is a key consideration when using preemptible instances in Dataproc?

You need to analyse streaming data from thousands of IoT devices, each sending temperature readings every second. You want to calculate the average temperature per device over the last 5 minutes, updating every minute. Which windowing strategy should you use in Dataflow?

A company uses Dataproc Serverless for Spark batch jobs. They notice that some jobs are failing due to out-of-memory (OOM) errors. Which configuration parameter should they adjust to allocate more memory per executor?

Your company uses Cloud Data Fusion to build ETL pipelines. You have a pipeline that reads from Cloud Storage, transforms data using a custom Wrangler recipe, and writes to BigQuery. The pipeline is failing with an error indicating that the Wrangler directive is invalid. You have verified the recipe works in the Cloud Data Fusion Studio. What is the most likely cause of the failure?

A developer wants to create a BigQuery table that automatically expires data older than 30 days to reduce storage costs. Which table design feature should be used?

A company uses BigQuery for analytics. They have a table that is queried frequently by date range. To reduce costs, they want to ensure queries only scan the relevant partitions. They also want to improve performance for queries filtering on a specific customer_id. Which table design should they use?

A data pipeline ingests streaming events into Pub/Sub. You need to guarantee that each event is processed exactly once downstream in Dataflow. Which combination of Pub/Sub and Dataflow configurations should you use?

A data team is building a near-real-time dashboard that displays aggregated metrics from Kafka topics. They want to use Pub/Sub as a managed messaging service and Dataflow for stream processing. They need to ingest data from Kafka into Pub/Sub with minimal custom code. Which THREE Google Cloud services should they use together? (Choose three.)

A data engineer is designing a real-time fraud detection system using Dataflow. The system must detect patterns across events from multiple users within a sliding window of 10 minutes. Events arrive on Pub/Sub topics per user. Which approach should they use to join the streams?

Your team is migrating a legacy batch processing system that uses Apache Spark on-premises. The migration must be completed with minimal code changes and support both batch and streaming in the future. You want to use a fully managed service. Which Google Cloud service is most appropriate?

A Dataflow pipeline processes a high-volume stream of JSON events. The pipeline has a bottleneck where a ParDo transformation performs an external API call for each element, causing high latency. Which strategy would BEST improve throughput without sacrificing correctness?

A retail company uses Dataflow to process real-time clickstream data. They need to enrich each event with customer profile data from Cloud Bigtable and session metadata from Cloud Spanner. Which two Dataflow features should they use?

Which Google Cloud service provides a fully managed, serverless Spark environment without requiring cluster provisioning?

You need to process a large Spark ML training job on a Dataproc cluster. The job is fault-tolerant and can handle occasional node failures. To reduce costs, which type of worker nodes should you use?

You are designing a Dataflow pipeline that joins two unbounded PCollections from different sources. Which transform should you use?

A data pipeline uses Dataflow to read from Pub/Sub, window messages into 1-minute fixed windows, and write to BigQuery. The pipeline occasionally has late-arriving data. How should they configure the pipeline to allow late data up to 5 minutes and then trigger a final pane?

A company needs to process data from a legacy system that outputs CSV files daily. They want to visually build transformations without writing code. Which Google Cloud service should they use?

Which BigQuery feature allows you to share query results with specific users without giving them direct access to the underlying tables?

A Dataflow streaming pipeline reads from Pub/Sub, processes events with a fixed window of 1 minute, and writes to BigQuery. Some events arrive late due to network issues. You need to ensure late events are still included in the correct window but the pipeline must not wait indefinitely. What configuration should you use?

A data pipeline using Cloud Dataflow reads from a Pub/Sub subscription that has a dead letter topic configured. Some messages are being sent to the dead letter topic. Upon investigation, the engineer finds that the messages contain valid data but are malformed according to the schema. What is the most likely reason for the messages being dead-lettered?

A company is migrating their on-premises Hadoop/Spark workloads to Google Cloud. They need a fully managed service that supports existing Spark jobs with minimal code changes, allows autoscaling, and provides integration with Cloud Storage and BigQuery. The team also wants to avoid managing cluster infrastructure and pay only for what they use. Which TWO services meet these requirements? (Choose two.)

Your company ingests millions of events per second into a Pub/Sub topic. The downstream consumer must process events with minimal latency and high throughput. However, the consumer occasionally falls behind during traffic spikes, and you need to ensure no data loss while minimizing costs. Which subscription type and configuration should you choose?

Your team is using Cloud Dataprep to clean and transform a dataset. Which TWO features of Cloud Dataprep help you understand data quality issues before running the pipeline? (Choose 2.)

A company wants to use Cloud Data Fusion to build ETL pipelines. They need to connect to a legacy on-premises database using JDBC and also want to use prebuilt transforms from the Hub. Which two features should they use?

A data engineer needs to run an existing Spark job on Google Cloud with minimal code changes. The job requires Hive metastore access. Which Dataproc feature should they use to provide a managed Hive metastore?

You are moving an on-premises Hadoop workload to Google Cloud. The workload uses Hive for metadata and HDFS for storage. Which services should you use to minimise reconfiguration?