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Google Cloud Professional Data Engineer Free Sample Questions

Description

Covers designing secure and reliable data processing systems, building and deploying data pipelines, choosing storage systems and data warehouses, and preparing data for analysis and ML.

12 free sample questions145 in the full practice test Other version: PDE(386)

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GCP-PDE Sample Questions

  1. Question 1

    Advanced

    Ingesting and processing the data · Building the pipelines

    You are designing a data ingestion architecture for a global logistics company. The system must ingest telemetry data from millions of IoT devices installed in delivery trucks. The data arrives in bursts, and the order of events is critical for calculating accurate delivery estimates. However, due to intermittent connectivity, some devices may send data hours late. You need a solution that ensures strictly ordered processing per truck ID while handling high throughput and potential duplicates.

    Which architecture should you implement?

    Answer and explanation

    Correct answer: A

    Pub/Sub ordering keys ensure that messages with the same key (truck ID) are delivered to subscribers in the order they were published, which is critical for this scenario. Dataflow's streaming engine with 'exactly-once' processing handles the deduplication and ensures reliable processing of the ordered stream. Standard Pub/Sub without ordering keys does not guarantee order, and Kafka on Compute Engine adds unnecessary operational overhead.

  2. Question 2

    IntermediateMultiple answers

    Designing data processing systems · Designing for security and compliance

    A financial institution requires a new data warehouse solution. The security team mandates that all data stored must be encrypted using keys that the institution manages and rotates annually. Additionally, specific columns containing PII (Personally Identifiable Information) like Social Security Numbers must be restricted so that only the 'HR-Auditors' group can view the plaintext values, while data analysts see masked data. You plan to use BigQuery.

    Which combination of features should you configure?

    Answer and explanation

    Correct answers: A, B

    To meet the requirement of managing and rotating keys, Customer-Managed Encryption Keys (CMEK) via Cloud KMS is the correct approach. For column-level visibility control, BigQuery Policy Tags (Data Catalog) with Dynamic Data Masking is the standard solution. Authorized Views could work for row-level or table-level access but are less efficient for granular column masking compared to Policy Tags. Select TWO.

    Policy tags allow for fine-grained access control and dynamic masking based on user roles.

  3. Question 3

    Intermediate

    Maintaining and automating data workloads · Optimizing resources

    Your team is migrating a legacy Hadoop workload to Google Cloud. The workload currently runs on an on-premises cluster and processes 50 TB of log data daily using Spark jobs. The processing demand is highly variable: it spikes significantly between 2 AM and 6 AM and is idle for long periods. You need to minimize costs while ensuring the jobs complete within the 4-hour window.

    What architecture should you design?

    Answer and explanation

    Correct answer: A

    For variable workloads that are idle for long periods, ephemeral Dataproc clusters are the most cost-effective solution. You only pay for the compute resources while the job is running. Persistent clusters would incur costs during the idle 20 hours. Migrating to Dataflow would require rewriting the Spark code, which is not specified as a desire. Compute Engine requires manual management and is less efficient.

  4. Question 4

    Advanced

    Storing the data · Selecting storage systems

    You are designing a database schema for Cloud Bigtable to store time-series data from environmental sensors. The query patterns involve retrieving data for a specific sensor over a specific time range. You also need to perform periodic batch analytics on all sensors in a specific geographic region. The current row key design is [SensorID]#[Timestamp].

    What potential issue does this design introduce, and how should you fix it?

    Answer and explanation

    Correct answer: B

    If SensorIDs are sequential or if specific sensors are much more active, writing sequentially can cause hotspotting on specific nodes. Hashing the SensorID distributes writes across the cluster. While [SensorID]#[Timestamp] is good for single-sensor queries, it doesn't solve the write distribution issue if IDs are close. However, the best answer for general hotspot prevention in this context is hashing or salting. Note: For the geographic query requirement, a different approach might be needed (like a secondary index or different table), but the primary issue with [SensorID] is often write distribution if not hashed.

  5. Question 5

    Intermediate

    Storing the data · Planning for using a data warehouse

    A retail company uses BigQuery for its data warehouse. They have a table named Sales that is 500 TB in size. Analysts frequently run queries filtering by TransactionDate and aggregating by StoreId. These queries are becoming slower and more expensive.

    How should you optimize the table schema to improve performance and reduce costs?

    Answer and explanation

    Correct answer: A

    Partitioning by TransactionDate ensures that queries filtering on date only scan the relevant partitions, drastically reducing cost (bytes scanned). Clustering by StoreId sorts the data within each partition, which accelerates aggregations and filters on that column. This combination directly addresses both the filter and aggregation requirements.

  6. Question 6

    Beginner

    Preparing and using data for analysis · Preparing data for AI and ML

    Your organization is building a machine learning pipeline to predict customer churn. The data resides in BigQuery, and your data science team wants to use SQL to build and train the model because they are less proficient in Python/TensorFlow. The model needs to be retrained weekly with fresh data.

    Which Google Cloud service should you use?

    Answer and explanation

    Correct answer: A

    BigQuery ML allows users to create and execute machine learning models in BigQuery using standard SQL queries. This fits the requirement of using SQL and avoids moving data out of the warehouse. Vertex AI Custom Training would require Python/container knowledge.

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