Sample Questions and Answers
How can you optimize query performance in BigQuery when working with large datasets?
A) Use partitioned tables and clustered tables
B) Use non-partitioned tables only
C) Avoid compression of data
D) Use Cloud Storage for queries
Answer: A) Use partitioned tables and clustered tables
Explanation: Partitioning and clustering help reduce the amount of data scanned, improving query speed and reducing cost.
Which Cloud service would you use to build a fully managed, serverless, event-driven data processing pipeline?
A) Cloud Functions triggered by Pub/Sub events
B) Cloud Dataproc with manual cluster management
C) Cloud SQL
D) Cloud Storage lifecycle rules
Answer: A) Cloud Functions triggered by Pub/Sub events
Explanation: Cloud Functions provide lightweight serverless compute that can respond to Pub/Sub messages.
What is the best way to automate repetitive data transformation jobs in Google Cloud?
A) Use Cloud Composer (managed Apache Airflow)
B) Run manual scripts on Compute Engine
C) Use Cloud Storage lifecycle rules
D) Use Cloud SQL scheduled queries
Answer: A) Use Cloud Composer (managed Apache Airflow)
Explanation: Cloud Composer helps automate and orchestrate workflows with DAGs (Directed Acyclic Graphs).
What is a benefit of using BigQuery federated queries?
A) Query external data sources such as Cloud Storage or Cloud Bigtable without loading data
B) Automatically partitions data
C) Reduces query latency by caching results
D) Supports transaction management
Answer: A) Query external data sources such as Cloud Storage or Cloud Bigtable without loading data
Explanation: Federated queries allow querying data outside of BigQuery storage directly.
Which tool would you use to monitor and troubleshoot data pipelines in Cloud Dataflow?
A) Cloud Monitoring and Cloud Logging
B) Stackdriver Trace only
C) Cloud IAM
D) Cloud Shell
Answer: A) Cloud Monitoring and Cloud Logging
Explanation: These tools provide visibility into pipeline performance and errors.
What is the main function of the BigQuery Storage API?
A) Enables high-throughput data reads from BigQuery tables for analytics applications
B) Ingests data into BigQuery tables
C) Manages BigQuery datasets
D) Runs batch SQL queries
Answer: A) Enables high-throughput data reads from BigQuery tables for analytics applications
Explanation: It improves performance for client applications reading large datasets.
How do you ensure data integrity during ingestion in a streaming pipeline using Pub/Sub and Dataflow?
A) Enable exactly-once processing and use deduplication techniques
B) Allow at-least-once processing without deduplication
C) Skip validation to reduce latency
D) Use batch ingestion only
Answer: A) Enable exactly-once processing and use deduplication techniques
Explanation: This prevents duplicate data and ensures consistency.
Which feature in BigQuery allows you to materialize the results of a query for faster repeated access?
A) Materialized Views
B) Temporary tables
C) External tables
D) User-defined functions
Answer: A) Materialized Views
Explanation: Materialized views store precomputed query results for faster retrieval.
When designing a data warehouse, what is a benefit of denormalization?
A) Improves query performance by reducing the need for joins
B) Reduces storage usage
C) Increases data integrity
D) Makes updates easier
Answer: A) Improves query performance by reducing the need for joins
Explanation: Denormalized tables allow faster query performance at the cost of some redundancy.
Which type of encryption does Google Cloud provide by default for data at rest?
A) AES-256 encryption
B) No encryption
C) RSA encryption only
D) User-managed keys only
Answer: A) AES-256 encryption
Explanation: Google Cloud encrypts data at rest by default using AES-256.
What is the primary role of Data Catalog in Google Cloud?
A) Metadata management and data discovery
B) Data storage
C) Stream processing
D) Identity management
Answer: A) Metadata management and data discovery
Explanation: Data Catalog helps organize and find datasets across the cloud environment.
Which BigQuery feature supports row-level security?
A) Authorized views and policy tags with column-level access controls
B) Data encryption only
C) Partition expiration
D) Clustering
Answer: A) Authorized views and policy tags with column-level access controls
Explanation: These features allow granular access control at row and column levels.
How can you optimize costs when loading data into BigQuery from Cloud Storage?
A) Use batch loading instead of streaming inserts when possible
B) Always use streaming inserts
C) Use multiple small files instead of larger files
D) Avoid partitioning tables
Answer: A) Use batch loading instead of streaming inserts when possible
Explanation: Batch loading is more cost-effective for large datasets.
Which service provides a managed environment for running Apache Airflow workflows?
A) Cloud Composer
B) Cloud Run
C) Cloud Functions
D) Cloud Build
Answer: A) Cloud Composer
Explanation: Cloud Composer is a fully managed Apache Airflow service.
What is the best practice for controlling costs and query performance in BigQuery?
A) Use table partitioning and clustering
B) Use unpartitioned tables
C) Increase the number of slots manually
D) Avoid caching query results
Answer: A) Use table partitioning and clustering
Explanation: These help limit data scanned and improve query efficiency.
What is the primary benefit of using Cloud Bigtable over Cloud SQL for certain workloads?
A) Optimized for high-throughput, low-latency, NoSQL workloads such as time-series data
B) Provides relational database capabilities
C) Supports ACID transactions
D) Automatically indexes all columns
Answer: A) Optimized for high-throughput, low-latency, NoSQL workloads such as time-series data
Explanation: Bigtable is designed for scalable NoSQL use cases.
Which of the following is a key characteristic of Cloud Dataflow?
A) Unified stream and batch data processing with autoscaling
B) Only batch processing
C) Requires manual cluster management
D) Provides NoSQL storage
Answer: A) Unified stream and batch data processing with autoscaling
Explanation: Dataflow supports both batch and streaming jobs with dynamic resource management.
How can you secure data access in BigQuery for different departments within an organization?
A) Use dataset-level IAM roles and authorized views
B) Share service account credentials across departments
C) Allow all users full access by default
D) Use only public datasets
Answer: A) Use dataset-level IAM roles and authorized views
Explanation: IAM roles and authorized views provide fine-grained access control.
What is the recommended method to ingest unstructured log data into BigQuery?
A) Use Cloud Logging export to BigQuery or Cloud Storage for batch processing
B) Upload logs manually via CSV
C) Use Cloud SQL
D) Use Cloud Dataproc only
Answer: A) Use Cloud Logging export to BigQuery or Cloud Storage for batch processing
Explanation: Cloud Logging integrates with BigQuery for log analysis.
What is the purpose of schema evolution in BigQuery?
A) To allow adding or modifying fields in tables without downtime
B) To lock schema to prevent changes
C) To delete columns automatically
D) To convert data types automatically
Answer: A) To allow adding or modifying fields in tables without downtime
Explanation: Schema evolution supports flexibility in schema changes.
Which Google Cloud service supports running containerized data processing workloads in a serverless way?
A) Cloud Run
B) Cloud Functions
C) Compute Engine
D) Kubernetes Engine (GKE)
Answer: A) Cloud Run
Explanation: Cloud Run runs containers without managing servers, ideal for data processing.
What is a common use case for BigQuery ML?
A) Predicting customer churn using SQL without exporting data
B) Hosting websites
C) Managing IoT devices
D) Running Spark jobs
Answer: A) Predicting customer churn using SQL without exporting data
Explanation: BigQuery ML enables building and running ML models directly on BigQuery data.
Which service can be used for scalable metadata management across various data sources in Google Cloud?
A) Cloud Data Catalog
B) BigQuery
C) Cloud Storage
D) Cloud Dataproc
Answer: A) Cloud Data Catalog
Explanation: Data Catalog centralizes metadata management and data discovery.
What is a key feature of BigQuery’s streaming data inserts?
A) Enables near real-time data availability for analysis
B) Requires batch job scheduling
C) Not supported for streaming data
D) Only supports JSON data
Answer: A) Enables near real-time data availability for analysis
Explanation: Streaming inserts allow data to be analyzed within seconds.
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