You are building storage for files for a data pipeline on Google Cloud. You want to support JSON files. The schema of these files will occasionally change. Your analyst teams will use running aggregate ANSI SQL queries on this data. What should you do?

You use a Hadoop cluster both for serving analytics and for processing and transforming data. The data is currently stored on HDFS in Parquet format. The data processing jobs run for 6 hours each night. Analytics users can access the system 24 hours a day. Phase 1 is to quickly migrate the entire Hadoop environment without a major re-architecture. Phase 2 will include migrating to BigQuery for analytics and to Dataflow for data processing. You want to make the future migration to BigQuery and Dataflow easier by following Google-recommended practices and managed services. What should you do?

You are building a new real-time data warehouse for your company and will use BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

You are designing a streaming pipeline for ingesting player interaction data for a mobile game. You want the pipeline to handle out-of-order data delayed up to 15 minutes on a per-player basis and exponential growth in global users. What should you do?

Your company is loading CSV files into BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

You have 250,000 devices which produce a JSON device status event every 10 seconds. You want to capture this event data for outlier time series analysis. What should you do?

You are selecting a messaging service for log messages that must include final result message ordering as part of building a data pipeline on Google Cloud. You want to stream input for 5 days and be able to query the current status. You will be storing the data in a searchable repository. How should you set up the input messages?

You want to publish system metrics to Google Cloud from a large number of on-prem hypervisors and VMs for analysis and creation of dashboards. You have an existing custom monitoring agent deployed to all the hypervisors and your on-prem metrics system is unable to handle the load. You want to design a system that can collect and store metrics at scale. You don't want to manage your own time series database. Metrics from all agents should be written to the same table but agents must not have permission to modify or read data written by other agents. What should you do?

You are designing a relational data repository on Google Cloud to grow as needed. The data will be transactionally consistent and added from any location in the world. You want to monitor and adjust node count for input traffic, which can spike unpredictably. What should you do?

You have a Spark application that writes data to Cloud Storage in Parquet format. You scheduled the application to run daily using DataProcSparkOperator and Apache Airflow DAG by Cloud Composer. You want to add tasks to the DAG to make the data available to BigQuery users. You want to maximize query speed and configure partitioning and clustering on the table. What should you do?

You have a website that tracks page visits for each user and then creates a Pub/Sub message with the session ID and URL of the page. You want to create a Dataflow pipeline that sums the total number of pages visited by each user and writes the result to BigQuery. User sessions timeout after 30 minutes. Which type of Dataflow window should you choose?

You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules: a). No interaction by the user on the site for 1 hour b). Has added more than $30 worth of products to the basket c). Has not completed a transaction. You use Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

You need to stream time-series data in Avro format, and then write this to both BigQuery and Cloud Bigtable simultaneously using Dataflow. You want to achieve minimal end-to-end latency. Your business requirements state this needs to be completed as quickly as possible. What should you do?

Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?

Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

You are developing an application on Google Cloud that will automatically generate subject labels for users’ blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

Your company is using WILDCARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the error shown below. Which table name will make the SQL statement work correctly?

You are working on an ML-based application that will transcribe conversations between manufacturing workers. These conversations are in English and between 30-40 sec long. Conversation recordings come from old enterprise radio sets that have a low sampling rate of 8000 Hz, but you have a large dataset of these recorded conversations with their transcriptions. You want to follow Google-recommended practices. How should you proceed with building your application?

You are developing an application on Google Cloud that will label famous landmarks in users’ photos. You are under competitive pressure to develop a predictive model quickly. You need to keep service costs low. What should you do?

You are building a data pipeline on Google Cloud. You need to select services that will host a deep neural network machine-learning model also hosted on Google Cloud. You also need to monitor and run jobs that could occasionally fail. What should you do?

You work on a regression problem in a natural language processing domain, and you have 100M labeled examples in your dataset. You have randomly shuffled your data and split your dataset into training and test samples (in a 90/10 ratio). After you have trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?

You are using Pub/Sub to stream inventory updates from many point-of-sale (POS) terminals into BigQuery. Each update event has the following information: product identifier "prodSku", change increment "quantityDelta", POS identification "termId", and "messageId" which is created for each push attempt from the terminal. During a network outage, you discovered that duplicated messages were sent, causing the inventory system to over-count the changes. You determine that the terminal application has design problems and may send the same event more than once during push retries. You want to ensure that the inventory update is accurate. What should you do?

You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database table must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

You are working on a project with two compliance requirements. The first requirement states that your developers should be able to see the Google Cloud billing charges for only their own projects. The second requirement states that your finance team members can set budgets and view the current charges for all projects in the organization. The finance team should not be able to view the project contents. You want to set permissions. What should you do?