13. Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low. You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take?

Introduce data compression for each file to increase the rate file of file transfer.

Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premises data to the designated storage bucket.

15. You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query: SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1: (View Image) What is the most likely cause of the delay for this query?

Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

Users are running too many concurrent queries in the system

The [myproject:mydataset.mytable] table has too many partitions

Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values

16. Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.

Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.

Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.

Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

17. Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of data. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

Create a new view over events_partitioned using standard SQL

Create a service account for the ODBC connection to use for authentication

Create a new view over events using standard SQL

Create a new partitioned table using a standard SQL query

Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared "events"

18. Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

Use pre-emptible virtual machines (VMs) for the cluster

Migrate the workload to Google Cloud Dataflow

Use a higher-memory node so that the job runs faster

Use SSDs on the worker nodes so that the job can run faster

19. Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.

Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.

20. You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices.

Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.

Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.

Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.

Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.

21. You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

Use Cloud Storage for storage. Link as permanent tables in BigQuery for query

Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable

Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query

Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.

22. 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?

Use Cloud Spanner for storage. Add secondary indexes to support query patterns.

Use Cloud SQL for storage. Add secondary indexes to support query patterns.

Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

25. You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.

Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.

Place the MariaDB instances in an Instance Group with a Health Check.

Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.

Install the StackDriver Agent and configure the MySQL plugin.

27. You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?

Streaming job, PubSubIO, BigQueryIO, side-inputs

Batch job, PubSubIO, side-inputs

Streaming job, PubSubIO, JdbcIO, side-outputs

Streaming job, PubSubIO, BigQueryIO, side-outputs

28.You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps. You have the following requirements: - You will batch-load the posts once per day and run them through the Cloud Natural Language API. - You will extract topics and sentiment from the posts. - You must store the raw posts for archiving and reprocessing. - You will create dashboards to be shared with people both inside and outside your organization. You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.

Store the social media posts and the data extracted from the API in BigQuery.

Store the social media posts and the data extracted from the API in Cloud SQL.

Feed to social media posts into the API directly from the source, and write the extracted data from the API into

29. You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

Workflow Templates on Cloud Dataproc

30. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the

Create an authorized view in BigQuery to restrict access to tables with sensitive data.

Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive

Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may

32.You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour. How should you design the solution?

Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.

Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis

Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.

33. You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements: - Each department should have access only to their data. - Each department will have one or more leads who need to be able to create and update tables and provide them to their team. - Each department has data analysts who need to be able to query but not modify data. How should you set access to the data in BigQuery?

Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data

Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data

analysts the role of READER on their dataset.

Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts

the role of Editor on the project the table is in.

34. You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time

Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class

Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved

Use managed export, and then import the data into a BigQuery table created just for that export, and delete

Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery

Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a

35. You are designing a cloud-native historical data processing system to meet the following conditions: - The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine. - A streaming data pipeline stores new data daily. - Peformance is not a factor in the solution. - The solution design should maximize availability. How should you design data storage for this solution?

Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.

Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.

Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

36. Your United States-based company has created an application for assessing and responding to user actions. The primary table's data volume grows by 250,000 records per second. Many third parties use your application's APIs to build the functionality into their own frontend applications. Your application's APIs should comply with the following requirements: - Single global endpoint - ANSI SQL support - Consistent access to the most up-to-date data What should you do?

Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.

Implement BigQuery with no region selected for storage or processing.

Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.

Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

37. You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time. Consumers will receive the data in the following ways: - Real-time event stream - ANSI SQL access to real-time stream and historical data - Batch historical exports Which solution should you use?

Cloud Pub/Sub, Cloud Storage, BigQuery

Cloud Dataflow, Cloud SQL, Cloud Spanner

Cloud Dataproc, Cloud Dataflow, BigQuery

Cloud Pub/Sub, Cloud Dataproc, Cloud SQL

38. You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them

Create an API using App Engine to receive and send messages to the applications

Create a table on Cloud SQL, and insert and delete rows with the job information

Create a table on Cloud Spanner, and insert and delete rows with the job information

39. You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

Use Transfer Appliance to copy the data to Cloud Storage

Use gsutil cp ""J to compress the content being uploaded to Cloud Storage

Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage

Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/sec so it does not interfere with the production traffic

40. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include: - Executing the transformations on a schedule - Enabling non-developer analysts to modify transformations - Providing a graphical tool for designing transformations What should you do?

Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes

Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

41. You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.

Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.

Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.

Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.

42. You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.

44. You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

Deploy a Cloud Dataproc cluster. Use a standard persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://

Deploy a Cloud Dataproc cluster. Use an SSD persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://

Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instances. Install the Cloud Storage connector, and store the data in Cloud Storage. Change references in scripts from hdfs:// to gs://

Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances. Store data in HDFS. Change references in scripts from hdfs:// to gs://

46. What are two of the benefits of using denormalized data structures in BigQuery?

Increases query speed, makes queries simpler

Reduces the amount of data processed, reduces the amount of storage required

Reduces the amount of storage required, increases query speed

Reduces the amount of data processed, increases query speed

47. Which of the following are examples of hyperparameters? (Select 2 answers.)

Number of nodes in each hidden layer

48. Which of the following are feature engineering techniques? (Select 2 answers)

Bucketization of a continuous feature

49. You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

BigQuery cannot be used as a sink

51. Which of the following statements is NOT true regarding Bigtable access roles?

To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.

Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

You can configure access control only at the project level.

To give a user access to only one table in a project, you must configure access through your application.

52. What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

Keep your row key reasonably short

Include multiple time series values within the row key

Keep the row keep as an 8 bit integer

Keep your row key as long as the field permits

Training Data Exam Quiz

1.

MULTIPLE CHOICE QUESTION

30 sec • 5 pts

1. Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Threading

Serialization

Dropout Methods

Dimensionality Reduction

2.

MULTIPLE CHOICE QUESTION

30 sec • 5 pts

2. An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?

Use federated data sources, and check data in the SQL query.

Enable BigQuery monitoring in Google Stackdriver and create an alert.

Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.

Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.

3.

MULTIPLE CHOICE QUESTION

30 sec • 5 pts

3. Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

Issue a command to restart the database servers.

Retry the query with exponential backoff, up to a cap of 15 minutes.

Retry the query every second until it comes back online to minimize staleness of data.

Reduce the query frequency to once every hour until the database comes back online.

4.

MULTIPLE CHOICE QUESTION

30 sec • 5 pts

4. You are building new real-time data warehouse for your company and will use Google 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?

Include ORDER BY DESK on timestamp column and LIMIT to 1.

Use GROUP BY on the unique ID column and timestamp column and SUM on the values.

Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.

Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

5.

MULTIPLE CHOICE QUESTION

30 sec • 5 pts

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

Use a fixed-time window with a duration of 60 minutes.

Use a sliding time window with a duration of 60 minutes.

Use a session window with a gap time duration of 60 minutes.

Use a global window with a time based trigger with a delay of 60 minutes.

6.

MULTIPLE CHOICE QUESTION

30 sec • 5 pts

6. 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.

Create a Google Cloud Dataflow job to process the data.

Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

Create a Hadoop cluster on Google Compute Engine that uses persistent disks.

Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.

Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

7.

MULTIPLE CHOICE QUESTION

30 sec • 5 pts

7. Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud 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.

Put the data into Google Cloud Storage.

Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

8.

MULTIPLE CHOICE QUESTION

30 sec • 5 pts

8. You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time.

Send the data to Google Cloud Datastore and then export to BigQuery.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

9.

MULTIPLE CHOICE QUESTION

30 sec • 5 pts

9. You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project.

Grant the consultant the Viewer role on the project.

Grant the consultant the Cloud Dataflow Developer role on the project.

Create a service account and allow the consultant to log on with it.

Create an anonymized sample of the data for the consultant to work with in a different project.

10.

MULTIPLE CHOICE QUESTION

30 sec • 5 pts

11. You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible.

Load the data every 30 minutes into a new partitioned table in BigQuery.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Training Data Exam

99 questions

1. Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

3. Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

6. 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.

8. You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time.

9. You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project.

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