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Question # 1
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?
A. Create an API using App Engine to receive and send messages to the applications
B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
C. Create a table on Cloud SQL, and insert and delete rows with the job information
D. Create a table on Cloud Spanner, and insert and delete rows with the job information
Question # 2
You are responsible for writing your company’s ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?
A. PigLatin using Pig
B. HiveQL using Hive
C. Java using MapReduce
D. Python using MapReduce
Question # 3
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data). What should you do?
A. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
B. Add a try… catch block to your DoFn that transforms the data, extract erroneous rows from logs.
C. Add a try… catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
D. Add a try… catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.
Question # 4
You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?
A. Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load
the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
B. Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it
to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
C. Export the records from the database into a CSV file. Create a public URL for the CSV file, and then
use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using
the BigQuery web UI in the GCP Console.
D. Export the records from the database as an Avro file. Create a public URL for the Avro file, and then
use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using
the BigQuery web UI in the GCP Console.
Question # 5
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?
A. Use Transfer Appliance to copy the data to Cloud Storage Use gsutil cp –J to compress the content being uploaded to Cloud Storage
B. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage
C. Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20
D. Mb/sec so it does not interfere with the production traffic
Question # 6
You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?
A. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
B. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
C. Add sensor calibration data to the output of the ETL process, and document that all users need to apply
sensor calibration themselves.
D. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job
based on calibration factors, and apply the correction to all data.
Question # 7
Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of datA. Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?
A. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.
B. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
C. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
D. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.
Question # 8
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?
A. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
B. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
C. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
D. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
Question # 9
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 andprovide 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?
A. Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.
B. Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.
C. 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.
D. Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.
Question # 10
After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You’ve loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.What should you do?
A. Select random samples from the tables using the RAND() function and compare the samples.
B. Select random samples from the tables using the HASH() function and compare the samples.
C. Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.
D. Create stratified random samples using the OVER() function and compare equivalent samples from each table.
Question # 11
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?
A. Cloud Speech-to-Text API
B. Cloud Natural Language API
C. Dialogflow Enterprise Edition
D. Cloud AutoML Natural Language
Question # 12
You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?
A. The current epoch time
B. A concatenation of the product name and the current epoch time
C. A random universally unique identifier number (version 4 UUID)
D. The original order identification number from the sales system, which is a monotonically increasing integer
Question # 13
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?
A. Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
B. Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
C. Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
D. Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
Question # 14
You need to choose a database for a new project that has the following requirements:Fully managedAble to automatically scale upTransactionally consistentAble to scale up to 6 TBAble to be queried using SQLWhich database do you choose?
A. Cloud SQL
B. Cloud Bigtable
C. Cloud Spanner
D. Cloud Datastore
Question # 15
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?
A. Migrate the workload to Google Cloud Dataflow
B. Use pre-emptible virtual machines (VMs) for the cluster
C. Use a higher-memory node so that the job runs faster
D. Use SSDs on the worker nodes so that the job can run faster
