You've migrated a Hadoop job from an on-premises cluster to Dataproc and Good Storage. Your Spark job is a complex analytical workload fiat consists of many shuffling operations, and initial data are parquet toes (on average 200-400 MB size each) You see some degradation in performance after the migration to Dataproc so you'd like to optimize for it. Your organization is very cost-sensitive so you'd Idee to continue using Dataproc on preemptibles (with 2 non-preemptibles workers only) for this workload. What should you do?
A. Switch from HODs to SSDs override the preemptible VMs configuration to increase the boot disk size
B. Increase the see of your parquet files to ensure them to be 1 GB minimum
C. Switch to TFRecords format (appr 200 MB per We) instead of parquet files
D. Switch from HDDs to SSDs. copy initial data from Cloud Storage to Hadoop Distributed File System (HDFS) run the Spark job and copy results back to Cloud Storage
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
1.
Decoupling producer from consumer
2.
Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
3.
Near real-time SQL query
4.
Maintain at least 2 years of historical data, which will be queried with SQ
Which pipeline should you use to meet these requirements?
A. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
B. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
C. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
D. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
You are building a report-only data warehouse where the data is streamed into BigQuery via the streaming API Following Google's best practices, you have both a staging and a production table for the data How should you design your data loading to ensure that there is only one master dataset without affecting performance on either the ingestion or reporting pieces?
A. Have a staging table that is an append-only model, and then update the production table every three hours with the changes written to staging
B. Have a staging table that is an append-only model, and then update the production table every ninety minutes with the changes written to staging
C. Have a staging table that moves the staged data over to the production table and deletes the contents of the staging table every three hours
D. Have a staging table that moves the staged data over to the production table and deletes the contents of the staging table every thirty minutes
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?
A. Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
B. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
C. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
D. Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?
A. Build and train a complex classification model with Spark MLlib to generate labels and filter the results. Deploy the models using Cloud Dataproc. Call the model from your application.
B. Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.
You have a BigQuery table that ingests data directly from a Pub/Sub subscription. The ingested data is encrypted with a Google-managed encryption key. You need to meet a new organization policy that requires you to use keys from a centralized Cloud Key Management Service (Cloud KMS) project to encrypt data at rest. What should you do?
A. Create a new BigOuory table by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.
B. Create a new BigOuery table and Pub/Sub topic by using customer-managed encryption keys (CMEK), and migrate the data from the old Bigauery table.
C. Create a new Pub/Sub topic with CMEK and use the existing BigQuery table by using Google-managed encryption key.
D. Use Cloud KMS encryption key with Dataflow to ingest the existing Pub/Sub subscription to the existing BigQuery table.
You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.
What should you do?
A. Export Bigtable dump to GCS and run your analytical job on top of the exported files.
B. Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
C. Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
D. Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
A. Convert all daily log tables into date-partitioned tables
B. Convert the sharded tables into a single partitioned table
C. Enable query caching so you can cache data from previous months
D. Create separate views to cover each month, and query from these views
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
You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.
What should you do?
A. Increase the size of the dataset by collecting additional data.
B. Train a linear regression to predict a credit default risk score.
C. Remove the bias from the data and collect applications that have been declined loans.
D. Match loan applicants with their social profiles to enable feature engineering.
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