CertyIQ - Google - Prof Data Eng - pt 4

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?

• Increase the size of the dataset by collecting additional data

• Train a linear regression to predict a credit default risk score.

• Remove the bias from the data and collect applications that have been declined loans.

• Match loan applicants with their social profiles to enable feature engineering.

You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern. Which service do you select for storing and serving your data?

• Cloud Spanner

• Cloud Bigtable

• Cloud Firestore

• Cloud SQL

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?

• Export Bigtable dump to GCS and run your analytical job on top of the exported files.

• 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.

• 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

• Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.

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?

• Batch job, PubSubIO, side-inputs

• Streaming job, PubSubIO, JdbcIO, side-outputs

• Streaming job, PubSubIO, BigQueryIO, side-inputs

• Streaming job, PubSubIO, BigQueryIO, side-outputs

You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of your Cloud Bigtable cluster. Which two actions can you take to accomplish this? (Choose two.)

• Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.

• Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.

• Monitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.

• Monitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.

• Monitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.

• 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.

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

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

You store historic data in Cloud Storage. You need to perform analytics on the historic data. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL. What should you do?

• Use Cloud Dataflow with Beam to detect errors and perform transformations.

• Use Cloud Dataprep with recipes to detect errors and perform transformations.

• Use Cloud Dataproc with a Hadoop job to detect errors and perform transformations.

• Use federated tables in BigQuery with queries to detect errors and perform transformations.

Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing onpremises applications every day. What should they do?

• . Execute gsutil rsync from the on-premises servers.

• Use Dataflow and write the data to Cloud Storage.

• Write a job template in Dataproc to perform the data transfer.

• Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.

You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query `"-dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

• Create a separate table for each ID.

• Use the LIMIT keyword to reduce the number of rows returned.

• Recreate the table with a partitioning column and clustering column.

• Use the bq query --maximum_bytes_billed flag to restrict the number of bytes billed

You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

• Use bq load to load a batch of sensor data every 60 seconds

• Use bq load to load a batch of sensor data every 60 seconds

• Use the INSERT statement to insert a batch of data every 60 seconds.

• Use the MERGE statement to apply updates in batch every 60 seconds.