Professional-Data-Engineer Exam Dumps – Our Features

Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)

• Supervised learning to determine which transactions are most likely to be fraudulent.
• Unsupervised learning to determine which transactions are most likely to be fraudulent.
• Clustering to divide the transactions into N categories based on feature similarity.
• Supervised learning to predict the location of a transaction.
• Reinforcement learning to predict the location of a transaction.
• Unsupervised learning to predict the location of a transaction.

You want to encrypt the customer data stored in BigQuery. You need to implement for-user crypto- deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?

• Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.
• Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.
• Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.
• Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

• Re-write the application to load accumulated data every 2 minutes.
• Convert the streaming insert code to batch load for individual messages.
• Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
• Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

Which of the following is NOT true about Dataflow pipelines?

• Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
• Dataflow pipelines can consume data from other Google Cloud services
• Dataflow pipelines can be programmed in Java
• Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

MJTelco is building a custom interface to share data. They have these requirements:They need to do aggregations over their petabyte-scale datasets.They need to scan specific time range rows with a very fast response time (milliseconds).Which combination of Google Cloud Platform products should you recommend?

• Cloud Datastore and Cloud Bigtable
• Cloud Bigtable and Cloud SQL
• BigQuery and Cloud Bigtable
• BigQuery and Cloud Storage

You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?

• Use Cloud TPUs without any additional adjustment to your code.
• Use Cloud TPUs after implementing GPU kernel support for your customs ops.
• Use Cloud GPUs after implementing GPU kernel support for your customs ops.
• Stay on CPUs, and increase the size of the cluster you’re training your model on.

You have a table that contains millions of rows of sales data, partitioned by date Various applications and users query this data many times a minute. The query requires aggregating values by using avg. max. and sum, and does not require joining to other tables. The required aggregations are only computed over the past year of data, though you need to retain full historical data in the base tables You want to ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. What should you do?

• Create a materialized view to aggregate the base table data Configure a partition expiration on the base table to retain only the last one year of partitions.
• Create a materialized view to aggregate the base table data include a filter clause to specify the last one year of partitions.
• Create a new table that aggregates the base table data include a filter clause to specify the last year of partitions. Set up a scheduled query to recreate the new table every hour.
• Create a view to aggregate the base table data Include a filter clause to specify the last year of partitions.

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. What should you do?

• 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.

You need to create a SQL pipeline. The pipeline runs an aggregate SOL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures. What should you do?

• Create a BigQuery scheduled query to run the SOL transformation with schedule options that repeats every two hours, and enable emailnotifications.
• Use the BigQueryUpsertTableOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter totrue.
• Use the BigQuerylnsertJobOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter totrue.
• Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable
• notification to Pub/Sub topic. Use Pub/Sub and Cloud Functions to send an email after three tailed executions.

You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

• Create a table in BigQuery, and append the new samples for CPU and memory to the table
• Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
• Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
• Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.