Spring Sale Special Limited Time 70% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: xmas50

Google Professional-Data-Engineer - Google Professional Data Engineer Exam

Google Professional-Data-Engineer Premium Access     Download Demo    
Page: 3 / 6
Total 400 questions

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

A.

Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.

B.

Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.

C.

Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

D.

Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

E.

Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

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. What should you do?

A.

Put the data into Google Cloud Storage.

B.

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

C.

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

D.

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

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?

A.

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

B.

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

C.

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

D.

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

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A.

Eliminate features that are highly correlated to the output labels.

B.

Combine highly co-dependent features into one representative feature.

C.

Instead of feeding in each feature individually, average their values in batches of 3.

D.

Remove the features that have null values for more than 50% of the training records.

Your company’s customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

A.

Add a node to the MySQL cluster and build an OLAP cube there.

B.

Use an ETL tool to load the data from MySQL into Google BigQuery.

C.

Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.

D.

Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

A.

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

B.

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

C.

In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

D.

Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

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?

A.

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

B.

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

C.

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

D.

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

You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?

A.

Update the current pipeline and use the drain flag.

B.

Update the current pipeline and provide the transform mapping JSON object.

C.

Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.

D.

Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

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?

A.

Issue a command to restart the database servers.

B.

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

C.

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

D.

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

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

A.

Linear regression

B.

Logistic classification

C.

Recurrent neural network

D.

Feedforward neural network