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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A) Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
B) Add a new column called FullName to the Users tabl
C) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
D) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
E) Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
F) Use BigQuery to export the data for the table to a CSV fil

2. You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A) Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
B) Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
C) Consume the stream of data in Cloud Dataflow using Kafka I
D) Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
E) Consume the stream of data in Cloud Dataflow using Kafka I
F) Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
G) If that number falls below 4000, send an alert.
H) Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
I) Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
J) Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
K) Set a sliding time window of 1 hour every 5 minute
L) If that number falls below 4000, send an alert.
M) Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

3. 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) Remove the features that have null values for more than 50% of the training records.
B) Eliminate features that are highly correlated to the output labels.
C) Instead of feeding in each feature individually, average their values in batches of 3.
D) Combine highly co-dependent features into one representative feature.

4. You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A) Create a temporary table in Cloud Bigtable that will act as a buffer for new data
B) Create a temporary table in Cloud Spanner that will act as a buffer for new data
C) Increase the number of max workers
D) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
E) Use a larger instance type for your Cloud Dataflow workers
F) Change the zone of your Cloud Dataflow pipeline to run in us-central1
G) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery

5. 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: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query 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 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.
B) 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.
C) Create an application that writes to a Cloud SQL database to store the dat
D) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
E) Create an application that provides an AP
F) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

Solutions: