Google Certified Professional Data Engineer - Professional-Data-Engineer Free Exam Questions

A. Increase the memory available to the Airflow workers.

B. Increase the Cloud Composer 2 environment size from medium to large.

C. Increase the maximum number of workers and reduce worker concurrency.

D. Increase the directed acyclic graph (DAG) file parsing interval.

E. Increase the memory available to the Airflow triggerer.

A. Create a scheduled query to build sales day and sales month aggregate tables on an hourly basis.

B. Build materialized views on top of the sales table to aggregate data at the day and month level.

C. Enable Bl Engine and add your sales table as a preferred table.

D. Build authorized views on top of the sales table to aggregate data at the day and month level.

A. hour-by-hour

B. minute-by-minute

C. month-by-month

D. week-by-week

A. Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions.
Integrate the package tracking applications with this function.

B. Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.

C. Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.

D. Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.

A. Create two new read replicas from the new primary instance, one in Region3 and one in a new region.

B. Create a new read replica in Region1, promote the new read replica to be the primary instance, and enable zonal high availability.

C. Enable zonal high availability on the primary instance. Create a new read replica in a new region.

D. Create a cascading read replica from the existing read replica in Region3.

A. Create one unique table for all of the indices, and then use the index and timestamp as the row key design

B. For each index, have a separate table and use a reverse timestamp as the row key design

C. Create one unique table for all of the indices, and then use a reverse timestamp as the row key design.

D. For each index, have a separate table and use a timestamp as the row key design

A. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.

B. Increase the size of your parquet files to ensure them to be 1 GB minimum.

C. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.

D. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.

A. Disable external IP addresses from worker VMs and enable Private Google Access.

B. Disable external IP addresses and establish a Private Service Connect endpoint IP address.

C. Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.

D. Leave external IP addresses assigned to worker VMs while enforcing firewall rules.

A. Create a Cloud Run function and schedule it to run daily using Cloud Scheduler to load the data into BigQuery.

B. Create a pipeline by using BigQuery pipelines and schedule it to load the data into BigQuery daily.

C. Use the BigQuery Data Transfer Service to load files from Cloud Storage to BigQuery, create a BigQuery job which transforms the data using BigQuery SQL and schedule it to run daily.

D. Build a custom Apache Beam pipeline and run it on Dataflow to load the file from Cloud Storage to BigQuery and schedule it to run daily using Cloud Composer.

A. Provide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.

B. Load the data into BigQuery and use SQL to transform the data as necessary. Provide the data science team access to staging tables to explore the raw data.

C. Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.

D. Create an external table in BigQuery and use SQL to transform the data as necessary. Provide the data science team access to the external tables to explore the raw data.

A. [0, 0, 0, 1, 0, 0, 1]

B. [1, 0, 0, 0, 0, 0, 0]

C. [0, 5, 0, 0, 0, 0]

D. [0, 1]

A. Cloud Composer

B. Workflow Templates on Cloud Dataproc

C. cron

D. Cloud Scheduler

A. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

B. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

C. Create a cron schedule in Cloud Dataprep.

D. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

A. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.

B. Store and process the entire dataset in Cloud Bigtable.

C. Store and process the entire dataset in BigQuery.

D. Store the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as 80% warm and 20% active.

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. Use BigQuery to export the data for the table to a CSV file. 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 FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

C. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

D. Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.