Free Professional-Data-Engineer Exam Dumps

Question 16

- (Exam Topic 6)
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don’t get slots to execute their query and you need to correct this. You’d like to avoid introducing new projects to your account.
What should you do?

A. Convert your batch BQ queries into interactive BQ queries.
B. Create an additional project to overcome the 2K on-demand per-project quota.
C. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
D. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.

Correct Answer:C
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery

Question 17

- (Exam Topic 1)
Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

A. Threading
B. Serialization
C. Dropout Methods
D. Dimensionality Reduction

Correct Answer:C
Reference
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505

Question 18

- (Exam Topic 6)
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country
You check the query plan for the query and see the following output in the Read section of Stage:1:
Professional-Data-Engineer dumps exhibit
What is the most likely cause of the delay for this query?

A. Users are running too many concurrent queries in the system
B. The [myproject:mydataset.mytable] table has too many partitions
C. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
D. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

Correct Answer:A

Question 19

- (Exam Topic 6)
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of data. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

A. Create a new view over events using standard SQL
B. Create a new partitioned table using a standard SQL query
C. Create a new view over events_partitioned using standard SQL
D. Create a service account for the ODBC connection to use for authentication
E. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connectionand shared “events”

Correct Answer:AE

Question 20

- (Exam Topic 5)
Why do you need to split a machine learning dataset into training data and test data?

A. So you can try two different sets of features
B. To make sure your model is generalized for more than just the training data
C. To allow you to create unit tests in your code
D. So you can use one dataset for a wide model and one for a deep model

Correct Answer:B
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/