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Google Certified Professional Data Engineer Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
Your startup has a web application that currently serves customers out of a single region in Asi a. You are targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

A. Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oud Spanner instances after securing funding.
B. Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highly available configuration after securing funding.
C. Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia after securing funding.
D. Use a Cloud SQL for PostgreSQL highly available instance first, and Bigtable with US. Europe, and Asiareplication alter securing funding

Answer: A

Explanation:
https://cloud.google.com/spanner/docs/instance-configurations#tradeoffs_regional_versus_multi-region_configurations

NEW QUESTION # 57
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A. Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.
B. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
C. Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
D. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

Answer: D

NEW QUESTION # 58
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions.
You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?

A. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
B. Create a Cloud Dataflow pipeline using BigQueryIOto read predictions for all users from the query. Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.
C. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
D. Create a Cloud Dataflow pipeline using BigQueryIOto read results from the query. Grant the Dataflow Worker role to the application service account.

Answer: B

NEW QUESTION # 59
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.)

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

Answer: A,C,E

Explanation:
Explanation/Reference:

NEW QUESTION # 60
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Use K-means Clustering to detect faces in the pixels.
B. Use feature engineering to add features for eyes, noses, and mouths to the input data.
C. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
D. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

Answer: D

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning.
So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION # 61
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