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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	24%	- Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL - Data ingestion and integration 1. Batch ingestion pipelines (BigQuery, Cloud Storage) 2. Streaming ingestion (Pub/Sub, Dataflow)
Topic 2: Operationalizing machine learning models	26%	- ML pipeline integration 1. Vertex AI pipeline deployment 2. Feature engineering and feature stores - Model deployment and monitoring 1. Model monitoring and drift detection 2. Online vs batch prediction
Topic 3: Designing data processing systems	22%	- Batch and streaming data processing design 1. Event-driven vs batch architectures 2. Latency, throughput, and consistency trade-offs - Data architecture and storage design 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse) 2. Designing scalable and cost-effective data models
Topic 4: Ensuring solution quality	28%	- Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads - Security and governance 1. IAM and access control in GCP 2. Data governance and compliance

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Google Certified Professional Data Engineer Exam Sample Questions (Q242-Q247):

NEW QUESTION # 242
Which of these are examples of a value in a sparse vector? (Select 2 answers.)

A. [0, 0, 0, 1, 0, 0, 1]
B. [0, 1]
C. [1, 0, 0, 0, 0, 0, 0]
D. [0, 5, 0, 0, 0, 0]

Answer: B,C

Explanation:
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single
1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s.
Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations

NEW QUESTION # 243
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

A. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
B. Re-write the application to load accumulated data every 2 minutes.
C. Convert the streaming insert code to batch load for individual messages.
D. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

Answer: D

Explanation:
Explanation
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue.
So We need to wait till it's written tio storage

NEW QUESTION # 244
Which software libraries are supported by Cloud Machine Learning Engine?

A. Theano and Torch
B. Theano and TensorFlow
C. TensorFlow
D. TensorFlow and Torch

Answer: C

Explanation:
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
Reference:
https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does

NEW QUESTION # 245
You are preparing data that your machine learning team will use to train a model using BigQueryML. They want to predict the price per square foot of real estate. The training data has a column for the price and a column for the number of square feet. Another feature column called
'feature1' contains null values due to missing data. You want to replace the nulls with zeros to keep more data points. Which query should you use?

Answer: B

NEW QUESTION # 246
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Userstable consisting of a FirstNamefield and a LastNamefield. 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 FullNamefield consisting of the value of the FirstNamefield concatenated with a space, followed by the value of the LastNamefield for each employee. How can you make that data available while minimizing cost?

A. Add a new column called FullNameto the Users table. Run an UPDATEstatement that updates the FullNamecolumn for each user with the concatenation of the FirstNameand LastNamevalues.
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, LastNameand FullName. Run a BigQuery load job to load the new CSV file into BigQuery.
C. Create a Google Cloud Dataflow job that queries BigQuery for the entire Userstable, concatenates the FirstNamevalue and LastNamevalue for each user, and loads the proper values for FirstName, LastName, and FullNameinto a new table in BigQuery.
D. Create a view in BigQuery that concatenates the FirstNameand LastNamefield values to produce the FullName.

Answer: C

Explanation:
Explanation/Reference:

NEW QUESTION # 247
......

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