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비스를 제공해드려 아무런 걱정없이 Professional-Machine-Learning-Engineer시험에 도전하도록 힘이 되어드립니다. PassTIP덤프를 사용하여 시험에서 통과하신 분이 전해주신 희소식이 PassTIP 덤프품질을 증명해드립니다.

Google Professional-Machine-Learning-Engineer 시험요강:

주제	소개
주제 1	Model performance against baselines, simpler models, and across the time dimension Defining outcome of model predictions
주제 2	Organization and tracking experiments and pipeline runs Hooking models into existing CI CD deployment system
주제 3	Automation of data preparation and model training deployment Determination of when a model is deemed unsuccessful
주제 4	Design architecture that complies with regulatory and security concerns Define business success criteria
주제 5	Batching and streaming data pipelines at scale Managing incorrect results Identifying nonML solutions
주제 6	Choose appropriate Google Cloud hardware components Privacy implications of data usage Identifying potential regulatory issues
주제 7	Training a model as a job in different environments Constructing and testing of parameterized pipeline definition in SDK
주제 8	Performance and business quality of ML model predictions Establishing continuous evaluation metrics

최신 Google Cloud Certified Professional-Machine-Learning-Engineer 무료샘플문제 (Q119-Q124):

질문 # 119
Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

A. 1 = Pub/Sub, 2 = Datastore
B. 1 = Cloud Function, 2 = Cloud SQL
C. 1 = Dataflow, 2 = BigQuery
D. 1 = Dataflow, 2 = Cloud SQL

정답：A

질문 # 120
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

A. Execute a query in BigQuery to retrieve all the existing table names in your project using the
B. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
C. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
D. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.

정답：B

설명：
INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
Explanation:
A should be the way to go for large datasets --This is also good but it is legacy way of checking:- NFORMATION_SCHEMA contains these views for table metadata: TABLES and TABLE_OPTIONS for metadata about tables. COLUMNS and COLUMN_FIELD_PATHS for metadata about columns and fields. PARTITIONS for metadata about table partitions (Preview)

질문 # 121
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

A. Cloud Composer, BigQuery ML , and Al Platform Prediction
B. Kubeflow Pipelines and App Engine
C. Kubeflow Pipelines and Al Platform Prediction
D. Cloud Composer, Al Platform Training with custom containers , and App Engine

정답：C

질문 # 122
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

A. Binomial distribution
B. Poisson distribution
C. Uniform distribution
D. Normal distribution

정답：A

질문 # 123
You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an "Out of Memory" error. What should you do?

A. Use base64 to encode your data before using it for prediction.
B. Use batch prediction mode instead of online mode.
C. Send the request again with a smaller batch of instances.
D. Apply for a quota increase for the number of prediction requests.

정답：A

질문 # 124
......

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