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Exam Details

The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.

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Google Professional Machine Learning Engineer Sample Questions (Q130-Q135):

NEW QUESTION # 130
You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model's predictions will be used to adapt each user's game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?

A. Deploy the model to Vertex AI Prediction. Make predictions using batch reading data from Cloud Bigtable, and push the data to Cloud SQL.
B. Embed the model in the streaming Dataflow pipeline. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
C. Embed the model in the mobile application. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
D. Import the model into BigQuery ML. Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL

Answer: D

NEW QUESTION # 131
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. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
C. 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.
D. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.

Answer: D

Explanation:
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)

NEW QUESTION # 132
You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?

A. Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.
B. Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.
C. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket
D. Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket

Answer: B

NEW QUESTION # 133
You work on a growing team of more than 50 data scientists who all use Al Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

A. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
B. Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.
C. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources
D. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.

Answer: A

NEW QUESTION # 134
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

A. Significantly increase the max_batch_size TensorFlow Serving parameter
B. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
C. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
D. Significantly increase the max_enqueued_batches TensorFlow Serving parameter

Answer: B

Explanation:
https://www.tensorflow.org/tfx/serving/performance

NEW QUESTION # 135
......

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