Free Google Professional-Machine-Learning-Engineer Exam Questions

Question 1

You work at a large organization that recently decided to move their ML and data workloads to Google Cloud. The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format. You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction. How should you configure the pipeline?

A : Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features, and store them in Vertex AI Feature Store for online prediction.

B : Ingest the Avro files into BigQuery to perform analytics. Use a Dataflow pipeline to create the features, and store them in Vertex AI Feature Store for online prediction.

C : Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features, and store them in BigQuery for online prediction.

D : Ingest the Avro files into BigQuery to perform analytics. Use BigQuery SQL to create features and store them in a separate BigQuery table for online prediction.

Answer: C

Question 2

You work on a team in a data center that is responsible for server maintenance. Your management team wants you to build a predictive maintenance solution that uses monitoring data to detect potential server failures. Incident data has not been labeled yet. What should you do first?

A : ain a time-series model to predict the machines’ performance values. Configure an alert if a machine’s actual performance values significantly differ from the predicted performance values.

B : velop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Use this heuristic to monitor server performance in real time.

C : velop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Train a model to predict anomalies based on this labeled dataset.

D : re a team of qualified analysts to review and label the machines’ historical performance data. Train a model based on this manually labeled dataset.

Answer: D

Question 3

You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an AI Platform notebook.

What should you do?

A : AI Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe.

B : port your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance.

C : wnload your table from BigQuery as a local CSV file, and upload it to your AI Platform notebook instance. Use pandas.read_csv to ingest he file as a pandas dataframe.

D : om a bash cell in your AI Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutil cp to copy the data into the notebook. Use pandas.read_csv to ingest the file as a pandas dataframe.

Answer: C

Question 4

You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations AI to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

A : the ג€Other Products You May Likeג€ recommendation type to increase the click-through rate.

B : the ג€Frequently Bought Togetherג€ recommendation type to increase the shopping cart size for each order.

C : port your user events and then your product catalog to make sure you have the highest quality event stream.

D : cause it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

Answer: B

Question 5

You trained a text classification model. You have the following SignatureDefs:

You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using: headers = {"content-type": "application/json"} json_response = requests.post('http: //localhost:8501/v1/models/text_model:predict', data=data, headers=headers)

What is the correct way to write the predict request?

A : ta = json.dumps({ג€signature_nameג€: ג€seving_defaultג€, ג€instancesג€ [['ab', 'bc', 'cd']]})

B : ta = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c', 'd', 'e', 'f']]})

C : ta = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c'], ['d', 'e', 'f']]})

D : ta = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b'], ['c', 'd'], ['e', 'f']]})

Answer: C