Professional Data Engineer Professional Data Engineer

Use original educational practice content, randomized simulator sessions, score history and explanation review for Professional Data Engineer.

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Les questions d’entraînement sont fournies en anglais afin de rester proches de la langue couramment utilisée dans les certifications IT.

QZ9 est une plateforme de formation indépendante. Toutes les questions d’entraînement sont originales et créées uniquement à des fins éducatives. Nous ne fournissons pas de contenu d’examen réel ni de contenu non autorisé. Microsoft, AWS, Cisco, CompTIA, PMI et les autres marques appartiennent à leurs propriétaires respectifs.

FournisseurGoogle

CodeProfessional Data Engineer

DurationDurée variable

Questions173 refs

Informations examen

Une préparation basée sur les objectifs, sans contenu non autorisé

Certification metadata is preserved from the legacy QZ9 catalog. Questions remain English-only.

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Simulateur d’examen

Les questions d’entraînement sont fournies en anglais afin de rester proches de la langue couramment utilisée dans les certifications IT.

Question 1

Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Question 2

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

Question 3

You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

Question 4

You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

Question 5

An external customer provides you with a daily practice test of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?