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Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) Sample Questions:

1. データ エンジニアは、きめ細かな権限を持つジョブをデプロイするために Databricks アセット バンドルを構成しています。
要件は次のとおりです。
- データ エンジニア グループにジョブへの CAN_MANAGE アクセスを許可します。
- 監査人グループがジョブを表示できるが、変更/実行できないことを確認します。
- 他のユーザー/グループに意図しない権限を付与しないようにしてください。
データ エンジニアは、要件を満たしながらジョブをどのように展開する必要がありますか?

A) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
permissions:
- group_name: auditors
level: CAN_VIEW
B) permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
C) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
- group_name: admin-team
level: IS_OWNER
D) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW

2. データエンジニアは、顧客がレンタル自転車を利用中にどこに持ち込んでいるかを把握するためのデータ取り込みパイプラインを構築しています。エンジニアは、時間の経過とともに、自転車のセンサーから送信されるデータに緯度や経度などの重要な詳細情報が含まれていないことに気づきました。下流のアナリストは、クリーンなレコードと隔離されたレコードの両方を個別に処理する必要があります。
データ エンジニアはすでに次のコードを持っています。
インポートDLT
pyspark.sql.functionsからexprをインポートする
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?

A) @dlt.view
@dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)") def trips_data_quarantine():
return spark.readStream.table("ride_and_go.telemetry.trips")
B) @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")
C) @dlt.table(partition_cols=["is_quarantined", ])
@dlt.expect_all(rules)
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.withColumn("is_quarantined", expr(quarantine_rules))
)
D) @dlt.table(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)

3. あるデータエンジニアは、Lakeflowの宣言型パイプラインの期待値機能を使用して、入力センサーデータのデータ品質を追跡しています。センサーは定期的に範囲外の不良データを送信し、現在、それらの行に警告フラグを付けて、正常なデータと共にSilverテーブルに書き込んでいます。そこで、新たな要件が課されました。不良行は別の隔離テーブルに隔離し、Silverテーブルには含めないようにする必要があるのです。
これはシルバーテーブルの既存のコードです:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?

A) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading < 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
B) @dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
C) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
D) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

4. データガバナンスチームは、個人識別情報(PII)を含む「ユーザー」テーブルの社会保障番号列に適切なマスキングを施すことを必須としました。これは、HRAdminGroup以外のユーザーからは、マスキングされた社会保障番号が***-**-として表示されることを意味します。
****。
チームはマスキング機能を作成しました。

この目標を達成するために、データ ガバナンス チームは次に何をする必要がありますか?

A) CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask;
B) CREATE TABLE users
(name STRING, ssn STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask;
C) CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask);
D) CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup');

5. Predictive Optimization は、Unity Catalog Managed テーブルに対してデフォルトで有効になっている自動化された Databricks サービスです。Delta テーブルを継続的に最適化することで、最適なパフォーマンスとコストを確保し、維持管理を支援します。Predictive Optimization は、Delta テーブルを維持するためにどの 2 つの操作を実行しますか？(2 つ選択してください。)

A) 最適化
B) 分析
C) バケット化
D) パーティション
E) コンパクト

Solutions: